diff --git a/9G-Train/apps/cpm9g/config/11b/config.json b/9G-Train/apps/cpm9g/config/11b/config.json
deleted file mode 100644
index 82d1933..0000000
--- a/9G-Train/apps/cpm9g/config/11b/config.json
+++ /dev/null
@@ -1,16 +0,0 @@
-{
- "vocab_size": 119696,
- "dropout_p": 0.0,
- "eps": 1e-05,
- "half": true,
- "use_flash_attn": true,
- "flash_attn_mask_shape": "2d",
- "dim_model": 4096,
- "dim_ff": 12288,
- "dim_head": 128,
- "num_heads": 32,
- "num_kv_heads": 32,
- "num_layers": 48,
- "activate_fn": "silu",
- "scale": false
-}
diff --git a/9G-Train/apps/cpm9g/config/7b/config.json b/9G-Train/apps/cpm9g/config/7b/config.json
deleted file mode 100644
index bfeb6c1..0000000
--- a/9G-Train/apps/cpm9g/config/7b/config.json
+++ /dev/null
@@ -1,14 +0,0 @@
-{
- "vocab_size": 119696,
- "dropout_p": 0.0,
- "eps": 1e-05,
- "half": true,
- "dim_model": 4096,
- "dim_ff": 11008,
- "dim_head": 128,
- "num_heads": 32,
- "num_kv_heads": 32,
- "num_layers": 32,
- "activate_fn": "silu",
- "scale": false
-}
diff --git a/9G-Train/apps/cpm9g/config/7b/vocab.txt b/9G-Train/apps/cpm9g/config/7b/vocab.txt
deleted file mode 100644
index 24fc772..0000000
--- a/9G-Train/apps/cpm9g/config/7b/vocab.txt
+++ /dev/null
@@ -1,119696 +0,0 @@
-""
-""
-""
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-" Angry"
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-" April"
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-" Arnold"
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-" Astron"
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-" Astros"
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-" Asus"
-" Asylum"
-" Async"
-" AsyncIClusCfg"
-" At"
-" Atari"
-" Ath"
-" Athe"
-" Athen"
-" Athena"
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-" Atlantis"
-" Atlas"
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-" Atom"
-" Atomic"
-" AtomicInteger"
-" Att"
-" Attach"
-" Attachment"
-" Attack"
-" Attacks"
-" Attempt"
-" Attend"
-" Attention"
-" Attorney"
-" Attr"
-" Attribute"
-" AttributeError"
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-" Attributes"
-" Attribution"
-" Au"
-" Aub"
-" Auburn"
-" Auch"
-" Auckland"
-" Auction"
-" Aud"
-" Audi"
-" Audio"
-" Audiod"
-" Audiodateien"
-" Audit"
-" Auditor"
-" Audrey"
-" Auf"
-" Auflage"
-" Aug"
-" Augen"
-" August"
-" Augusta"
-" Augustine"
-" Augustus"
-" Aunt"
-" Aur"
-" Aura"
-" Aure"
-" Aurora"
-" Aus"
-" Auschwitz"
-" Aust"
-" Austin"
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-" Availability"
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-" Avenger"
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-" Awakens"
-" Award"
-" Awards"
-" Aware"
-" Awareness"
-" Away"
-" Awesome"
-" Awoken"
-" Aws"
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-" Mao"
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-" Maryland"
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-" Mastery"
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-" Match"
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-" Mathemat"
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-" Mathematics"
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-" Maurice"
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-" Maxim"
-" Maximum"
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-" Maya"
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-" Mayor"
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-" McA"
-" McAuliffe"
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-" McCain"
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-" McCarthy"
-" McCartney"
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-" McConnell"
-" McCorm"
-" McCoy"
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-" McDonald"
-" McDonnell"
-" McF"
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-" McGregor"
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-" McH"
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-" McLaren"
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-" McMahon"
-" McMaster"
-" McN"
-" Md"
-" Me"
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-" Meaning"
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-" Meanwhile"
-" Measure"
-" Measurement"
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-" Meat"
-" Mecca"
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-" Mechan"
-" Mechanical"
-" Mechanics"
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-" Medal"
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-" Medicaid"
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-" Meditation"
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-" Mediterranean"
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-" Medline"
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-" Meeting"
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-" Mehran"
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-" Mellon"
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-" Mental"
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-" Mesa"
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-" Messiah"
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-" Method"
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-" Meyer"
-" Mi"
-" Mia"
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-" Micha"
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-" Microsoft"
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-" Mig"
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-" Mighty"
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-" Migration"
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-" Mik"
-" Mike"
-" Mikhail"
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-" Mild"
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-" Military"
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-" Milk"
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-" Millenn"
-" Millennials"
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-" Millionen"
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-" Milo"
-" Milton"
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-" Mime"
-" Min"
-" Mind"
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-" Minimum"
-" Mining"
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-" Minneapolis"
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-" Mint"
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-" Mir"
-" Mira"
-" Miracle"
-" Mirage"
-" Miranda"
-" Mirror"
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-" Miscellaneous"
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-" Missile"
-" Missing"
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-" Mississippi"
-" Missouri"
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-" Mister"
-" Mistress"
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-" Mit"
-" Mitch"
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-" Mitglied"
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-" Miz"
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-" Mn"
-" Mo"
-" Mob"
-" Mobil"
-" Mobile"
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-" Modal"
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-" ModelAndView"
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-" Modifier"
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-" Mohamed"
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-" Moj"
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-" Moment"
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-" Monaco"
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-" Money"
-" Mong"
-" Mongo"
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-" Monsanto"
-" Monsieur"
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-" Moral"
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-" Moreno"
-" Moreover"
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-" Moroccan"
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-" Mort"
-" Mortal"
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-" Morty"
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-" Moscow"
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-" Most"
-" Mostly"
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-" Mot"
-" Mother"
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-" Motor"
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-" Mount"
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-" Mov"
-" Move"
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-" Movie"
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-" My"
-" MyBatis"
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-" ND"
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-" NEED"
-" NEGLIGENCE"
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-" NETWORK"
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-" NEW"
-" NEWS"
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-" NFC"
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-" NGC"
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-" NHL"
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-" NI"
-" NIC"
-" NIGHT"
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-" NL"
-" NM"
-" NN"
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-" NOAA"
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-" NOI"
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-" NONE"
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-" NOTE"
-" NOTICE"
-" NOW"
-" NOWRAP"
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-" NSArray"
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-" NULL"
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-" NYT"
-" NYU"
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-" Na"
-" NaN"
-" Nab"
-" Nach"
-" Nachdem"
-" Nacional"
-" Nad"
-" Nadu"
-" Nag"
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-" Nam"
-" Name"
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-" Naples"
-" Napole"
-" Napoleon"
-" Napoli"
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-" Narc"
-" Nare"
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-" Nashville"
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-" Nat"
-" Natal"
-" Natalie"
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-" Nathan"
-" Nathaniel"
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-" Natural"
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-" Nature"
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-" Nav"
-" Navajo"
-" Naval"
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-" Naz"
-" Nazi"
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-" Nbt"
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-" Neal"
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-" Near"
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-" Neigh"
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-" Neither"
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-" Nell"
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-" Nemesis"
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-" Nepal"
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-" Neptune"
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-" Net"
-" Netanyahu"
-" Netflix"
-" Nether"
-" Netherlands"
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-" Network"
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-" Neue"
-" Neural"
-" Neuro"
-" Neurolog"
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-" Nevada"
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-" Nevertheless"
-" Neville"
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-" Newspaper"
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-" Next"
-" Nexus"
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-" Ng"
-" Nginx"
-" Nguyen"
-" Ni"
-" Niagara"
-" Nib"
-" Nic"
-" Nicarag"
-" Nicaragua"
-" Nice"
-" Nich"
-" Nicholas"
-" Nichols"
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-" Nickel"
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-" Nicola"
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-" Nicole"
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-" Nid"
-" Nie"
-" Nied"
-" Nieder"
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-" Nieto"
-" Nietzsche"
-" Nig"
-" Nigel"
-" Niger"
-" Nigeria"
-" Nigerian"
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-" Nightmare"
-" Nightmares"
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-" Nik"
-" Nike"
-" Nikki"
-" Nikol"
-" Nikola"
-" Nikon"
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-" Nile"
-" Nim"
-" Nin"
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-" Ning"
-" Ninja"
-" Nintendo"
-" Ninth"
-" Nir"
-" Nirvana"
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-" Nissan"
-" Nit"
-" NixOS"
-" Nixon"
-" No"
-" NoSuch"
-" Noah"
-" Nob"
-" Nobel"
-" Noble"
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-" Noct"
-" Node"
-" NodeJS"
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-" Noel"
-" Noir"
-" Noise"
-" Nokia"
-" Nolan"
-" Nom"
-" Non"
-" None"
-" Nonetheless"
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-" Nope"
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-" Nora"
-" Nord"
-" Norden"
-" Nordic"
-" Norfolk"
-" Norm"
-" Normal"
-" Normalize"
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-" Norman"
-" Normandy"
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-" Normdaten"
-" Norn"
-" Norris"
-" Norse"
-" Nort"
-" Norte"
-" North"
-" Northeast"
-" Northern"
-" Northwest"
-" Northwestern"
-" Norton"
-" Norway"
-" Norweg"
-" Norwegian"
-" Norwich"
-" Nos"
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-" Nost"
-" Not"
-" NotFound"
-" NotImplemented"
-" NotImplementedError"
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-" Notably"
-" Note"
-" Notebook"
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-" Nothing"
-" Notice"
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-" Notre"
-" Notting"
-" Nottingham"
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-" Now"
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-" Nt"
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-" NullPointerException"
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-" Obama"
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-" Observable"
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-" Optical"
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-" PARTICULAR"
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-" PATH"
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-" PCI"
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-" Rep"
-" Repair"
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-" Repeat"
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-" Replace"
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-" Retail"
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-" Rev"
-" Reve"
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-" Revelation"
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-" Revenue"
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-" Reverse"
-" Review"
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-" Revised"
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-" Reward"
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-" RewriteRule"
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-" Reynolds"
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-" Richtung"
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-" Ride"
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-" Ronnie"
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-" Running"
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-" SUMMARY"
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-" Sauce"
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-" Sav"
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-" Scythe"
-" Se"
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-" Search"
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-" Securities"
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-" Sed"
-" See"
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-" Sek"
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-" Semantic"
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-" Senate"
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-" Sequ"
-" Sequelize"
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-" Seraph"
-" Serbia"
-" Serbian"
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-" Serge"
-" Sergeant"
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-" Sergey"
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-" Serial"
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-" Serv"
-" Serve"
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-" SetFontSize"
-" SetTextColor"
-" SetValue"
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-" Setter"
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-" Seventh"
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-" Several"
-" Severity"
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-" Sew"
-" Sex"
-" Sexual"
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-" Seym"
-" Seymour"
-" Sgt"
-" Sh"
-" Sha"
-" Shack"
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-" Shakespe"
-" Shakespeare"
-" Shal"
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-" Sham"
-" Shaman"
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-" Shan"
-" Shanahan"
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-" Shang"
-" Shanghai"
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-" Shannon"
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-" Shap"
-" Shape"
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-" Shapiro"
-" Shar"
-" Shard"
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-" Share"
-" SharePoint"
-" Shared"
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-" Sharing"
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-" Sharon"
-" Sharp"
-" Shattered"
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-" She"
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-" Shelby"
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-" Shi"
-" Shia"
-" Shib"
-" Shield"
-" Shields"
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-" Shin"
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-" Ship"
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-" Shir"
-" Shirley"
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-" Shooting"
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-" Shopping"
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-" Shortly"
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-" Shotgun"
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-" Showtime"
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-" Shutdown"
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-" Si"
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-" Siber"
-" Siberia"
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-" Sidebar"
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-" Sie"
-" Sied"
-" Sieg"
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-" Siehe"
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-" Sierra"
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-" Sight"
-" Sigma"
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-" Sikh"
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-" SimFlow"
-" Similar"
-" Similarly"
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-" Simple"
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-" Software"
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-" Somali"
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-" Somerset"
-" Something"
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-" Somewhere"
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-" Sorry"
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-" Spanish"
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-" Speak"
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-" Spin"
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-" Spiral"
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-" Spirits"
-" Spiritual"
-" Spit"
-" Spl"
-" Splash"
-" Split"
-" Spo"
-" Spock"
-" Spoiler"
-" Spokane"
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-" Н"
-" На"
-" Насе"
-" Население"
-" Населення"
-" Насеље"
-" Нау"
-" Националь"
-" Не"
-" Ни"
-" Никола"
-" Николай"
-" Но"
-" Нов"
-" Ново"
-" Нор"
-" Нью"
-" Ні"
-" О"
-" Об"
-" Од"
-" Одна"
-" Однако"
-" Он"
-" Ор"
-" Ос"
-" Основ"
-" От"
-" Оте"
-" Отече"
-" Официаль"
-" П"
-" Па"
-" Пав"
-" Пар"
-" Пари"
-" Пе"
-" Пер"
-" Пере"
-" Пет"
-" Пи"
-" По"
-" Под"
-" Поль"
-" Попис"
-" Пор"
-" Посилання"
-" После"
-" Пра"
-" Пре"
-" Пред"
-" През"
-" Према"
-" При"
-" Про"
-" Пу"
-" Пів"
-" Під"
-" Після"
-" Р"
-" Ра"
-" Раз"
-" Рас"
-" Распо"
-" Расподела"
-" Ре"
-" Резу"
-" Резултати"
-" Рес"
-" Республи"
-" Республики"
-" Ри"
-" Ро"
-" Роб"
-" Роди"
-" Роз"
-" Рос"
-" Росси"
-" России"
-" Российской"
-" Ру"
-" Рус"
-" С"
-" СП"
-" СССР"
-" США"
-" Са"
-" Савез"
-" Савезне"
-" Сан"
-" Санкт"
-" Свя"
-" Се"
-" Север"
-" Сер"
-" Серг"
-" Сере"
-" Си"
-" См"
-" Со"
-" Сов"
-" Сове"
-" Совет"
-" Список"
-" Спо"
-" Спољашње"
-" Ср"
-" Сред"
-" Ссылки"
-" Ст"
-" Ста"
-" Станов"
-" Становништво"
-" Сте"
-" Су"
-" Т"
-" Та"
-" Так"
-" Также"
-" Те"
-" Тер"
-" Ти"
-" То"
-" Тре"
-" Три"
-" Ту"
-" Тур"
-" У"
-" Уи"
-" Укра"
-" Украи"
-" Украины"
-" Украї"
-" України"
-" Ф"
-" Фа"
-" Фе"
-" Федера"
-" Федерации"
-" Фи"
-" Фо"
-" Фран"
-" Х"
-" Ха"
-" Хар"
-" Хо"
-" Хро"
-" Хронологи"
-" Хронологија"
-" Ху"
-" Ц"
-" Це"
-" Цент"
-" Ч"
-" Ча"
-" Че"
-" Чем"
-" Чер"
-" Чи"
-" Ш"
-" Ша"
-" Ши"
-" Щ"
-" Э"
-" Это"
-" Ю"
-" Я"
-" а"
-" або"
-" ав"
-" авгу"
-" августа"
-" авиа"
-" авто"
-" автомо"
-" автор"
-" ад"
-" административ"
-" ак"
-" акаде"
-" акт"
-" актив"
-" ал"
-" але"
-" аль"
-" альбо"
-" американ"
-" ан"
-" ана"
-" анг"
-" англий"
-" анти"
-" апре"
-" апреля"
-" ар"
-" армии"
-" арти"
-" архи"
-" ат"
-" б"
-" ба"
-" бан"
-" бе"
-" без"
-" бере"
-" березня"
-" би"
-" био"
-" благо"
-" бли"
-" бо"
-" более"
-" боль"
-" бор"
-" бра"
-" бри"
-" брига"
-" британ"
-" бро"
-" броја"
-" бу"
-" був"
-" буде"
-" буду"
-" була"
-" були"
-" було"
-" бы"
-" был"
-" была"
-" были"
-" было"
-" быть"
-" бі"
-" біль"
-" в"
-" ва"
-" важ"
-" ве"
-" везе"
-" века"
-" вели"
-" вер"
-" вере"
-" вересня"
-" верну"
-" вз"
-" взя"
-" ви"
-" вид"
-" виде"
-" вико"
-" викори"
-" використову"
-" висини"
-" висо"
-" вклю"
-" включа"
-" вла"
-" власти"
-" вме"
-" вместе"
-" вне"
-" внут"
-" во"
-" водо"
-" воен"
-" воз"
-" возмож"
-" возра"
-" вой"
-" войны"
-" воло"
-" вопро"
-" вос"
-" восто"
-" впер"
-" вра"
-" вре"
-" време"
-" времени"
-" время"
-" вс"
-" все"
-" всего"
-" всех"
-" встре"
-" всі"
-" вто"
-" второй"
-" ву"
-" въ"
-" вы"
-" выпол"
-" выпу"
-" высо"
-" выступа"
-" выше"
-" ві"
-" від"
-" відбу"
-" відо"
-" вій"
-" війни"
-" війсь"
-" він"
-" віці"
-" г"
-" га"
-" гг"
-" где"
-" ге"
-" гене"
-" генерал"
-" гер"
-" ги"
-" гла"
-" глав"
-" го"
-" гово"
-" год"
-" года"
-" годах"
-" годи"
-" година"
-" године"
-" годов"
-" году"
-" годы"
-" голо"
-" голов"
-" гор"
-" горо"
-" город"
-" города"
-" городе"
-" государ"
-" гра"
-" град"
-" граф"
-" гре"
-" грома"
-" гру"
-" грудня"
-" груп"
-" группы"
-" гу"
-" губер"
-" д"
-" да"
-" дан"
-" данным"
-" два"
-" две"
-" дви"
-" дво"
-" дву"
-" двух"
-" де"
-" дей"
-" действи"
-" дека"
-" декабря"
-" дела"
-" день"
-" депута"
-" дере"
-" дерев"
-" деревня"
-" держа"
-" держав"
-" деся"
-" дет"
-" дея"
-" деятель"
-" ди"
-" диа"
-" дивизи"
-" дире"
-" для"
-" до"
-" дов"
-" доку"
-" дол"
-" долж"
-" дома"
-" допо"
-" доро"
-" дости"
-" др"
-" дра"
-" древ"
-" држа"
-" државе"
-" држави"
-" дру"
-" други"
-" другие"
-" других"
-" друго"
-" ду"
-" ді"
-" дія"
-" е"
-" его"
-" еди"
-" един"
-" ему"
-" ен"
-" есть"
-" ещё"
-" её"
-" ж"
-" же"
-" желез"
-" жен"
-" жена"
-" женщи"
-" жи"
-" живе"
-" живело"
-" живо"
-" жизни"
-" жов"
-" жовт"
-" жовтня"
-" журна"
-" жі"
-" з"
-" за"
-" зав"
-" завер"
-" заво"
-" зада"
-" зай"
-" зали"
-" заме"
-" зан"
-" занима"
-" заня"
-" запа"
-" запад"
-" заслу"
-" затем"
-" защи"
-" збір"
-" зв"
-" зву"
-" зда"
-" зе"
-" зем"
-" змі"
-" зна"
-" значи"
-" зо"
-" золо"
-" зу"
-" зі"
-" и"
-" иг"
-" игра"
-" иде"
-" из"
-" извест"
-" изда"
-" изу"
-" или"
-" им"
-" има"
-" име"
-" имеет"
-" имен"
-" имени"
-" импе"
-" ин"
-" институ"
-" инте"
-" интер"
-" информа"
-" ис"
-" искус"
-" испо"
-" испол"
-" исполь"
-" использова"
-" использу"
-" исследова"
-" исто"
-" истори"
-" истории"
-" их"
-" ию"
-" июля"
-" июня"
-" й"
-" його"
-" к"
-" ка"
-" как"
-" кам"
-" кан"
-" као"
-" капи"
-" кар"
-" карь"
-" ката"
-" кате"
-" като"
-" кафе"
-" каче"
-" качестве"
-" квіт"
-" квітня"
-" ки"
-" кино"
-" кла"
-" кли"
-" клу"
-" клуб"
-" км"
-" кни"
-" кня"
-" ко"
-" когда"
-" коли"
-" колле"
-" коло"
-" ком"
-" коман"
-" команди"
-" команду"
-" коми"
-" комму"
-" комп"
-" компа"
-" компози"
-" кон"
-" контра"
-" контро"
-" конце"
-" копи"
-" копия"
-" кор"
-" кораб"
-" коро"
-" корпу"
-" кото"
-" которая"
-" которого"
-" которой"
-" которы"
-" которые"
-" который"
-" которых"
-" која"
-" које"
-" који"
-" кра"
-" край"
-" краї"
-" кре"
-" кри"
-" круп"
-" ку"
-" куль"
-" кур"
-" къ"
-" кі"
-" кіль"
-" ла"
-" ле"
-" лет"
-" ли"
-" липня"
-" листо"
-" листопада"
-" литера"
-" ло"
-" лу"
-" луч"
-" лю"
-" людей"
-" люди"
-" людя"
-" людях"
-" лютого"
-" лі"
-" м"
-" ма"
-" май"
-" мало"
-" мар"
-" марта"
-" мат"
-" мате"
-" матери"
-" мая"
-" має"
-" ме"
-" меда"
-" меди"
-" между"
-" международ"
-" мен"
-" мене"
-" мест"
-" места"
-" место"
-" меся"
-" мет"
-" мето"
-" ми"
-" мини"
-" мира"
-" мм"
-" мно"
-" многи"
-" много"
-" мо"
-" могу"
-" мож"
-" може"
-" может"
-" можно"
-" моло"
-" мона"
-" мор"
-" моря"
-" му"
-" муж"
-" музе"
-" музи"
-" музы"
-" муниципа"
-" муніципалі"
-" мы"
-" мя"
-" мі"
-" між"
-" міс"
-" міста"
-" місце"
-" н"
-" на"
-" нав"
-" награ"
-" над"
-" надмор"
-" надморској"
-" назва"
-" название"
-" назна"
-" называ"
-" наи"
-" най"
-" налази"
-" нап"
-" написа"
-" направ"
-" народ"
-" насе"
-" населения"
-" населення"
-" насеља"
-" насеље"
-" насељу"
-" настоя"
-" нау"
-" наук"
-" находи"
-" находится"
-" националь"
-" національ"
-" нача"
-" начале"
-" началь"
-" нај"
-" не"
-" него"
-" неза"
-" ней"
-" неко"
-" некоторы"
-" немец"
-" необ"
-" неско"
-" несколько"
-" ни"
-" них"
-" но"
-" нов"
-" ново"
-" нор"
-" ноября"
-" ну"
-" ні"
-" о"
-" об"
-" оби"
-" обла"
-" области"
-" область"
-" області"
-" обо"
-" оборо"
-" обра"
-" образ"
-" образова"
-" обращения"
-" обу"
-" обще"
-" общи"
-" объ"
-" объек"
-" обы"
-" од"
-" один"
-" одна"
-" однако"
-" одним"
-" одно"
-" одного"
-" одной"
-" ок"
-" около"
-" окон"
-" оконча"
-" окру"
-" округа"
-" округу"
-" окт"
-" октября"
-" он"
-" она"
-" они"
-" оп"
-" опера"
-" опреде"
-" општи"
-" општини"
-" ор"
-" орган"
-" органи"
-" организа"
-" органі"
-" ори"
-" ору"
-" ос"
-" основ"
-" основа"
-" осо"
-" особи"
-" ост"
-" оста"
-" остан"
-" остров"
-" осу"
-" осіб"
-" от"
-" отде"
-" откры"
-" отли"
-" отме"
-" отно"
-" относи"
-" отри"
-" отрима"
-" официаль"
-" още"
-" п"
-" па"
-" памя"
-" пар"
-" пара"
-" пе"
-" пер"
-" первы"
-" первый"
-" пере"
-" перед"
-" переда"
-" пери"
-" перио"
-" период"
-" персона"
-" пес"
-" пи"
-" писа"
-" пись"
-" пла"
-" пло"
-" площа"
-" по"
-" побе"
-" пов"
-" пове"
-" повер"
-" пові"
-" под"
-" пода"
-" подацима"
-" поддер"
-" подо"
-" позво"
-" пози"
-" пока"
-" показа"
-" пол"
-" поле"
-" поли"
-" политиче"
-" поло"
-" полови"
-" полу"
-" получи"
-" получил"
-" поль"
-" полі"
-" поме"
-" помо"
-" помощ"
-" пописа"
-" попу"
-" пор"
-" пора"
-" пос"
-" посе"
-" после"
-" послед"
-" постро"
-" поч"
-" поча"
-" поэ"
-" поя"
-" пр"
-" пра"
-" прав"
-" права"
-" прави"
-" право"
-" працю"
-" пре"
-" пред"
-" представ"
-" представи"
-" през"
-" према"
-" при"
-" приз"
-" призна"
-" приня"
-" прис"
-" про"
-" пробле"
-" пров"
-" прове"
-" прово"
-" проводи"
-" програ"
-" програм"
-" прод"
-" прода"
-" продол"
-" проду"
-" проек"
-" произ"
-" производ"
-" проис"
-" проте"
-" проти"
-" против"
-" проф"
-" профес"
-" проце"
-" пря"
-" пси"
-" пу"
-" публи"
-" пун"
-" пункт"
-" пя"
-" пі"
-" пів"
-" півден"
-" півні"
-" під"
-" після"
-" р"
-" ра"
-" рабо"
-" работа"
-" работал"
-" работы"
-" ради"
-" раз"
-" разви"
-" разли"
-" разрабо"
-" рай"
-" райо"
-" район"
-" района"
-" районе"
-" району"
-" ран"
-" рас"
-" распо"
-" располо"
-" расположен"
-" ре"
-" реа"
-" револю"
-" реги"
-" регі"
-" ред"
-" реда"
-" режи"
-" резу"
-" результа"
-" результате"
-" реки"
-" ри"
-" ро"
-" робо"
-" род"
-" роди"
-" роз"
-" розта"
-" розташ"
-" роках"
-" року"
-" років"
-" роль"
-" роман"
-" рос"
-" россий"
-" році"
-" ру"
-" руково"
-" рус"
-" ры"
-" ря"
-" рі"
-" рів"
-" різ"
-" рік"
-" с"
-" са"
-" савез"
-" савезној"
-" сай"
-" сайт"
-" сайте"
-" сайті"
-" сам"
-" само"
-" самы"
-" сбор"
-" све"
-" свет"
-" сви"
-" сво"
-" свобо"
-" своего"
-" своей"
-" свои"
-" своих"
-" свой"
-" свою"
-" свої"
-" свя"
-" связи"
-" сві"
-" світ"
-" се"
-" себе"
-" себя"
-" север"
-" северо"
-" сез"
-" сезо"
-" секре"
-" села"
-" село"
-" сель"
-" сельсов"
-" сем"
-" семей"
-" сент"
-" сентября"
-" сер"
-" серед"
-" сери"
-" серпня"
-" си"
-" сим"
-" син"
-" систе"
-" систем"
-" система"
-" скла"
-" складу"
-" складі"
-" ско"
-" сла"
-" сле"
-" след"
-" следу"
-" сло"
-" слова"
-" слу"
-" служ"
-" случа"
-" см"
-" смер"
-" со"
-" соб"
-" сов"
-" совер"
-" совет"
-" советский"
-" современ"
-" созда"
-" сооб"
-" соответ"
-" соста"
-" состав"
-" составе"
-" составля"
-" составляет"
-" состоя"
-" сот"
-" сотруд"
-" сохра"
-" сп"
-" спе"
-" специаль"
-" спи"
-" спо"
-" спор"
-" способ"
-" сра"
-" сре"
-" сред"
-" среди"
-" ст"
-" ста"
-" став"
-" стал"
-" стала"
-" стали"
-" стан"
-" станов"
-" станови"
-" становника"
-" стар"
-" старо"
-" стату"
-" стать"
-" статьи"
-" ство"
-" сте"
-" степени"
-" сти"
-" сто"
-" столі"
-" сторо"
-" стра"
-" страны"
-" стре"
-" строи"
-" струк"
-" сту"
-" су"
-" суще"
-" счита"
-" съ"
-" сы"
-" сі"
-" сіль"
-" січня"
-" т"
-" та"
-" так"
-" также"
-" тако"
-" також"
-" такой"
-" там"
-" тан"
-" твор"
-" те"
-" теа"
-" театра"
-" тек"
-" теле"
-" тем"
-" темпера"
-" тео"
-" тер"
-" територ"
-" террито"
-" территории"
-" тех"
-" течение"
-" ти"
-" титу"
-" то"
-" това"
-" того"
-" той"
-" только"
-" том"
-" тому"
-" тор"
-" тра"
-" трав"
-" травня"
-" тради"
-" тран"
-" тре"
-" три"
-" тро"
-" тру"
-" ту"
-" тур"
-" турни"
-" ты"
-" тя"
-" у"
-" уби"
-" уда"
-" уез"
-" уже"
-" уз"
-" укра"
-" україн"
-" ули"
-" уни"
-" университе"
-" университета"
-" уні"
-" університе"
-" упо"
-" управ"
-" уров"
-" ус"
-" успе"
-" установ"
-" уча"
-" участ"
-" участи"
-" участие"
-" уче"
-" учи"
-" учё"
-" ф"
-" фа"
-" факуль"
-" фами"
-" фамили"
-" фамилией"
-" фе"
-" фев"
-" февра"
-" февраля"
-" фи"
-" физи"
-" филь"
-" фло"
-" фо"
-" фон"
-" фор"
-" фото"
-" фран"
-" францу"
-" француз"
-" фрон"
-" функ"
-" футбо"
-" фі"
-" філь"
-" х"
-" ха"
-" харак"
-" хи"
-" хо"
-" хозяй"
-" хра"
-" худо"
-" худож"
-" ц"
-" цар"
-" це"
-" цент"
-" центр"
-" центра"
-" цер"
-" церкви"
-" церков"
-" ци"
-" ць"
-" цього"
-" ці"
-" ч"
-" ча"
-" час"
-" част"
-" части"
-" часто"
-" часть"
-" че"
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-"买来"
-"买点"
-"买菜"
-"买车"
-"买过"
-"买进"
-"乱"
-"乱七八糟"
-"乱世"
-"乱说"
-"乱象"
-"乳"
-"乳化"
-"乳头"
-"乳房"
-"乳汁"
-"乳液"
-"乳糖"
-"乳胶"
-"乳腺"
-"乳腺癌"
-"乳酸"
-"乾"
-"乾坤"
-"乾隆"
-"了"
-"了不起"
-"了个"
-"了然"
-"了结"
-"了解"
-"予"
-"予以"
-"争"
-"争先"
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-"争吵"
-"争夺"
-"争执"
-"争斗"
-"争相"
-"争端"
-"争议"
-"争论"
-"争霸"
-"事"
-"事业"
-"事业单位"
-"事业部"
-"事事"
-"事件"
-"事例"
-"事儿"
-"事先"
-"事关"
-"事前"
-"事务"
-"事务所"
-"事半"
-"事半功倍"
-"事发"
-"事变"
-"事后"
-"事宜"
-"事实"
-"事实上"
-"事实证明"
-"事态"
-"事情"
-"事故"
-"事物"
-"事记"
-"事诉讼"
-"事迹"
-"事项"
-"二手"
-"二手房"
-"二手车"
-"二维码"
-"二胎"
-"二胡"
-"二进制"
-"于"
-"于世"
-"于事"
-"于土"
-"于心"
-"于是"
-"亏"
-"亏损"
-"亏本"
-"云"
-"云南"
-"云南省"
-"云海"
-"云狗"
-"云端"
-"云集"
-"云雾"
-"云龙"
-"互"
-"互不"
-"互为"
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-"互惠"
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-"互联网"
-"互补"
-"互通"
-"亓"
-"五官"
-"五彩"
-"五指"
-"五粮"
-"五粮液"
-"五脏"
-"五脏六腑"
-"五色"
-"五花"
-"五花八门"
-"五花肉"
-"五谷"
-"五金"
-"井"
-"井上"
-"井冈山"
-"井水"
-"亘"
-"亚"
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-"亚健康"
-"亚军"
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-"亚热"
-"亚特"
-"亚特兰大"
-"亚瑟"
-"亚硝"
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-"亚运"
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-"些"
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-"亡"
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-"亡灵"
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-"交"
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-"交房"
-"交所"
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-"交接"
-"交易"
-"交易中心"
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-"交易平台"
-"交易所"
-"交易日"
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-"交易者"
-"交易量"
-"交替"
-"交朋友"
-"交汇"
-"交汇处"
-"交流"
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-"交界"
-"交界处"
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-"交给"
-"交融"
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-"交谈"
-"交货"
-"交通"
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-"交集"
-"亥"
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-"亨"
-"亨利"
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-"京"
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-"人太多"
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-"人情"
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-"人意"
-"人手"
-"人才"
-"人才培养"
-"人数"
-"人文"
-"人有"
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-"人权"
-"人来"
-"人格"
-"人次"
-"人民"
-"人民代表大会"
-"人民公社"
-"人民共和国"
-"人民币"
-"人民政府"
-"人民日报"
-"人民检察院"
-"人民法院"
-"人民网"
-"人气"
-"人流"
-"人海"
-"人物"
-"人生"
-"人生观"
-"人用"
-"人知"
-"人称"
-"人类"
-"人类学"
-"人缘"
-"人群"
-"人能"
-"人脉"
-"人脸"
-"人脸识别"
-"人行"
-"人行道"
-"人要"
-"人设"
-"人身"
-"人选"
-"人造"
-"人道"
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-"人间"
-"人际"
-"人际交往"
-"人际关系"
-"人马"
-"亻"
-"什"
-"什么"
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-"仅此"
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-"今夜"
-"今天"
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-"今属"
-"今年"
-"今年以来"
-"今日"
-"今晚"
-"今朝"
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-"仑"
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-"仙"
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-"仞"
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-"令"
-"令人"
-"令狐冲"
-"令营"
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-"仨"
-"仪"
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-"仲"
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-"任何"
-"任何人"
-"任免"
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-"任务"
-"任命"
-"任性"
-"任意"
-"任教"
-"任期"
-"任由"
-"任职"
-"份"
-"份额"
-"仿"
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-"仿古"
-"仿真"
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-"企业家"
-"企事"
-"企事业"
-"企划"
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-"伉"
-"伊"
-"伊丽莎白"
-"伊利"
-"伊始"
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-"伊斯兰教"
-"伊朗"
-"伊犁"
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-"伍"
-"伍德"
-"伎"
-"伏"
-"伏羲"
-"伐"
-"休"
-"休假"
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-"优"
-"优于"
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-"优惠活动"
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-"优点"
-"优秀"
-"优秀人才"
-"优秀教师"
-"优缺"
-"优美"
-"优胜"
-"优良"
-"优质"
-"优质服务"
-"优越"
-"优选"
-"优酷"
-"优雅"
-"伙"
-"伙伴"
-"伙食"
-"会"
-"会上"
-"会为"
-"会以"
-"会儿"
-"会务"
-"会变"
-"会同"
-"会员"
-"会场"
-"会展"
-"会展中心"
-"会想"
-"会见"
-"会计"
-"会计学"
-"会计师"
-"会议"
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-"会诊"
-"会谈"
-"会费"
-"会长"
-"会阴"
-"伛"
-"伝"
-"伞"
-"伟"
-"伟人"
-"伟大"
-"传"
-"传世"
-"传人"
-"传入"
-"传出"
-"传到"
-"传动"
-"传奇"
-"传媒"
-"传导"
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-"传感器"
-"传承"
-"传授"
-"传播"
-"传播学"
-"传教"
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-"传染性"
-"传染病"
-"传热"
-"传球"
-"传真"
-"传神"
-"传祺"
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-"传统"
-"传言"
-"传记"
-"传说"
-"传输"
-"传输速"
-"传达"
-"传送"
-"传递"
-"传销"
-"传闻"
-"伢"
-"伤"
-"伤亡"
-"伤人"
-"伤口"
-"伤官"
-"伤害"
-"伤寒"
-"伤心"
-"伤感"
-"伤残"
-"伤病"
-"伤痕"
-"伤痛"
-"伤者"
-"伥"
-"伦"
-"伦敦"
-"伦理"
-"伦理学"
-"伧"
-"伪"
-"伪劣"
-"伪装"
-"伪造"
-"伫"
-"伯"
-"伯克"
-"伯恩"
-"伯格"
-"伯爵"
-"伯特"
-"估"
-"估价"
-"估值"
-"估算"
-"估计"
-"伴"
-"伴侣"
-"伴奏"
-"伴有"
-"伴着"
-"伴随"
-"伶"
-"伸"
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-"伸展"
-"伸手"
-"伸直"
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-"伸长"
-"伺"
-"伺候"
-"伺服"
-"似"
-"似乎"
-"似的"
-"伽"
-"佃"
-"但"
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-"但愿"
-"但是"
-"位"
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-"位列"
-"位子"
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-"位数"
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-"位置"
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-"低下"
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-"体裁"
-"体谅"
-"体质"
-"体贴"
-"体重"
-"体量"
-"体面"
-"体验"
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-"何不"
-"何为"
-"何从"
-"何以"
-"何其"
-"何况"
-"何去"
-"何在"
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-"何尝"
-"何必"
-"何方"
-"何时"
-"何种"
-"何等"
-"何苦"
-"何谓"
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-"佘"
-"余"
-"余个"
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-"余年"
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-"余部"
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-"佚"
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-"佛山市"
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-"佛说"
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-"佟"
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-"侵入"
-"侵华"
-"侵占"
-"侵害"
-"侵权"
-"侵犯"
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-"农机"
-"农村"
-"农林"
-"农民"
-"农民工"
-"农民收入"
-"农牧"
-"农用"
-"农田"
-"农科"
-"农耕"
-"农药"
-"农贸"
-"农贸市场"
-"冠"
-"冠军"
-"冠名"
-"冠希"
-"冠心病"
-"冠状"
-"冠状病毒"
-"冢"
-"冤"
-"冤家"
-"冤枉"
-"冥"
-"冥冥"
-"冥想"
-"冬"
-"冬天"
-"冬季"
-"冬日"
-"冬瓜"
-"冬至"
-"冯"
-"冰"
-"冰冰"
-"冰冷"
-"冰冻"
-"冰凉"
-"冰块"
-"冰山"
-"冰岛"
-"冰川"
-"冰河"
-"冰淇淋"
-"冰激"
-"冰箱"
-"冰糖"
-"冰雪"
-"冲"
-"冲上"
-"冲冲"
-"冲出"
-"冲击"
-"冲击力"
-"冲刷"
-"冲刺"
-"冲动"
-"冲压"
-"冲天"
-"冲泡"
-"冲洗"
-"冲浪"
-"冲着"
-"冲破"
-"冲突"
-"冲绳"
-"冲锋"
-"冲高"
-"决"
-"决不"
-"决定"
-"决定性"
-"决心"
-"决战"
-"决斗"
-"决断"
-"决策"
-"决算"
-"决胜"
-"决议"
-"决赛"
-"况"
-"况且"
-"冶"
-"冶炼"
-"冶金"
-"冷"
-"冷冷"
-"冷冻"
-"冷凝"
-"冷却"
-"冷战"
-"冷暖"
-"冷水"
-"冷汗"
-"冷淡"
-"冷清"
-"冷漠"
-"冷热"
-"冷空气"
-"冷笑"
-"冷落"
-"冷藏"
-"冷血"
-"冷酷"
-"冷门"
-"冷静"
-"冷饮"
-"冻"
-"冻结"
-"冼"
-"冽"
-"净"
-"净值"
-"净净"
-"净利润"
-"净化"
-"净化器"
-"净土"
-"净水"
-"净水器"
-"净流入"
-"净资产"
-"凄"
-"凄凉"
-"准"
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-"准则"
-"准备"
-"准备金"
-"准妈"
-"准妈妈"
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-"准确"
-"准确性"
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-"凉"
-"凉拌"
-"凉水"
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-"减"
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-"减仓"
-"减低"
-"减免"
-"减压"
-"减去"
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-"减排"
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-"减脂"
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-"凝结"
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-"几件"
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-"凤"
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-"凤姐"
-"凫"
-"凭"
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-"凭着"
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-"凭证"
-"凯"
-"凯尔特"
-"凯尔特人"
-"凯恩斯"
-"凯撒"
-"凯文"
-"凯旋"
-"凯特"
-"凯迪"
-"凯迪拉克"
-"凰"
-"凳"
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-"凶"
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-"凶残"
-"凶猛"
-"凸"
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-"凹"
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-"出"
-"出世"
-"出乎"
-"出了"
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-"出于"
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-"出人"
-"出任"
-"出众"
-"出入"
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-"出家"
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-"出席"
-"出席会议"
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-"出征"
-"出息"
-"出战"
-"出手"
-"出新"
-"出来"
-"出柜"
-"出栏"
-"出水"
-"出汗"
-"出没"
-"出海"
-"出游"
-"出演"
-"出炉"
-"出版"
-"出版社"
-"出现"
-"出生"
-"出生地"
-"出示"
-"出租"
-"出租车"
-"出线"
-"出自"
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-"出行"
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-"出货"
-"出货量"
-"出资"
-"出走"
-"出路"
-"出身"
-"出轨"
-"出过"
-"出道"
-"出钱"
-"出锅"
-"出错"
-"出门"
-"出院"
-"出面"
-"击"
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-"击杀"
-"击球"
-"击败"
-"凼"
-"函"
-"函授"
-"函数"
-"凿"
-"刀"
-"刀具"
-"刀切"
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-"刀片"
-"刁"
-"刃"
-"分"
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-"分为"
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-"分会"
-"分公司"
-"分别"
-"分割"
-"分化"
-"分区"
-"分发"
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-"分娩"
-"分子"
-"分子生物学"
-"分寸"
-"分局"
-"分层"
-"分工"
-"分布"
-"分布式"
-"分开"
-"分成"
-"分手"
-"分批"
-"分担"
-"分支"
-"分支机构"
-"分散"
-"分数"
-"分数线"
-"分时"
-"分明"
-"分期"
-"分机"
-"分析"
-"分析仪"
-"分析师"
-"分枝"
-"分校"
-"分歧"
-"分段"
-"分水"
-"分泌"
-"分泌物"
-"分流"
-"分清"
-"分界"
-"分离"
-"分管"
-"分类"
-"分红"
-"分级"
-"分组"
-"分给"
-"分行"
-"分裂"
-"分解"
-"分辨"
-"分辨率"
-"分部"
-"分配"
-"分量"
-"分钟"
-"分销"
-"分队"
-"分院"
-"分隔"
-"切"
-"切不可"
-"切丝"
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-"切碎"
-"切磋"
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-"刈"
-"刊"
-"刊物"
-"刊登"
-"刍"
-"刎"
-"刑"
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-"刑事拘留"
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-"刑警"
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-"列表"
-"列车"
-"列颠"
-"刘"
-"刘备"
-"刘海"
-"刘秀"
-"刘翔"
-"则"
-"刚"
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-"初心"
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-"初次"
-"初步"
-"初级"
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-"判"
-"判决"
-"判别"
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-"別"
-"刨"
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-"利亚"
-"利器"
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-"利害"
-"利尿"
-"利己"
-"利息"
-"利斯"
-"利比亚"
-"利润"
-"利润率"
-"利物"
-"利物浦"
-"利率"
-"利用"
-"利用率"
-"利益"
-"利税"
-"利空"
-"利落"
-"别"
-"别人"
-"别克"
-"别具"
-"别名"
-"别墅"
-"别太"
-"别姬"
-"别忘"
-"别忘了"
-"别想"
-"别扭"
-"别提"
-"别有"
-"别样"
-"别的"
-"别看"
-"别离"
-"别致"
-"别说"
-"刭"
-"刮"
-"到"
-"到位"
-"到场"
-"到处"
-"到大"
-"到头"
-"到头来"
-"到家"
-"到底"
-"到手"
-"到时候"
-"到晚"
-"到期"
-"到来"
-"到达"
-"刳"
-"制"
-"制作"
-"制作方法"
-"制冷"
-"制剂"
-"制动"
-"制品"
-"制图"
-"制备"
-"制定"
-"制宜"
-"制导"
-"制度"
-"制式"
-"制成"
-"制服"
-"制止"
-"制法"
-"制热"
-"制片"
-"制片人"
-"制程"
-"制约"
-"制胜"
-"制药"
-"制裁"
-"制订"
-"制造"
-"制造业"
-"制造厂"
-"制造商"
-"制高"
-"刷"
-"刷刷"
-"刷卡"
-"刷子"
-"刷屏"
-"刷新"
-"刷牙"
-"刷票"
-"券"
-"券商"
-"刹"
-"刹车"
-"刹那"
-"刺"
-"刺史"
-"刺客"
-"刺杀"
-"刺激"
-"刺激性"
-"刺猬"
-"刺痛"
-"刺绣"
-"刻"
-"刻刻"
-"刻度"
-"刻录"
-"刻意"
-"刻画"
-"刻苦"
-"刻骨"
-"刻骨铭心"
-"刽"
-"刿"
-"剀"
-"剁"
-"剂"
-"剂量"
-"剃"
-"則"
-"削"
-"削减"
-"削弱"
-"剋"
-"剌"
-"前"
-"前不久"
-"前世"
-"前些"
-"前人"
-"前任"
-"前值"
-"前列"
-"前列腺"
-"前列腺炎"
-"前卫"
-"前去"
-"前台"
-"前后"
-"前夕"
-"前天"
-"前奏"
-"前女"
-"前妻"
-"前年"
-"前往"
-"前所"
-"前所未有"
-"前排"
-"前提"
-"前方"
-"前景"
-"前期"
-"前来"
-"前段"
-"前段时间"
-"前沿"
-"前瞻"
-"前瞻性"
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-"前线"
-"前置"
-"前者"
-"前脸"
-"前茅"
-"前行"
-"前要"
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-"前辈"
-"前进"
-"前途"
-"前锋"
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-"剩女"
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-"剪纸"
-"剪裁"
-"剪辑"
-"副"
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-"副司令"
-"副处"
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-"副局长"
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-"副总裁"
-"副教授"
-"副本"
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-"副省"
-"副科"
-"副组"
-"副驾驶"
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-"剽"
-"剿"
-"劁"
-"劇"
-"劈"
-"劉"
-"劓"
-"力"
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-"力图"
-"力学"
-"力宏"
-"力度"
-"力所能及"
-"力气"
-"力求"
-"力矩"
-"力量"
-"劝"
-"劝导"
-"劝说"
-"劝阻"
-"办"
-"办事"
-"办事处"
-"办公"
-"办公厅"
-"办公室"
-"办公桌"
-"办公楼"
-"办好"
-"办学"
-"办案"
-"办法"
-"办理"
-"功"
-"功利"
-"功力"
-"功劳"
-"功勋"
-"功名"
-"功夫"
-"功底"
-"功德"
-"功效"
-"功法"
-"功率"
-"功用"
-"功耗"
-"功能"
-"功能性"
-"功能障碍"
-"功臣"
-"功课"
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-"加以"
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-"加倍"
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-"加盖"
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-"加速度"
-"加重"
-"加长"
-"务"
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-"务工"
-"务必"
-"劢"
-"劣"
-"劣势"
-"劣质"
-"动"
-"动不"
-"动不动"
-"动乱"
-"动人"
-"动作"
-"动力"
-"动力学"
-"动力电池"
-"动力系统"
-"动向"
-"动听"
-"动员"
-"动工"
-"动心"
-"动态"
-"动感"
-"动手"
-"动摇"
-"动机"
-"动植物"
-"动漫"
-"动物"
-"动物园"
-"动用"
-"动画"
-"动画片"
-"动症"
-"动能"
-"动脉"
-"动脉硬化"
-"动脑"
-"动荡"
-"动词"
-"动车"
-"动辄"
-"动静"
-"动魄"
-"助"
-"助人"
-"助力"
-"助听"
-"助听器"
-"助学"
-"助手"
-"助推"
-"助攻"
-"助理"
-"助长"
-"努"
-"努力"
-"努力学习"
-"劫"
-"劫持"
-"劬"
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-"励"
-"励志"
-"劲"
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-"劳"
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-"劳动力"
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-"劳动者"
-"劳工"
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-"劳累"
-"劳资"
-"劼"
-"劾"
-"势"
-"势利"
-"势力"
-"势头"
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-"势必会"
-"勃"
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-"勃起"
-"勇"
-"勇于"
-"勇士"
-"勇士队"
-"勇敢"
-"勇气"
-"勇猛"
-"勇者"
-"勉"
-"勉强"
-"勋"
-"勋章"
-"勐"
-"勒"
-"勒布"
-"勒索"
-"動"
-"勖"
-"勘"
-"勘察"
-"勘探"
-"勘查"
-"勘测"
-"務"
-"勝"
-"募"
-"募捐"
-"募集"
-"勢"
-"勤"
-"勤俭"
-"勤劳"
-"勤奋"
-"勤工"
-"勰"
-"勺"
-"勺子"
-"勾"
-"勾勒"
-"勾搭"
-"勿"
-"勿扰"
-"匀"
-"包"
-"包养"
-"包包"
-"包含"
-"包围"
-"包头"
-"包子"
-"包容"
-"包括"
-"包皮"
-"包袱"
-"包装"
-"包裹"
-"包邮"
-"匆"
-"匆匆"
-"匆忙"
-"匈"
-"匈奴"
-"匈牙利"
-"匍"
-"匏"
-"匐"
-"匕"
-"化"
-"化为"
-"化作"
-"化合"
-"化合物"
-"化名"
-"化妆"
-"化妆品"
-"化学"
-"化学反应"
-"化学品"
-"化学物质"
-"化工"
-"化成"
-"化疗"
-"化痰"
-"化瘀"
-"化石"
-"化纤"
-"化肥"
-"化脓"
-"化解"
-"化身"
-"化验"
-"北"
-"北上"
-"北京"
-"北京大学"
-"北京市"
-"北京师范大学"
-"北京电影学院"
-"北伐"
-"北侧"
-"北区"
-"北大"
-"北宋"
-"北平"
-"北斗"
-"北方"
-"北极"
-"北欧"
-"北汽"
-"北洋"
-"北海"
-"北海道"
-"北站"
-"北约"
-"北纬"
-"北美"
-"北航"
-"北路"
-"北邻"
-"北部"
-"北面"
-"北风"
-"北魏"
-"北齐"
-"匙"
-"匜"
-"匝"
-"匠"
-"匠心"
-"匡"
-"匣"
-"匦"
-"匪"
-"匪夷"
-"匮"
-"匮乏"
-"匹"
-"匹林"
-"匹配"
-"区"
-"区位"
-"区内"
-"区分"
-"区划"
-"区别"
-"区县"
-"区块"
-"区域"
-"区域性"
-"区委"
-"区政府"
-"区长"
-"区间"
-"医"
-"医保"
-"医务"
-"医务人员"
-"医嘱"
-"医学"
-"医学会"
-"医学院"
-"医师"
-"医护"
-"医护人员"
-"医术"
-"医治"
-"医生"
-"医用"
-"医疗"
-"医疗保险"
-"医疗卫生"
-"医疗器械"
-"医疗机构"
-"医科"
-"医科大学"
-"医药"
-"医闹"
-"医院"
-"匾"
-"匿"
-"匿名"
-"區"
-"十堰"
-"十字军"
-"十字架"
-"十字路"
-"十字路口"
-"千万别"
-"千千万"
-"千古"
-"千寻"
-"千山"
-"千方百计"
-"千玺"
-"千秋"
-"千金"
-"卅"
-"升"
-"升值"
-"升华"
-"升学"
-"升温"
-"升级"
-"升职"
-"升至"
-"升起"
-"升降"
-"升高"
-"午"
-"午后"
-"午夜"
-"午睡"
-"午餐"
-"午饭"
-"卉"
-"半"
-"半个"
-"半决赛"
-"半句"
-"半场"
-"半夏"
-"半夜"
-"半天"
-"半导体"
-"半小"
-"半小时"
-"半山"
-"半岛"
-"半年"
-"半径"
-"半数"
-"半斤"
-"半月"
-"半死"
-"半点"
-"半球"
-"半生"
-"半路"
-"半身"
-"半透明"
-"半部"
-"华"
-"华东"
-"华东师范大学"
-"华中"
-"华中科技"
-"华为"
-"华丽"
-"华人"
-"华侨"
-"华北"
-"华北大"
-"华南"
-"华南理工"
-"华夏"
-"华尔"
-"华尔街"
-"华山"
-"华文"
-"华润"
-"华盛顿"
-"华硕"
-"华纳"
-"华美"
-"华西"
-"华语"
-"协"
-"协会"
-"协会会"
-"协会会员"
-"协作"
-"协力"
-"协办"
-"协助"
-"协同"
-"协和"
-"协商"
-"协奏"
-"协定"
-"协议"
-"协议书"
-"协调"
-"卑"
-"卑微"
-"卑鄙"
-"卒"
-"卓"
-"卓著"
-"卓越"
-"单"
-"单一"
-"单个"
-"单亲"
-"单人"
-"单从"
-"单价"
-"单位"
-"单体"
-"单元"
-"单击"
-"单列"
-"单单"
-"单反"
-"单向"
-"单品"
-"单子"
-"单手"
-"单打"
-"单日"
-"单曲"
-"单机"
-"单板"
-"单核"
-"单片"
-"单独"
-"单相"
-"单纯"
-"单薄"
-"单行"
-"单词"
-"单调"
-"单身"
-"单车"
-"单边"
-"单项"
-"卖"
-"卖不出"
-"卖出"
-"卖场"
-"卖家"
-"卖掉"
-"卖方"
-"卖淫"
-"卖点"
-"卖萌"
-"南"
-"南下"
-"南亚"
-"南京"
-"南京大学"
-"南京大屠杀"
-"南京市"
-"南侧"
-"南充"
-"南北"
-"南北朝"
-"南区"
-"南县"
-"南宁"
-"南宁市"
-"南安"
-"南宋"
-"南宫"
-"南山"
-"南岸"
-"南开"
-"南开大学"
-"南斯拉夫"
-"南方"
-"南方人"
-"南无"
-"南昌"
-"南朝"
-"南极"
-"南沙"
-"南洋"
-"南海"
-"南湖"
-"南瓜"
-"南站"
-"南美"
-"南航"
-"南路"
-"南通"
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-"南部"
-"南门"
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-"南面"
-"博"
-"博主"
-"博会"
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-"博士生"
-"博大"
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-"博弈"
-"博彩"
-"博文"
-"博爱"
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-"卜"
-"卞"
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-"卢"
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-"卣"
-"卤"
-"卦"
-"卧"
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-"卧槽"
-"卧铺"
-"卧龙"
-"卫"
-"卫健"
-"卫国"
-"卫士"
-"卫星"
-"卫浴"
-"卫生"
-"卫生局"
-"卫生组织"
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-"卮"
-"卯"
-"印"
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-"印发"
-"印尼"
-"印度"
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-"印度尼西亚"
-"印章"
-"印第安"
-"印花"
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-"印证"
-"印象"
-"危"
-"危及"
-"危害"
-"危急"
-"危机"
-"危重"
-"危险"
-"危险性"
-"即"
-"即位"
-"即使"
-"即便"
-"即兴"
-"即刻"
-"即可"
-"即将"
-"即日起"
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-"即用"
-"却"
-"却是"
-"却说"
-"卵"
-"卵圆"
-"卵子"
-"卵巢"
-"卵形"
-"卵用"
-"卵管"
-"卷"
-"卷入"
-"卷发"
-"卷子"
-"卷烟"
-"卷轴"
-"卸"
-"卸妆"
-"卸载"
-"卻"
-"卿"
-"厂"
-"厂商"
-"厂家"
-"厂房"
-"厂长"
-"厄"
-"厅"
-"厅长"
-"历"
-"历代"
-"历任"
-"历史"
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-"历史悠久"
-"历年"
-"历时"
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-"历经"
-"历险"
-"厉"
-"厉害"
-"压"
-"压倒"
-"压制"
-"压力"
-"压器"
-"压实"
-"压抑"
-"压根"
-"压榨"
-"压缩"
-"压缩机"
-"压迫"
-"厌"
-"厌倦"
-"厌恶"
-"厌烦"
-"厍"
-"厕"
-"厕所"
-"厘"
-"厘头"
-"厘米"
-"厚"
-"厚厚"
-"厚厚的"
-"厚实"
-"厚度"
-"厚道"
-"厚重"
-"厚非"
-"厝"
-"原"
-"原产"
-"原产地"
-"原件"
-"原价"
-"原住"
-"原作"
-"原作者"
-"原先"
-"原则"
-"原则上"
-"原创"
-"原发"
-"原名"
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-"原味"
-"原唱"
-"原因"
-"原地"
-"原型"
-"原声"
-"原始"
-"原子"
-"原文"
-"原料"
-"原有"
-"原本"
-"原材料"
-"原来"
-"原汁"
-"原汁原味"
-"原油"
-"原版"
-"原理"
-"原生"
-"原生态"
-"原画"
-"原装"
-"原谅"
-"厢"
-"厢情"
-"厥"
-"厦"
-"厦门"
-"厦门市"
-"厨"
-"厨具"
-"厨师"
-"厨房"
-"厩"
-"厮"
-"去"
-"去世"
-"去向"
-"去处"
-"去年"
-"去年同期"
-"去往"
-"去掉"
-"去皮"
-"去过"
-"去除"
-"厾"
-"县"
-"县城"
-"县域"
-"县委"
-"县委书记"
-"县市"
-"县政府"
-"县级"
-"县长"
-"叁"
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-"参与者"
-"参会"
-"参保"
-"参军"
-"参加"
-"参展"
-"参差"
-"参战"
-"参政"
-"参数"
-"参演"
-"参照"
-"参考"
-"参考书"
-"参考文献"
-"参考资料"
-"参见"
-"参观"
-"参议"
-"参谋"
-"参谋长"
-"参赛"
-"参选"
-"參"
-"叆"
-"又"
-"又名"
-"又称"
-"叉"
-"及"
-"及其"
-"及待"
-"及早"
-"及时"
-"及时发现"
-"及格"
-"友"
-"友人"
-"友们"
-"友善"
-"友好"
-"友情"
-"友爱"
-"友谊"
-"双"
-"双人"
-"双休"
-"双侧"
-"双击"
-"双双"
-"双向"
-"双子"
-"双层"
-"双床"
-"双手"
-"双打"
-"双方"
-"双核"
-"双眼"
-"双眼皮"
-"双肩"
-"双胞"
-"双胞胎"
-"双脚"
-"双腿"
-"双臂"
-"双色"
-"双语"
-"双赢"
-"双边"
-"双重"
-"双面"
-"双鱼"
-"双龙"
-"反"
-"反之"
-"反倒"
-"反光"
-"反击"
-"反动"
-"反反"
-"反反复复"
-"反叛"
-"反向"
-"反响"
-"反复"
-"反对"
-"反射"
-"反差"
-"反应"
-"反应堆"
-"反弹"
-"反思"
-"反恐"
-"反感"
-"反抗"
-"反攻"
-"反映"
-"反智"
-"反正"
-"反派"
-"反省"
-"反而"
-"反腐"
-"反观"
-"反转"
-"反过来"
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-"发作"
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-"发出"
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-"发呆"
-"发售"
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-"发小"
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-"发布公告"
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-"发抖"
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-"发挥作用"
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-"发改委"
-"发放"
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-"发文"
-"发明"
-"发明专利"
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-"发泡"
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-"发火"
-"发炎"
-"发烧"
-"发热"
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-"发现自己"
-"发球"
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-"发生率"
-"发电"
-"发电机"
-"发病"
-"发病率"
-"发短信"
-"发票"
-"发红"
-"发给"
-"发育"
-"发胖"
-"发脾气"
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-"发芽"
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-"发誓"
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-"发货"
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-"发黑"
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-"变异"
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-"变态"
-"变性"
-"变成"
-"变换"
-"变更"
-"变焦"
-"变现"
-"变电"
-"变白"
-"变相"
-"变美"
-"变色"
-"变质"
-"变迁"
-"变速"
-"变速器"
-"变速箱"
-"变量"
-"变革"
-"变频"
-"变黑"
-"叙"
-"叙事"
-"叙利亚"
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-"叛逆"
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-"口吻"
-"口味"
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-"口水"
-"口渴"
-"口碑"
-"口红"
-"口罩"
-"口腔"
-"口腔溃疡"
-"口臭"
-"口舌"
-"口袋"
-"口诀"
-"口译"
-"口语"
-"古"
-"古人"
-"古今"
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-"古兰"
-"古典"
-"古城"
-"古墓"
-"古巴"
-"古希腊"
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-"古怪"
-"古文"
-"古时"
-"古朴"
-"古树"
-"古玩"
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-"台灯"
-"台独"
-"台球"
-"台词"
-"台阶"
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-"台风"
-"叱"
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-"史以"
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-"史诗"
-"右"
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-"右手"
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-"叵"
-"叶"
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-"叶轮"
-"叶酸"
-"号"
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-"号楼"
-"号牌"
-"号码"
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-"吃喝玩乐"
-"吃完饭"
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-"吃水果"
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-"吃点"
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-"吃素"
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-"吃药"
-"吃货"
-"吃过"
-"吃醋"
-"吃饭"
-"吃饱"
-"各"
-"各不相同"
-"各业"
-"各个"
-"各个方面"
-"各个环节"
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-"各位"
-"各区"
-"各县"
-"各国"
-"各地"
-"各处"
-"各大"
-"各家"
-"各异"
-"各式"
-"各式各样"
-"各方"
-"各族"
-"各有"
-"各有所"
-"各样"
-"各界"
-"各省"
-"各种"
-"各种各样"
-"各科"
-"各类"
-"各级"
-"各自"
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-"各行"
-"各行各业"
-"各路"
-"各项"
-"吆"
-"合"
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-"合伙"
-"合伙人"
-"合体"
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-"合唱"
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-"合成"
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-"合法权益"
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-"合理"
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-"合约"
-"合群"
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-"吉"
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-"吊"
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-"吊顶"
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-"名为"
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-"名列"
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-"名号"
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-"吏"
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-"啉"
-"啊"
-"啊啊"
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-"問題"
-"啐"
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-"啖"
-"啜"
-"啡"
-"啤"
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-"啥"
-"啦"
-"啧"
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-"啪"
-"啬"
-"啭"
-"啮"
-"啰"
-"啰嗦"
-"啵"
-"啶"
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-"嘧"
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-"嘿嘿"
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-"器材"
-"器械"
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-"噩噩"
-"噩梦"
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-"噪音"
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-"嚣张"
-"嚯"
-"嚴"
-"嚷"
-"嚼"
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-"囊肿"
-"囔"
-"囗"
-"囚"
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-"四射"
-"四川"
-"四川省"
-"四库"
-"四方"
-"四时"
-"四海"
-"四肢"
-"四通"
-"四驱"
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-"回乡"
-"回事"
-"回到"
-"回升"
-"回去"
-"回合"
-"回味"
-"回国"
-"回复"
-"回头"
-"回家"
-"回应"
-"回归"
-"回忆"
-"回忆起"
-"回想"
-"回报"
-"回报率"
-"回撤"
-"回收"
-"回旋"
-"回族"
-"回暖"
-"回来"
-"回民"
-"回流"
-"回眸"
-"回答"
-"回老"
-"回老家"
-"回落"
-"回访"
-"回调"
-"回购"
-"回路"
-"回转"
-"回过"
-"回避"
-"回顾"
-"回馈"
-"回首"
-"囟"
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-"团团"
-"团圆"
-"团委"
-"团战"
-"团支"
-"团结"
-"团聚"
-"团购"
-"团长"
-"团队"
-"囤"
-"囧"
-"囫"
-"园"
-"园内"
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-"园林"
-"园艺"
-"困"
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-"困惑"
-"困扰"
-"困难"
-"囱"
-"围"
-"围城"
-"围墙"
-"围巾"
-"围攻"
-"围棋"
-"围着"
-"围绕"
-"围观"
-"囵"
-"囹"
-"固"
-"固体"
-"固化"
-"固定"
-"固定资产"
-"固态"
-"固执"
-"固有"
-"固然"
-"固醇"
-"国"
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-"国人"
-"国企"
-"国会"
-"国债"
-"国共"
-"国内"
-"国内外"
-"国军"
-"国力"
-"国务"
-"国务院"
-"国土"
-"国土资源"
-"国外"
-"国学"
-"国安"
-"国宝"
-"国家"
-"国家专利"
-"国家有关"
-"国家机关"
-"国家标准"
-"国家税务"
-"国家税务总局"
-"国家级"
-"国家统计局"
-"国家自然科学基金"
-"国家队"
-"国庆"
-"国庆节"
-"国库"
-"国度"
-"国情"
-"国旗"
-"国有"
-"国有企业"
-"国有资产"
-"国服"
-"国标"
-"国歌"
-"国民"
-"国民党"
-"国民政府"
-"国民经济"
-"国民革命军"
-"国泰"
-"国王"
-"国画"
-"国税"
-"国立"
-"国籍"
-"国美"
-"国荣"
-"国药"
-"国营"
-"国藩"
-"国语"
-"国货"
-"国贸"
-"国资"
-"国资委"
-"国足"
-"国道"
-"国门"
-"国防"
-"国防部"
-"国际"
-"国际交流"
-"国际化"
-"国际标准"
-"国际贸易"
-"国际金融"
-"图"
-"图上"
-"图中"
-"图为"
-"图书"
-"图书馆"
-"图像"
-"图册"
-"图层"
-"图形"
-"图文"
-"图标"
-"图案"
-"图片"
-"图画"
-"图纸"
-"图腾"
-"图表"
-"图解"
-"囿"
-"圃"
-"圄"
-"圆"
-"圆圆"
-"圆弧"
-"圆形"
-"圆柱"
-"圆桌"
-"圆润"
-"圆满"
-"圆筒"
-"圆锥"
-"圈"
-"圈内"
-"圈子"
-"圈里"
-"圉"
-"圊"
-"國"
-"園"
-"圖"
-"團"
-"圜"
-"土"
-"土司"
-"土地"
-"土地革命"
-"土壤"
-"土家族"
-"土建"
-"土星"
-"土木"
-"土木工程"
-"土耳其"
-"土著"
-"土豆"
-"土豪"
-"圣"
-"圣人"
-"圣地"
-"圣殿"
-"圣母"
-"圣经"
-"圣诞"
-"圣诞节"
-"圣贤"
-"在"
-"在一起"
-"在世"
-"在世界上"
-"在乎"
-"在于"
-"在位"
-"在内"
-"在历史上"
-"在场"
-"在外"
-"在实践"
-"在实践中"
-"在家"
-"在座"
-"在建"
-"在意"
-"在我"
-"在我看来"
-"在所"
-"在手"
-"在校"
-"在校学生"
-"在此"
-"在此之"
-"在此之前"
-"在此期间"
-"在生活"
-"在生活中"
-"在线"
-"在职"
-"在读"
-"圩"
-"圪"
-"圬"
-"圭"
-"圮"
-"圯"
-"地"
-"地上"
-"地下"
-"地下室"
-"地下水"
-"地中海"
-"地为"
-"地主"
-"地久"
-"地亚"
-"地产"
-"地价"
-"地位"
-"地利"
-"地势"
-"地区"
-"地去"
-"地名"
-"地向"
-"地图"
-"地址"
-"地块"
-"地坪"
-"地域"
-"地基"
-"地壳"
-"地处"
-"地委"
-"地层"
-"地带"
-"地形"
-"地想"
-"地摊"
-"地支"
-"地方"
-"地方税"
-"地板"
-"地标"
-"地步"
-"地段"
-"地毯"
-"地点"
-"地狱"
-"地球"
-"地理"
-"地理位置"
-"地理信息"
-"地理学"
-"地瓜"
-"地用"
-"地盘"
-"地砖"
-"地税"
-"地笑"
-"地级"
-"地缘"
-"地藏"
-"地表"
-"地被"
-"地说"
-"地貌"
-"地质"
-"地质学"
-"地道"
-"地里"
-"地铁"
-"地铁站"
-"地问"
-"地震"
-"地面"
-"地黄"
-"圳"
-"圹"
-"场"
-"场上"
-"场内"
-"场合"
-"场地"
-"场均"
-"场外"
-"场所"
-"场景"
-"场面"
-"场馆"
-"圻"
-"圾"
-"址"
-"坂"
-"均"
-"均价"
-"均匀"
-"均线"
-"均衡"
-"坊"
-"坌"
-"坍"
-"坎"
-"坎坷"
-"坏"
-"坏习惯"
-"坏事"
-"坏人"
-"坏死"
-"坐"
-"坐下"
-"坐在"
-"坐姿"
-"坐标"
-"坐落"
-"坐落于"
-"坐车"
-"坑"
-"块"
-"块状"
-"块钱"
-"坚"
-"坚信"
-"坚决"
-"坚固"
-"坚守"
-"坚定"
-"坚定不移"
-"坚实"
-"坚强"
-"坚持"
-"坚持下去"
-"坚持不懈"
-"坚挺"
-"坚果"
-"坚硬"
-"坚韧"
-"坛"
-"坝"
-"坞"
-"坟"
-"坟墓"
-"坠"
-"坠落"
-"坡"
-"坤"
-"坦"
-"坦克"
-"坦然"
-"坦白"
-"坦言"
-"坦诚"
-"坨"
-"坩"
-"坪"
-"坫"
-"坭"
-"坯"
-"坳"
-"坷"
-"坻"
-"坼"
-"垂"
-"垂体"
-"垂直"
-"垂钓"
-"垃"
-"垃圾"
-"垃圾桶"
-"垄"
-"垄断"
-"垅"
-"垆"
-"型"
-"型号"
-"型材"
-"垌"
-"垒"
-"垓"
-"垛"
-"垠"
-"垡"
-"垢"
-"垣"
-"垤"
-"垦"
-"垧"
-"垩"
-"垫"
-"垫底"
-"垭"
-"垮"
-"垱"
-"垸"
-"埂"
-"埃"
-"埃及"
-"埃尔"
-"埃斯"
-"埋"
-"埋头"
-"埋怨"
-"埋没"
-"埋葬"
-"埋藏"
-"城"
-"城东"
-"城中"
-"城乡"
-"城乡居民"
-"城关"
-"城内"
-"城北"
-"城区"
-"城南"
-"城县"
-"城堡"
-"城墙"
-"城市"
-"城市化"
-"城市群"
-"城建"
-"城管"
-"城西"
-"城郊"
-"城里"
-"城镇"
-"城镇化"
-"城门"
-"城际"
-"城隍"
-"埒"
-"埔"
-"埕"
-"埘"
-"埙"
-"埚"
-"埝"
-"域"
-"域名"
-"埠"
-"埤"
-"埭"
-"埯"
-"埴"
-"埵"
-"埸"
-"培"
-"培养"
-"培植"
-"培育"
-"培训"
-"培训中心"
-"培训班"
-"基"
-"基于"
-"基准"
-"基友"
-"基因"
-"基因组"
-"基地"
-"基尼"
-"基层"
-"基建"
-"基数"
-"基本"
-"基本上"
-"基本功"
-"基本知识"
-"基本面"
-"基点"
-"基督"
-"基督徒"
-"基督教"
-"基石"
-"基础"
-"基础教育"
-"基础知识"
-"基础设施"
-"基站"
-"基调"
-"基质"
-"基部"
-"基金"
-"基金会"
-"埽"
-"堀"
-"堂"
-"堂堂"
-"堂皇"
-"堆"
-"堆放"
-"堆积"
-"堇"
-"堉"
-"堋"
-"堍"
-"堑"
-"堕"
-"堕胎"
-"堕落"
-"堙"
-"堞"
-"堡"
-"堡垒"
-"堤"
-"堪"
-"堪比"
-"堪称"
-"堰"
-"報"
-"場"
-"堵"
-"堵住"
-"堵塞"
-"堵车"
-"塄"
-"塆"
-"塌"
-"塍"
-"塑"
-"塑性"
-"塑料"
-"塑料袋"
-"塑胶"
-"塑造"
-"塔"
-"塔尔"
-"塘"
-"塞"
-"塞尔"
-"塞尔维亚"
-"塞罗"
-"塞进"
-"填"
-"填充"
-"填写"
-"填报"
-"填料"
-"填满"
-"填空"
-"填补"
-"填词"
-"塬"
-"塾"
-"墀"
-"墁"
-"境"
-"境内"
-"境地"
-"境外"
-"境界"
-"墅"
-"墉"
-"墒"
-"墓"
-"墓地"
-"墓志"
-"墓葬"
-"墙"
-"墙上"
-"墙体"
-"墙壁"
-"墙角"
-"墙面"
-"增"
-"增产"
-"增值"
-"增值税"
-"增减"
-"增加"
-"增加值"
-"增压"
-"增发"
-"增多"
-"增大"
-"增幅"
-"增强"
-"增持"
-"增收"
-"增效"
-"增殖"
-"增添"
-"增生"
-"增益"
-"增肌"
-"增至"
-"增补"
-"增设"
-"增资"
-"增进"
-"增速"
-"增量"
-"增长"
-"增长率"
-"增长速度"
-"增高"
-"墟"
-"墨"
-"墨尔本"
-"墨水"
-"墨西哥"
-"墨迹"
-"墨镜"
-"墩"
-"墼"
-"壁"
-"壁垒"
-"壁挂"
-"壁画"
-"壁纸"
-"壅"
-"壑"
-"壕"
-"壤"
-"士"
-"士兵"
-"士官"
-"士气"
-"士顿"
-"壬"
-"壮"
-"壮丽"
-"壮大"
-"壮志"
-"壮族"
-"壮烈"
-"壮观"
-"壮阔"
-"壮阳"
-"声"
-"声乐"
-"声名"
-"声响"
-"声器"
-"声声"
-"声学"
-"声明"
-"声望"
-"声波"
-"声称"
-"声色"
-"声誉"
-"声音"
-"売"
-"壳"
-"壶"
-"壹"
-"处"
-"处世"
-"处事"
-"处于"
-"处以"
-"处分"
-"处在"
-"处境"
-"处处"
-"处女"
-"处方"
-"处死"
-"处理"
-"处理器"
-"处级"
-"处罚"
-"处置"
-"处长"
-"备"
-"备份"
-"备受"
-"备忘"
-"备战"
-"备案"
-"备注"
-"备用"
-"备考"
-"备课"
-"変"
-"复"
-"复习"
-"复产"
-"复仇"
-"复位"
-"复兴"
-"复出"
-"复制"
-"复印"
-"复印件"
-"复原"
-"复发"
-"复古"
-"复合"
-"复合材料"
-"复复"
-"复审"
-"复工"
-"复旦"
-"复旦大学"
-"复杂"
-"复杂性"
-"复查"
-"复核"
-"复活"
-"复苏"
-"复议"
-"复试"
-"复读"
-"夏"
-"夏令"
-"夏令营"
-"夏侯"
-"夏天"
-"夏威夷"
-"夏季"
-"夏日"
-"夏普"
-"夏洛"
-"夏洛特"
-"夏秋"
-"夔"
-"夕"
-"夕阳"
-"外"
-"外乎"
-"外事"
-"外交"
-"外交部"
-"外人"
-"外企"
-"外伤"
-"外侧"
-"外公"
-"外出"
-"外力"
-"外加"
-"外包"
-"外卖"
-"外号"
-"外向"
-"外商"
-"外商投资"
-"外围"
-"外国"
-"外国人"
-"外国语"
-"外地"
-"外墙"
-"外壳"
-"外套"
-"外婆"
-"外媒"
-"外层"
-"外币"
-"外延"
-"外形"
-"外挂"
-"外接"
-"外援"
-"外文"
-"外星"
-"外星人"
-"外景"
-"外来"
-"外汇"
-"外汇储备"
-"外甥"
-"外用"
-"外界"
-"外皮"
-"外省"
-"外科"
-"外籍"
-"外线"
-"外经贸"
-"外置"
-"外行"
-"外衣"
-"外表"
-"外观"
-"外观设计"
-"外语"
-"外语教学"
-"外貌"
-"外贸"
-"外资"
-"外边"
-"外部"
-"外链"
-"外阴"
-"外面"
-"夙"
-"多"
-"多一些"
-"多一点"
-"多个"
-"多为"
-"多久"
-"多么"
-"多于"
-"多云"
-"多些"
-"多人"
-"多以"
-"多伦"
-"多伦多"
-"多位"
-"多余"
-"多元"
-"多元化"
-"多公里"
-"多出"
-"多功能"
-"多加"
-"多半"
-"多单"
-"多发"
-"多变"
-"多名"
-"多喝水"
-"多地"
-"多坏"
-"多处"
-"多多"
-"多大"
-"多天"
-"多头"
-"多好"
-"多姿"
-"多媒体"
-"多家"
-"多少"
-"多层"
-"多层次"
-"多岁"
-"多平方"
-"多年"
-"多彩"
-"多得"
-"多情"
-"多想"
-"多才"
-"多数"
-"多方"
-"多方面"
-"多月"
-"多有"
-"多条"
-"多样"
-"多样化"
-"多样性"
-"多次"
-"多款"
-"多点"
-"多用"
-"多种"
-"多种多样"
-"多种形式"
-"多空"
-"多篇"
-"多米"
-"多糖"
-"多维"
-"多芬"
-"多见"
-"多见于"
-"多谢"
-"多达"
-"多远"
-"多部"
-"多重"
-"多钱"
-"多长"
-"多长时间"
-"多难"
-"多项"
-"多食"
-"夜"
-"夜夜"
-"夜市"
-"夜幕"
-"夜店"
-"夜总"
-"夜晚"
-"夜景"
-"夜深"
-"夜班"
-"夜空"
-"夜色"
-"夜里"
-"夜间"
-"够"
-"夠"
-"夢"
-"夤"
-"大"
-"大不了"
-"大专"
-"大业"
-"大中专"
-"大中华"
-"大中型"
-"大为"
-"大乘"
-"大事"
-"大于"
-"大人"
-"大件"
-"大众"
-"大伙"
-"大会"
-"大体"
-"大佛"
-"大作"
-"大佬"
-"大使"
-"大使馆"
-"大侠"
-"大便"
-"大全"
-"大关"
-"大兴"
-"大写"
-"大军"
-"大减"
-"大到"
-"大力"
-"大力发展"
-"大力支持"
-"大势"
-"大匙"
-"大千"
-"大半"
-"大卫"
-"大厅"
-"大厦"
-"大发"
-"大叔"
-"大叫"
-"大可"
-"大吃"
-"大吉"
-"大同"
-"大名"
-"大吵"
-"大哥"
-"大哭"
-"大唐"
-"大喊"
-"大喜"
-"大国"
-"大圣"
-"大地"
-"大块"
-"大坝"
-"大型"
-"大城市"
-"大堂"
-"大堆"
-"大增"
-"大声"
-"大多"
-"大多数"
-"大大"
-"大大增加"
-"大大小小"
-"大大提高"
-"大大的"
-"大大降低"
-"大夫"
-"大头"
-"大奖"
-"大好"
-"大妈"
-"大姐"
-"大姨"
-"大字"
-"大学"
-"大学毕业"
-"大学生"
-"大宗"
-"大宝"
-"大家"
-"大家庭"
-"大将"
-"大将军"
-"大小"
-"大局"
-"大屏"
-"大屏幕"
-"大展"
-"大山"
-"大峡谷"
-"大巴"
-"大师"
-"大帝"
-"大幅"
-"大幅度"
-"大庆"
-"大度"
-"大开"
-"大德"
-"大怒"
-"大悟"
-"大悲"
-"大意"
-"大戏"
-"大成"
-"大战"
-"大户"
-"大手"
-"大打"
-"大打折扣"
-"大批"
-"大把"
-"大拇指"
-"大招"
-"大提琴"
-"大方"
-"大明"
-"大有"
-"大本"
-"大本营"
-"大杯"
-"大林"
-"大枣"
-"大树"
-"大桥"
-"大棚"
-"大楼"
-"大概"
-"大殿"
-"大气"
-"大气污染"
-"大汉"
-"大汗"
-"大江"
-"大河"
-"大法"
-"大洋"
-"大海"
-"大涨"
-"大清"
-"大湾"
-"大潮"
-"大火"
-"大灯"
-"大炮"
-"大热"
-"大熊猫"
-"大爱"
-"大爷"
-"大片"
-"大牌"
-"大牛"
-"大王"
-"大理"
-"大理石"
-"大病"
-"大白"
-"大白菜"
-"大盘"
-"大碗"
-"大神"
-"大笑"
-"大米"
-"大红"
-"大约"
-"大纲"
-"大肆"
-"大肠"
-"大胆"
-"大胜"
-"大脑"
-"大腿"
-"大臣"
-"大自然"
-"大致"
-"大葱"
-"大蒜"
-"大街"
-"大街小巷"
-"大衣"
-"大西洋"
-"大观"
-"大规模"
-"大豆"
-"大象"
-"大财"
-"大赏"
-"大赛"
-"大跌"
-"大路"
-"大辞"
-"大运"
-"大连"
-"大连市"
-"大连理工"
-"大选"
-"大通"
-"大道"
-"大部"
-"大部分"
-"大都"
-"大都市"
-"大酒店"
-"大量"
-"大钱"
-"大长"
-"大门"
-"大闸"
-"大闹"
-"大队"
-"大阪"
-"大陆"
-"大院"
-"大雨"
-"大雪"
-"大面"
-"大面积"
-"大革命"
-"大额"
-"大风"
-"大餐"
-"大骂"
-"大鱼"
-"大鹏"
-"大麦"
-"大麻"
-"大黄"
-"大龄"
-"天"
-"天上"
-"天下"
-"天下第一"
-"天主"
-"天主教"
-"天亮"
-"天人"
-"天价"
-"天体"
-"天使"
-"天候"
-"天内"
-"天台"
-"天后"
-"天启"
-"天呐"
-"天命"
-"天国"
-"天地"
-"天堂"
-"天大"
-"天天"
-"天子"
-"天安门"
-"天宝"
-"天宫"
-"天山"
-"天干"
-"天平"
-"天府"
-"天性"
-"天成"
-"天才"
-"天数"
-"天文"
-"天文学"
-"天时"
-"天桥"
-"天气"
-"天气预报"
-"天水"
-"天池"
-"天河"
-"天津"
-"天津市"
-"天涯"
-"天灾"
-"天然"
-"天然气"
-"天猫"
-"天王"
-"天生"
-"天皇"
-"天盖"
-"天真"
-"天神"
-"天秤"
-"天空"
-"天窗"
-"天竺"
-"天籁"
-"天线"
-"天花"
-"天花板"
-"天蝎"
-"天赋"
-"天边"
-"天道"
-"天长"
-"天门"
-"天际"
-"天马"
-"天鹅"
-"天黑"
-"天龙"
-"太"
-"太久"
-"太低"
-"太冷"
-"太原"
-"太原市"
-"太后"
-"太和"
-"太多"
-"太大"
-"太太"
-"太好"
-"太好了"
-"太子"
-"太守"
-"太宗"
-"太小"
-"太少"
-"太岁"
-"太差"
-"太平"
-"太平天国"
-"太平洋"
-"太强"
-"太快"
-"太晚"
-"太极"
-"太极拳"
-"太深"
-"太湖"
-"太白"
-"太监"
-"太祖"
-"太空"
-"太累"
-"太行"
-"太贵"
-"太过"
-"太远"
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-"年前"
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-"年华"
-"年均"
-"年夜"
-"年头"
-"年少"
-"年幼"
-"年底"
-"年度"
-"年报"
-"年收入"
-"年春"
-"年月"
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-"年末"
-"年来"
-"年版"
-"年生"
-"年秋"
-"年级"
-"年纪"
-"年终"
-"年终奖"
-"年老"
-"年薪"
-"年货"
-"年轻"
-"年轻人"
-"年轻化"
-"年里"
-"年间"
-"年限"
-"年青"
-"年龄"
-"年龄段"
-"并"
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-"并不一定"
-"并不多"
-"并不大"
-"并不知道"
-"并不需要"
-"并且"
-"并举"
-"并于"
-"并入"
-"并列"
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-"并发症"
-"并处"
-"并存"
-"并未"
-"并用"
-"并称"
-"并肩"
-"并行"
-"并论"
-"并购"
-"并重"
-"并非"
-"幸"
-"幸亏"
-"幸好"
-"幸存"
-"幸福"
-"幸福感"
-"幸福生活"
-"幸运"
-"幺"
-"幻"
-"幻影"
-"幻想"
-"幻灯"
-"幻觉"
-"幼"
-"幼儿"
-"幼儿园"
-"幼小"
-"幼年"
-"幼稚"
-"幼虫"
-"幽"
-"幽灵"
-"幽默"
-"幾"
-"广"
-"广东"
-"广东省"
-"广为"
-"广义"
-"广受"
-"广告"
-"广场"
-"广大"
-"广大群众"
-"广州"
-"广州市"
-"广度"
-"广播"
-"广播电"
-"广播电视"
-"广汽"
-"广泛"
-"广泛应用"
-"广深"
-"广电"
-"广西"
-"广西壮族自治区"
-"广角"
-"广阔"
-"庄"
-"庄严"
-"庄园"
-"庄子"
-"庄家"
-"庄村"
-"庄稼"
-"庄重"
-"庄镇"
-"庆"
-"庆典"
-"庆幸"
-"庆祝"
-"庇"
-"庇护"
-"床"
-"床上"
-"床位"
-"床单"
-"床垫"
-"床头"
-"床边"
-"庋"
-"序"
-"序列"
-"序号"
-"序幕"
-"庐"
-"庐山"
-"庑"
-"库"
-"库克"
-"库存"
-"库容"
-"库尔"
-"库房"
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-"应酬"
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-"庠"
-"庥"
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-"庵"
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-"弁"
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-"异乡"
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-"弃"
-"弄"
-"弄清"
-"弄清楚"
-"弈"
-"弊"
-"弊端"
-"弋"
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-"弑"
-"弓"
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-"引人"
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-"引战"
-"引擎"
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-"引水"
-"引流"
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-"引资"
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-"张家界"
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-"张扬"
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-"弥"
-"弥勒"
-"弥漫"
-"弥补"
-"弦"
-"弦乐"
-"弧"
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-"弧形"
-"弩"
-"弭"
-"弯"
-"弯弯"
-"弯曲"
-"弯腰"
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-"弱"
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-"弱化"
-"弱小"
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-"弱点"
-"弱者"
-"張"
-"強"
-"弹"
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-"弹奏"
-"弹性"
-"弹簧"
-"弹药"
-"弹跳"
-"弹道"
-"强"
-"强人"
-"强健"
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-"强制性"
-"强力"
-"强加"
-"强劲"
-"强势"
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-"强奸"
-"强度"
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-"强强"
-"强悍"
-"强有"
-"强有力"
-"强求"
-"强烈"
-"强盗"
-"强盛"
-"强硬"
-"强者"
-"强行"
-"强调"
-"强身"
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-"弼"
-"彀"
-"归"
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-"归功"
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-"彤"
-"彦"
-"彦宏"
-"彧"
-"彩"
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-"彩票"
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-"彩色"
-"彩虹"
-"彪"
-"彬"
-"彭"
-"彰"
-"彰显"
-"影"
-"影业"
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-"影子"
-"影片"
-"影线"
-"影视"
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-"影迷"
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-"影音"
-"彳"
-"彷"
-"彷徨"
-"役"
-"彻"
-"彻底"
-"彼"
-"彼岸"
-"彼得"
-"彼时"
-"彼此"
-"往"
-"往上"
-"往下"
-"往事"
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-"往往"
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-"征"
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-"征兆"
-"征地"
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-"徉"
-"徊"
-"律"
-"律师"
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-"徐州"
-"徐徐"
-"徐志"
-"徒"
-"徒刑"
-"徒劳"
-"徒弟"
-"徒手"
-"徒步"
-"徕"
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-"微创"
-"微博"
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-"微妙"
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-"微微"
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-"微波"
-"微波炉"
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-"微薄"
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-"微软"
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-"微量元素"
-"微风"
-"徳"
-"徵"
-"德"
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-"德勒"
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-"德尔"
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-"德意志"
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-"德甲"
-"德罗"
-"德育"
-"德行"
-"德语"
-"德里"
-"徼"
-"徽"
-"徽州"
-"徽章"
-"心"
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-"态"
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-"怅"
-"怆"
-"怍"
-"怎"
-"怎么"
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-"怎么回事"
-"怎么样"
-"怎样"
-"怎样才能"
-"怎能"
-"怎麼"
-"怏"
-"怒"
-"怒气"
-"怒火"
-"怔"
-"怕"
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-"怕死"
-"怖"
-"怙"
-"怛"
-"怜"
-"怜悯"
-"思"
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-"思念"
-"思想"
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-"急"
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-"急切"
-"急剧"
-"急功"
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-"怦"
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-"怨气"
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-"怩"
-"怪"
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-"怪异"
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-"怫"
-"怯"
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-"总会"
-"总体"
-"总体规划"
-"总值"
-"总公司"
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-"总冠军"
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-"数以"
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-"数十万"
-"数字"
-"数字化"
-"数学"
-"数年"
-"数据"
-"数据中心"
-"数据分析"
-"数据处理"
-"数据库"
-"数控"
-"数控机"
-"数日"
-"数月"
-"数次"
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-"数百万"
-"数目"
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-"数量"
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-"止步"
-"止渴"
-"止痛"
-"止盈"
-"止血"
-"止跌"
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-"武器"
-"武士"
-"武大"
-"武夷"
-"武夷山"
-"武学"
-"武将"
-"武帝"
-"武当"
-"武昌"
-"武术"
-"武林"
-"武汉"
-"武汉大学"
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-"浪"
-"浪漫"
-"浪漫主义"
-"浪潮"
-"浪花"
-"浪费"
-"浪费时间"
-"浮"
-"浮云"
-"浮出"
-"浮动"
-"浮夸"
-"浮游"
-"浮现"
-"浮肿"
-"浮躁"
-"浮雕"
-"浯"
-"浴"
-"浴室"
-"浴缸"
-"海"
-"海上"
-"海事"
-"海伦"
-"海关"
-"海内"
-"海内外"
-"海军"
-"海南"
-"海南省"
-"海参"
-"海口"
-"海啸"
-"海域"
-"海外"
-"海宁"
-"海尔"
-"海岛"
-"海岸"
-"海峡"
-"海峡两岸"
-"海带"
-"海底"
-"海归"
-"海战"
-"海报"
-"海拔"
-"海景"
-"海棠"
-"海水"
-"海洋"
-"海浪"
-"海涛"
-"海淀"
-"海淀区"
-"海淘"
-"海湾"
-"海滨"
-"海滩"
-"海盗"
-"海绵"
-"海藻"
-"海豚"
-"海贼"
-"海边"
-"海运"
-"海里"
-"海量"
-"海面"
-"海风"
-"海马"
-"海鲜"
-"浸"
-"浸泡"
-"浸润"
-"涂"
-"涂上"
-"涂层"
-"涂抹"
-"涂改"
-"涂料"
-"涂装"
-"涂鸦"
-"涅"
-"消"
-"消化"
-"消化不良"
-"消化吸收"
-"消化系统"
-"消化道"
-"消失"
-"消息"
-"消息面"
-"消散"
-"消极"
-"消毒"
-"消灭"
-"消炎"
-"消瘦"
-"消磨"
-"消耗"
-"消肿"
-"消融"
-"消费"
-"消费品"
-"消费市场"
-"消费者"
-"消退"
-"消逝"
-"消遣"
-"消防"
-"消防员"
-"消防安全"
-"消除"
-"消食"
-"涉"
-"涉及"
-"涉外"
-"涉嫌"
-"涉案"
-"涉足"
-"涌"
-"涌入"
-"涌动"
-"涌泉"
-"涌现"
-"涎"
-"涑"
-"涓"
-"涔"
-"涕"
-"涛"
-"涝"
-"涞"
-"涟"
-"涟漪"
-"涠"
-"涡"
-"涡轮"
-"涣"
-"涤"
-"润"
-"润发"
-"润泽"
-"润滑"
-"润滑油"
-"润肠"
-"润肤"
-"润肺"
-"涧"
-"涨"
-"涨价"
-"涨停"
-"涨停板"
-"涨势"
-"涨幅"
-"涨跌"
-"涩"
-"涪"
-"涫"
-"涮"
-"涯"
-"液"
-"液体"
-"液化"
-"液压"
-"液态"
-"液晶"
-"液晶屏"
-"涵"
-"涵养"
-"涵盖"
-"涸"
-"涿"
-"淀"
-"淀粉"
-"淄"
-"淄博"
-"淅"
-"淆"
-"淇"
-"淋"
-"淋巴"
-"淋巴结"
-"淋浴"
-"淋漓"
-"淋漓尽致"
-"淌"
-"淑"
-"淑女"
-"淖"
-"淘"
-"淘宝"
-"淘宝网"
-"淘气"
-"淘汰"
-"淙"
-"淝"
-"淞"
-"淠"
-"淡"
-"淡化"
-"淡季"
-"淡定"
-"淡水"
-"淡泊"
-"淡淡"
-"淡淡的"
-"淡然"
-"淡雅"
-"淡黄"
-"淤"
-"淦"
-"淫"
-"淫秽"
-"淬"
-"淬火"
-"淮"
-"淮北"
-"淮南"
-"淮安"
-"淮河"
-"淮海"
-"深"
-"深信"
-"深入"
-"深入人心"
-"深入开展"
-"深入研究"
-"深切"
-"深刻"
-"深化"
-"深化改革"
-"深厚"
-"深受"
-"深呼吸"
-"深圳"
-"深圳市"
-"深处"
-"深夜"
-"深奥"
-"深层"
-"深层次"
-"深山"
-"深度"
-"深得"
-"深思"
-"深情"
-"深感"
-"深有"
-"深水"
-"深沉"
-"深沟"
-"深浅"
-"深海"
-"深深"
-"深深地"
-"深渊"
-"深爱"
-"深知"
-"深耕"
-"深色"
-"深蓝"
-"深藏"
-"深远"
-"深造"
-"深邃"
-"深陷"
-"淳"
-"淳朴"
-"混"
-"混乱"
-"混凝"
-"混凝土"
-"混动"
-"混合"
-"混日"
-"混杂"
-"混沌"
-"混淆"
-"混混"
-"混血"
-"混迹"
-"混音"
-"淹"
-"淹没"
-"添"
-"添加"
-"添加剂"
-"添花"
-"淼"
-"清"
-"清仓"
-"清代"
-"清净"
-"清凉"
-"清北"
-"清华"
-"清华大学"
-"清单"
-"清扫"
-"清新"
-"清明"
-"清明节"
-"清晨"
-"清晰"
-"清晰度"
-"清朝"
-"清末"
-"清楚"
-"清气"
-"清水"
-"清汤"
-"清河"
-"清泉"
-"清洁"
-"清洗"
-"清淡"
-"清清"
-"清清楚楚"
-"清澈"
-"清热"
-"清热解"
-"清热解毒"
-"清爽"
-"清理"
-"清白"
-"清真"
-"清秀"
-"清算"
-"清纯"
-"清脆"
-"清蒸"
-"清远"
-"清醒"
-"清闲"
-"清除"
-"清静"
-"清风"
-"清香"
-"清高"
-"渊"
-"渊明"
-"渊源"
-"渌"
-"渍"
-"渎"
-"渐"
-"渐变"
-"渐渐"
-"渐进"
-"渑"
-"渔"
-"渔业"
-"渔民"
-"渔船"
-"渗"
-"渗入"
-"渗出"
-"渗透"
-"渗透到"
-"渚"
-"渝"
-"渠"
-"渠道"
-"渡"
-"渡口"
-"渡江"
-"渡河"
-"渡边"
-"渡过"
-"渣"
-"渤"
-"渤海"
-"渥"
-"温"
-"温和"
-"温哥"
-"温婉"
-"温室"
-"温州"
-"温差"
-"温带"
-"温度"
-"温开水"
-"温情"
-"温控"
-"温暖"
-"温柔"
-"温水"
-"温泉"
-"温润"
-"温热"
-"温饱"
-"温馨"
-"渫"
-"渭"
-"港"
-"港元"
-"港口"
-"港台"
-"港币"
-"港湾"
-"港澳"
-"港澳台"
-"港股"
-"渲"
-"渲染"
-"渴"
-"渴望"
-"游"
-"游乐"
-"游人"
-"游侠"
-"游击"
-"游击队"
-"游刃"
-"游客"
-"游戏"
-"游戏规则"
-"游泳"
-"游泳池"
-"游牧"
-"游玩"
-"游离"
-"游艇"
-"游荡"
-"游行"
-"游览"
-"游记"
-"游走"
-"渺"
-"渺小"
-"渺茫"
-"湃"
-"湄"
-"湄公"
-"湉"
-"湍"
-"湎"
-"湔"
-"湖"
-"湖人"
-"湖人队"
-"湖北"
-"湖北省"
-"湖区"
-"湖南"
-"湖南卫视"
-"湖南省"
-"湖州"
-"湖水"
-"湖泊"
-"湖畔"
-"湖边"
-"湖面"
-"湘"
-"湘潭"
-"湘西"
-"湛"
-"湛江"
-"湜"
-"湟"
-"湫"
-"湮"
-"湲"
-"湾"
-"湿"
-"湿地"
-"湿度"
-"湿气"
-"湿润"
-"湿热"
-"湿疣"
-"湿疹"
-"溃"
-"溃疡"
-"溅"
-"溆"
-"溉"
-"溏"
-"源"
-"源于"
-"源头"
-"源性"
-"源氏"
-"源泉"
-"源源"
-"源源不断"
-"源码"
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-"源远"
-"準"
-"溘"
-"溜"
-"溜溜"
-"溜达"
-"溟"
-"溢"
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-"溢出"
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-"溧"
-"溪"
-"溪水"
-"溪流"
-"溯"
-"溯源"
-"溱"
-"溲"
-"溴"
-"溶"
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-"溶剂"
-"溶性"
-"溶洞"
-"溶液"
-"溶血"
-"溶解"
-"溷"
-"溺"
-"溺水"
-"溺爱"
-"溻"
-"溽"
-"滁"
-"滂"
-"滃"
-"滇"
-"滋"
-"滋养"
-"滋味"
-"滋润"
-"滋生"
-"滋补"
-"滋阴"
-"滏"
-"滑"
-"滑冰"
-"滑动"
-"滑坡"
-"滑板"
-"滑稽"
-"滑落"
-"滑行"
-"滑雪"
-"滓"
-"滔"
-"滔滔"
-"滕"
-"滗"
-"滚"
-"滚动"
-"滚滚"
-"滚筒"
-"滞"
-"滞后"
-"滞留"
-"滟"
-"满"
-"满分"
-"满嘴"
-"满地"
-"满天"
-"满心"
-"满怀"
-"满意"
-"满意度"
-"满族"
-"满是"
-"满月"
-"满洲"
-"满清"
-"满满"
-"满满的"
-"满目"
-"满脑"
-"满脸"
-"满足"
-"满身"
-"满载"
-"满面"
-"滢"
-"滤"
-"滤波"
-"滤芯"
-"滤过"
-"滤镜"
-"滥"
-"滥用"
-"滦"
-"滨"
-"滨州"
-"滨江"
-"滨海"
-"滩"
-"滪"
-"滫"
-"滴"
-"滴定"
-"滴水"
-"滴滴"
-"滹"
-"滿"
-"漂"
-"漂亮"
-"漂泊"
-"漂流"
-"漂浮"
-"漂白"
-"漂移"
-"漆"
-"漆黑"
-"漉"
-"漏"
-"漏斗"
-"漏水"
-"漏洞"
-"漓"
-"演"
-"演义"
-"演习"
-"演出"
-"演化"
-"演变"
-"演员"
-"演唱"
-"演唱会"
-"演奏"
-"演戏"
-"演技"
-"演示"
-"演练"
-"演绎"
-"演职"
-"演艺"
-"演艺圈"
-"演讲"
-"演说"
-"演进"
-"漕"
-"漠"
-"漢"
-"漩"
-"漩涡"
-"漪"
-"漫"
-"漫天"
-"漫威"
-"漫步"
-"漫游"
-"漫漫"
-"漫画"
-"漫长"
-"漭"
-"漯"
-"漱"
-"漱口"
-"漳"
-"漳州"
-"漶"
-"漾"
-"潇"
-"潇洒"
-"潋"
-"潍"
-"潍坊"
-"潘"
-"潜"
-"潜伏"
-"潜入"
-"潜力"
-"潜在"
-"潜心"
-"潜意识"
-"潜水"
-"潜移"
-"潜移默化"
-"潜能"
-"潜艇"
-"潜规则"
-"潞"
-"潟"
-"潢"
-"潦"
-"潭"
-"潮"
-"潮州"
-"潮汐"
-"潮汕"
-"潮流"
-"潮湿"
-"潲"
-"潴"
-"潸"
-"潺"
-"潼"
-"澄"
-"澄清"
-"澈"
-"澉"
-"澌"
-"澍"
-"澎"
-"澎湃"
-"澜"
-"澡"
-"澡堂"
-"澥"
-"澧"
-"澳"
-"澳元"
-"澳大利亚"
-"澳洲"
-"澳门"
-"澶"
-"澹"
-"激"
-"激光"
-"激动"
-"激励"
-"激发"
-"激情"
-"激活"
-"激烈"
-"激素"
-"激起"
-"激进"
-"濂"
-"濉"
-"濑"
-"濒"
-"濒临"
-"濒危"
-"濛"
-"濞"
-"濟"
-"濠"
-"濡"
-"濮"
-"濮阳"
-"濯"
-"瀑"
-"瀑布"
-"瀚"
-"瀛"
-"瀣"
-"瀬"
-"灌"
-"灌木"
-"灌水"
-"灌注"
-"灌溉"
-"灌篮"
-"灌装"
-"灌输"
-"灌顶"
-"灏"
-"灞"
-"灣"
-"火"
-"火候"
-"火力"
-"火山"
-"火影"
-"火星"
-"火柴"
-"火火"
-"火灾"
-"火炬"
-"火烧"
-"火热"
-"火焰"
-"火爆"
-"火箭"
-"火箭队"
-"火腿"
-"火花"
-"火药"
-"火车"
-"火车票"
-"火车站"
-"火锅"
-"火龙"
-"灬"
-"灭"
-"灭亡"
-"灭火"
-"灭绝"
-"灭菌"
-"灯"
-"灯光"
-"灯具"
-"灯塔"
-"灯泡"
-"灯火"
-"灯笼"
-"灯饰"
-"灰"
-"灰尘"
-"灰暗"
-"灰白"
-"灰色"
-"灰褐"
-"灵"
-"灵光"
-"灵动"
-"灵山"
-"灵异"
-"灵性"
-"灵感"
-"灵敏"
-"灵气"
-"灵活"
-"灵活性"
-"灵芝"
-"灵魂"
-"灶"
-"灸"
-"灼"
-"灾"
-"灾区"
-"灾害"
-"灾难"
-"灿"
-"灿烂"
-"炀"
-"炅"
-"炉"
-"炉石"
-"炊"
-"炎"
-"炎帝"
-"炎炎"
-"炎热"
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-"炒"
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-"炒菜"
-"炒锅"
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-"炔"
-"炕"
-"炖"
-"炘"
-"炙"
-"炜"
-"炝"
-"炫"
-"炫富"
-"炫耀"
-"炬"
-"炭"
-"炮"
-"炮儿"
-"炮兵"
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-"炮台"
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-"炮火"
-"炯"
-"炱"
-"炳"
-"炷"
-"炸"
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-"炸药"
-"炸鸡"
-"点"
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-"点位"
-"点儿"
-"点击"
-"点到"
-"点半"
-"点名"
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-"点头"
-"点子"
-"点心"
-"点数"
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-"点火"
-"点点"
-"点点头"
-"点点滴滴"
-"点燃"
-"点球"
-"点睛"
-"点缀"
-"点评"
-"点赞"
-"点钟"
-"為"
-"炻"
-"炼"
-"炼成"
-"炼油"
-"炽"
-"烀"
-"烁"
-"烂"
-"烃"
-"烈"
-"烈士"
-"烈日"
-"烈火"
-"烈烈"
-"烊"
-"烘"
-"烘干"
-"烘托"
-"烘烤"
-"烘焙"
-"烙"
-"烛"
-"烜"
-"烟"
-"烟台"
-"烟囱"
-"烟火"
-"烟花"
-"烟草"
-"烟酒"
-"烟雨"
-"烟雾"
-"烤"
-"烤箱"
-"烤肉"
-"烦"
-"烦恼"
-"烦躁"
-"烧"
-"烧伤"
-"烧开"
-"烧烤"
-"烧热"
-"烧结"
-"烧饼"
-"烨"
-"烩"
-"烫"
-"烫伤"
-"烬"
-"热"
-"热力"
-"热力学"
-"热处理"
-"热巴"
-"热带"
-"热度"
-"热心"
-"热忱"
-"热情"
-"热播"
-"热敷"
-"热气"
-"热水"
-"热水器"
-"热潮"
-"热火"
-"热点"
-"热烈"
-"热爱"
-"热电"
-"热线"
-"热能"
-"热血"
-"热衷"
-"热衷于"
-"热议"
-"热身"
-"热量"
-"热销"
-"热门"
-"热闹"
-"烯"
-"烷"
-"烹"
-"烹调"
-"烹饪"
-"烽"
-"烽火"
-"焉"
-"焊"
-"焊接"
-"焐"
-"焓"
-"焕"
-"焕发"
-"焕然"
-"焖"
-"焗"
-"焘"
-"焙"
-"焚"
-"焚烧"
-"焜"
-"無"
-"焦"
-"焦作"
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-"焦点"
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-"焦躁"
-"焯"
-"焰"
-"焱"
-"然"
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-"然而"
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-"煌"
-"煎"
-"煎服"
-"煎熬"
-"煎饼"
-"煜"
-"煞"
-"煤"
-"煤气"
-"煤炭"
-"煤矿"
-"煦"
-"照"
-"照亮"
-"照射"
-"照常"
-"照搬"
-"照料"
-"照明"
-"照样"
-"照片"
-"照相"
-"照着"
-"照耀"
-"照镜"
-"照顾"
-"煨"
-"煮"
-"煮开"
-"煮沸"
-"煮熟"
-"煮粥"
-"煲"
-"煳"
-"煸"
-"煸炒"
-"煺"
-"煽"
-"熄"
-"熄火"
-"熄灭"
-"熄灯"
-"熊"
-"熊市"
-"熊猫"
-"熏"
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-"熔"
-"熔断"
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-"熙"
-"熜"
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-"熠"
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-"熬"
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-"燃料"
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-"燃油"
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-"燕"
-"燕京"
-"燕子"
-"燕山"
-"燕窝"
-"燕麦"
-"燠"
-"燥"
-"燧"
-"燮"
-"燹"
-"爆"
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-"爆棚"
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-"爆炸"
-"爆照"
-"爆破"
-"爆竹"
-"爆笑"
-"爆米"
-"爆表"
-"爆香"
-"爝"
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-"爪"
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-"爬行"
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-"爱奇艺"
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-"爱德华"
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-"牤"
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-"献身"
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-"率达"
-"率领"
-"玉"
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-"玉器"
-"玉林"
-"玉树"
-"玉皇"
-"玉石"
-"玉米"
-"玉龙"
-"王"
-"王八"
-"王国"
-"王妃"
-"王子"
-"王宝强"
-"王室"
-"王家"
-"王府"
-"王思聪"
-"王朝"
-"王母"
-"王氏"
-"王爷"
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-"王维"
-"王羲"
-"王老师"
-"王者"
-"王莽"
-"王道"
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-"玕"
-"玖"
-"玙"
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-"环绕"
-"环节"
-"环路"
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-"玲"
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-"玳"
-"玷"
-"玺"
-"玻"
-"玻璃"
-"玻璃钢"
-"珀"
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-"珅"
-"珈"
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-"画家"
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-"界"
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-"畔"
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-"留存"
-"留学"
-"留学生"
-"留守"
-"留心"
-"留念"
-"留恋"
-"留情"
-"留意"
-"留有"
-"留校"
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-"留给"
-"留言"
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-"畜"
-"畜牧"
-"畜牧业"
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-"略"
-"略带"
-"略微"
-"略显"
-"略有"
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-"畸形"
-"畹"
-"畿"
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-"疆"
-"疏"
-"疏导"
-"疏忽"
-"疏散"
-"疏松"
-"疏远"
-"疏通"
-"疑"
-"疑似"
-"疑惑"
-"疑虑"
-"疑问"
-"疑难"
-"疒"
-"疔"
-"疖"
-"疗"
-"疗养"
-"疗效"
-"疗法"
-"疗程"
-"疙"
-"疙瘩"
-"疚"
-"疝"
-"疟"
-"疠"
-"疡"
-"疣"
-"疤"
-"疤痕"
-"疥"
-"疫"
-"疫情"
-"疫病"
-"疫苗"
-"疬"
-"疭"
-"疮"
-"疯"
-"疯子"
-"疯狂"
-"疱"
-"疱疹"
-"疲"
-"疲乏"
-"疲倦"
-"疲劳"
-"疲惫"
-"疲软"
-"疳"
-"疴"
-"疵"
-"疸"
-"疹"
-"疼"
-"疼爱"
-"疼痛"
-"疽"
-"疾"
-"疾患"
-"疾控"
-"疾病"
-"痂"
-"病"
-"病人"
-"病例"
-"病历"
-"病原"
-"病变"
-"病史"
-"病因"
-"病害"
-"病床"
-"病态"
-"病患"
-"病情"
-"病房"
-"病死"
-"病毒"
-"病灶"
-"病理"
-"病理学"
-"病症"
-"病痛"
-"病程"
-"病菌"
-"病虫"
-"病虫害"
-"症"
-"症状"
-"痈"
-"痉"
-"痉挛"
-"痊"
-"痊愈"
-"痍"
-"痒"
-"痔"
-"痔疮"
-"痕"
-"痕迹"
-"痘"
-"痘痘"
-"痛"
-"痛哭"
-"痛心"
-"痛快"
-"痛点"
-"痛经"
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-"痛风"
-"痞"
-"痢"
-"痣"
-"痤"
-"痤疮"
-"痦"
-"痧"
-"痨"
-"痪"
-"痫"
-"痰"
-"痱"
-"痴"
-"痴呆"
-"痴情"
-"痴迷"
-"痹"
-"痼"
-"痿"
-"瘀"
-"瘁"
-"瘃"
-"瘆"
-"瘊"
-"瘌"
-"瘐"
-"瘘"
-"瘙"
-"瘙痒"
-"瘛"
-"瘟"
-"瘟疫"
-"瘠"
-"瘢"
-"瘤"
-"瘦"
-"瘦下"
-"瘦弱"
-"瘦肉"
-"瘦身"
-"瘩"
-"瘪"
-"瘫"
-"瘫痪"
-"瘰"
-"瘳"
-"瘴"
-"瘵"
-"瘸"
-"瘼"
-"瘾"
-"瘿"
-"癃"
-"癌"
-"癌症"
-"癌细胞"
-"癍"
-"癔"
-"癖"
-"癜"
-"癞"
-"癣"
-"癫"
-"癫痫"
-"癫痫病"
-"癯"
-"癸"
-"発"
-"登"
-"登上"
-"登台"
-"登场"
-"登山"
-"登录"
-"登机"
-"登记"
-"登陆"
-"登高"
-"發"
-"白"
-"白了"
-"白云"
-"白人"
-"白俄罗斯"
-"白兔"
-"白内障"
-"白发"
-"白天"
-"白头"
-"白宫"
-"白富"
-"白居"
-"白居易"
-"白山"
-"白带"
-"白开水"
-"白斑"
-"白族"
-"白日"
-"白术"
-"白沙"
-"白玉"
-"白痴"
-"白癜"
-"白癜风"
-"白白"
-"白皙"
-"白眼"
-"白石"
-"白砂"
-"白糖"
-"白纸"
-"白细胞"
-"白羊"
-"白色"
-"白芍"
-"白花"
-"白莲"
-"白菜"
-"白萝卜"
-"白虎"
-"白血"
-"白血病"
-"白衣"
-"白酒"
-"白醋"
-"白金"
-"白银"
-"白雪"
-"白领"
-"白马"
-"白骨"
-"百事"
-"百八十"
-"百合"
-"百姓"
-"百家"
-"百岁"
-"百川"
-"百度"
-"百强"
-"百态"
-"百搭"
-"百病"
-"百科"
-"百科全书"
-"百般"
-"百花"
-"百草"
-"百计"
-"百货"
-"皂"
-"的"
-"的是"
-"的确"
-"的话"
-"皆"
-"皆是"
-"皆知"
-"皇"
-"皇上"
-"皇冠"
-"皇后"
-"皇太"
-"皇子"
-"皇室"
-"皇宫"
-"皇家"
-"皇帝"
-"皇族"
-"皇马"
-"皈"
-"皈依"
-"皋"
-"皎"
-"皑"
-"皓"
-"皖"
-"皙"
-"皤"
-"皮"
-"皮下"
-"皮书"
-"皮具"
-"皮卡"
-"皮尔"
-"皮带"
-"皮毛"
-"皮炎"
-"皮肤"
-"皮肤病"
-"皮肤科"
-"皮脂"
-"皮质"
-"皮革"
-"皮鞋"
-"皱"
-"皱纹"
-"皲"
-"皴"
-"皿"
-"盂"
-"盅"
-"盆"
-"盆友"
-"盆地"
-"盆景"
-"盆栽"
-"盆腔"
-"盆腔炎"
-"盈"
-"盈亏"
-"盈利"
-"盈盈"
-"盉"
-"益"
-"益匪"
-"益处"
-"益寿"
-"益智"
-"益气"
-"益生"
-"益生菌"
-"益阳"
-"盍"
-"盎"
-"盎司"
-"盎然"
-"盏"
-"盐"
-"盐城"
-"盐水"
-"盐酸"
-"监"
-"监事"
-"监会"
-"监听"
-"监委"
-"监察"
-"监局"
-"监护"
-"监护人"
-"监控"
-"监测"
-"监狱"
-"监理"
-"监督"
-"监督管理"
-"监管"
-"监管局"
-"监管部门"
-"监考"
-"监视"
-"盒"
-"盒子"
-"盔"
-"盖"
-"盖上"
-"盖子"
-"盖章"
-"盖茨"
-"盗"
-"盗墓"
-"盗版"
-"盗窃"
-"盗贼"
-"盘"
-"盘中"
-"盘古"
-"盘子"
-"盘整"
-"盘旋"
-"盘点"
-"盘面"
-"盛"
-"盛世"
-"盛会"
-"盛典"
-"盛名"
-"盛夏"
-"盛大"
-"盛宴"
-"盛开"
-"盛行"
-"盟"
-"盟军"
-"盟友"
-"盥"
-"目"
-"目光"
-"目击"
-"目前"
-"目前为止"
-"目录"
-"目标"
-"目测"
-"目的"
-"目的地"
-"目睹"
-"盯"
-"盱"
-"盲"
-"盲人"
-"盲目"
-"直"
-"直击"
-"直到"
-"直到现在"
-"直升"
-"直升机"
-"直奔"
-"直属"
-"直径"
-"直接"
-"直播"
-"直播间"
-"直板"
-"直流"
-"直流电"
-"直男癌"
-"直立"
-"直系"
-"直线"
-"直肠"
-"直至"
-"直营"
-"直观"
-"直觉"
-"直角"
-"直言"
-"直辖"
-"直辖市"
-"直达"
-"直通"
-"直通车"
-"直销"
-"直隶"
-"直面"
-"相"
-"相互"
-"相互作用"
-"相交"
-"相亲"
-"相传"
-"相伴"
-"相似"
-"相位"
-"相依"
-"相信"
-"相克"
-"相关"
-"相关性"
-"相册"
-"相助"
-"相反"
-"相合"
-"相同"
-"相声"
-"相处"
-"相容"
-"相对"
-"相对来说"
-"相对论"
-"相差"
-"相干"
-"相应"
-"相当"
-"相当于"
-"相思"
-"相恋"
-"相成"
-"相接"
-"相提"
-"相望"
-"相机"
-"相比"
-"相比之下"
-"相爱"
-"相片"
-"相看"
-"相知"
-"相符"
-"相等"
-"相约"
-"相结合"
-"相继"
-"相聚"
-"相见"
-"相识"
-"相貌"
-"相距"
-"相较"
-"相辅"
-"相辅相成"
-"相近"
-"相连"
-"相适应"
-"相通"
-"相逢"
-"相遇"
-"相邻"
-"相间"
-"相随"
-"相隔"
-"盹"
-"盼"
-"盼望"
-"盾"
-"省"
-"省事"
-"省份"
-"省会"
-"省内"
-"省力"
-"省去"
-"省委"
-"省市"
-"省心"
-"省政府"
-"省油"
-"省略"
-"省级"
-"省道"
-"省部"
-"省钱"
-"省长"
-"眄"
-"眇"
-"眈"
-"眉"
-"眉头"
-"眉毛"
-"眊"
-"看"
-"看一看"
-"看上"
-"看上去"
-"看不"
-"看不上"
-"看不出"
-"看不出来"
-"看不到"
-"看不懂"
-"看不清"
-"看不见"
-"看不起"
-"看中"
-"看书"
-"看似"
-"看作"
-"看做"
-"看出"
-"看到"
-"看吧"
-"看多"
-"看好"
-"看守"
-"看待"
-"看得"
-"看得出"
-"看得出来"
-"看得见"
-"看成"
-"看房"
-"看望"
-"看来"
-"看法"
-"看涨"
-"看清"
-"看清楚"
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-"看电视"
-"看病"
-"看看"
-"看着"
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-"看过"
-"看透"
-"看重"
-"県"
-"眍"
-"眙"
-"真"
-"真主"
-"真事"
-"真人"
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-"真切"
-"真实"
-"真实性"
-"真心"
-"真情"
-"真想"
-"真挚"
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-"真是太"
-"真有"
-"真正"
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-"真皮"
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-"真真"
-"真空"
-"真能"
-"真菌"
-"真言"
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-"真谛"
-"真题"
-"眠"
-"眦"
-"眨"
-"眨眼"
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-"眬"
-"眭"
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-"眵"
-"眶"
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-"眷属"
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-"眼光"
-"眼前"
-"眼前一亮"
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-"眼圈"
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-"眼皮"
-"眼看"
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-"眼霜"
-"眾"
-"着"
-"着力"
-"着头"
-"着实"
-"着急"
-"着想"
-"着手"
-"着火"
-"着眼"
-"着眼于"
-"着色"
-"着装"
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-"着重"
-"着陆"
-"睁"
-"睁开"
-"睁开眼"
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-"睃"
-"睄"
-"睇"
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-"睛"
-"睡"
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-"睡不着"
-"睡前"
-"睡得"
-"睡眠"
-"睡眠不足"
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-"睡觉时"
-"睢"
-"督"
-"督促"
-"督办"
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-"督查"
-"睥"
-"睦"
-"睨"
-"睫"
-"睫毛"
-"睬"
-"睹"
-"睽"
-"睾"
-"睾丸"
-"睿"
-"睿智"
-"瞀"
-"瞄"
-"瞄准"
-"瞅"
-"瞋"
-"瞌"
-"瞌睡"
-"瞎"
-"瞑"
-"瞒"
-"瞟"
-"瞠"
-"瞢"
-"瞥"
-"瞧"
-"瞧不起"
-"瞩"
-"瞩目"
-"瞪"
-"瞬"
-"瞬时"
-"瞬间"
-"瞭"
-"瞰"
-"瞳"
-"瞳孔"
-"瞻"
-"瞽"
-"瞿"
-"矍"
-"矗"
-"矛"
-"矛盾"
-"矜"
-"矢"
-"矢量"
-"矣"
-"知"
-"知乎"
-"知名"
-"知名企业"
-"知名品牌"
-"知名度"
-"知己"
-"知心"
-"知性"
-"知情"
-"知晓"
-"知觉"
-"知识"
-"知识产权"
-"知识分子"
-"知识点"
-"知足"
-"知道"
-"知青"
-"知音"
-"矩"
-"矩形"
-"矩阵"
-"矫"
-"矫情"
-"矫正"
-"矬"
-"短"
-"短信"
-"短发"
-"短小"
-"短时间"
-"短暂"
-"短期"
-"短期内"
-"短板"
-"短片"
-"短短"
-"短短的"
-"短篇"
-"短线"
-"短缺"
-"短袖"
-"短裙"
-"短裤"
-"短路"
-"矮"
-"石"
-"石刻"
-"石化"
-"石器"
-"石墨"
-"石头"
-"石子"
-"石家庄"
-"石材"
-"石板"
-"石柱"
-"石桥"
-"石榴"
-"石油"
-"石油化工"
-"石灰"
-"石狮"
-"石窟"
-"石膏"
-"石英"
-"石门"
-"石雕"
-"矶"
-"矸"
-"矽"
-"矾"
-"矿"
-"矿业"
-"矿井"
-"矿产"
-"矿产资源"
-"矿山"
-"矿工"
-"矿泉水"
-"矿物"
-"矿物质"
-"矿石"
-"砀"
-"码"
-"码农"
-"码头"
-"砂"
-"砂浆"
-"砂糖"
-"砂锅"
-"砌"
-"砍"
-"砒"
-"研"
-"研习"
-"研修"
-"研判"
-"研制"
-"研发"
-"研磨"
-"研究"
-"研究会"
-"研究员"
-"研究室"
-"研究成果"
-"研究所"
-"研究生"
-"研究者"
-"研究院"
-"研讨"
-"研讨会"
-"砖"
-"砖头"
-"砖木"
-"砗"
-"砘"
-"砚"
-"砜"
-"砝"
-"砟"
-"砣"
-"砥"
-"砧"
-"砭"
-"砰"
-"破"
-"破产"
-"破位"
-"破坏"
-"破损"
-"破旧"
-"破案"
-"破灭"
-"破烂"
-"破碎"
-"破裂"
-"破解"
-"破财"
-"破门"
-"破除"
-"砷"
-"砸"
-"砹"
-"砺"
-"砻"
-"砼"
-"砾"
-"础"
-"硅"
-"硅胶"
-"硅藻"
-"硅谷"
-"硅酸"
-"硇"
-"硌"
-"硎"
-"硐"
-"硒"
-"硕"
-"硕士"
-"硕士学位"
-"硕果"
-"硖"
-"硗"
-"硝"
-"硝烟"
-"硝酸"
-"硫"
-"硫化"
-"硫酸"
-"硬"
-"硬件"
-"硬化"
-"硬币"
-"硬度"
-"硬座"
-"硬性"
-"硬是"
-"硬朗"
-"硬盘"
-"硬笔"
-"硬质"
-"硭"
-"确"
-"确保"
-"确信"
-"确切"
-"确定"
-"确定性"
-"确实"
-"确有"
-"确确"
-"确立"
-"确认"
-"确诊"
-"硼"
-"碁"
-"碇"
-"碉"
-"碌"
-"碍"
-"碍于"
-"碎"
-"碎片"
-"碎石"
-"碑"
-"碑林"
-"碓"
-"碗"
-"碘"
-"碚"
-"碛"
-"碜"
-"碟"
-"碡"
-"碣"
-"碧"
-"碧桂园"
-"碰"
-"碰上"
-"碰到"
-"碰巧"
-"碰撞"
-"碰碰"
-"碱"
-"碱性"
-"碲"
-"碳"
-"碳化"
-"碳水"
-"碳水化合物"
-"碳纤维"
-"碳酸"
-"碴"
-"確"
-"碾"
-"碾压"
-"磁"
-"磁力"
-"磁场"
-"磁带"
-"磁性"
-"磁盘"
-"磁铁"
-"磅"
-"磅礴"
-"磊"
-"磋"
-"磐"
-"磔"
-"磕"
-"磙"
-"磨"
-"磨合"
-"磨损"
-"磨砂"
-"磨砺"
-"磨练"
-"磨难"
-"磬"
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-"磴"
-"磷"
-"磷脂"
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-"磺"
-"磺酸"
-"礁"
-"礅"
-"礓"
-"礴"
-"示"
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-"示意"
-"示意图"
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-"示范区"
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-"礼仪"
-"礼包"
-"礼品"
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-"礼节"
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-"社"
-"社交"
-"社交圈"
-"社会"
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-"社会化"
-"社会各界"
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-"社会工作"
-"社会治安"
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-"社会科学院"
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-"社群"
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-"祀"
-"祁"
-"祆"
-"祇"
-"祈"
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-"祈祷"
-"祈福"
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-"纪律"
-"纪念"
-"纪念日"
-"纪念碑"
-"纪念馆"
-"纪检"
-"纪检监察"
-"纪要"
-"纫"
-"纬"
-"纬度"
-"纭"
-"纯"
-"纯中药"
-"纯净"
-"纯天"
-"纯天然"
-"纯属"
-"纯度"
-"纯收入"
-"纯棉"
-"纯正"
-"纯洁"
-"纯真"
-"纯粹"
-"纰"
-"纱"
-"纱布"
-"纲"
-"纲上"
-"纲目"
-"纲要"
-"纲领"
-"纳"
-"纳什"
-"纳入"
-"纳德"
-"纳斯"
-"纳税"
-"纳税人"
-"纳米"
-"纳粹"
-"纳闷"
-"纴"
-"纵"
-"纵使"
-"纵向"
-"纵容"
-"纵横"
-"纵深"
-"纵然"
-"纵观"
-"纵队"
-"纶"
-"纷"
-"纷争"
-"纷呈"
-"纷纷"
-"纷纷表示"
-"纸"
-"纸上"
-"纸巾"
-"纸币"
-"纸张"
-"纸条"
-"纸牌"
-"纸盒"
-"纸箱"
-"纸质"
-"纹"
-"纹理"
-"纹路"
-"纹身"
-"纹饰"
-"纺"
-"纺织"
-"纽"
-"纽带"
-"纽约"
-"纾"
-"线"
-"线上"
-"线下"
-"线图"
-"线圈"
-"线形"
-"线性"
-"线条"
-"线段"
-"线程"
-"线索"
-"线路"
-"绀"
-"绁"
-"绂"
-"练"
-"练习"
-"练习曲"
-"练功"
-"练就"
-"练琴"
-"练练"
-"组"
-"组件"
-"组分"
-"组合"
-"组团"
-"组委"
-"组委会"
-"组建"
-"组成"
-"组成部分"
-"组组"
-"组织"
-"组织部"
-"组装"
-"组长"
-"组队"
-"绅"
-"绅士"
-"细"
-"细分"
-"细则"
-"细化"
-"细嫩"
-"细小"
-"细微"
-"细心"
-"细思"
-"细思极"
-"细末"
-"细砂糖"
-"细纹"
-"细细"
-"细胞"
-"细腻"
-"细致"
-"细节"
-"细菌"
-"细长"
-"细雨"
-"织"
-"织物"
-"织田"
-"终"
-"终于"
-"终将"
-"终归"
-"终日"
-"终极"
-"终止"
-"终点"
-"终生"
-"终究"
-"终端"
-"终结"
-"终老"
-"终身"
-"绉"
-"绊"
-"绌"
-"绍"
-"绍兴"
-"绎"
-"经"
-"经不起"
-"经久"
-"经典"
-"经办"
-"经历"
-"经受"
-"经商"
-"经常"
-"经常出现"
-"经常性"
-"经开"
-"经得起"
-"经文"
-"经期"
-"经济"
-"经济体"
-"经济体制"
-"经济区"
-"经济危机"
-"经济学"
-"经济学家"
-"经济性"
-"经济损失"
-"经济效益"
-"经济林"
-"经济法"
-"经济社会"
-"经理"
-"经理人"
-"经由"
-"经管"
-"经纪"
-"经纪人"
-"经纬"
-"经络"
-"经脉"
-"经营"
-"经营性"
-"经营权"
-"经营者"
-"经营范围"
-"经贸"
-"经费"
-"经过"
-"经销"
-"经销商"
-"经验"
-"经验丰富"
-"绑"
-"绑定"
-"绑架"
-"绒"
-"绒毛"
-"结"
-"结业"
-"结了"
-"结交"
-"结伴"
-"结印"
-"结合"
-"结婚"
-"结实"
-"结尾"
-"结局"
-"结成"
-"结晶"
-"结束"
-"结构"
-"结构性"
-"结构调整"
-"结果"
-"结果显示"
-"结核"
-"结盟"
-"结石"
-"结算"
-"结缘"
-"结肠"
-"结膜"
-"结节"
-"结论"
-"结识"
-"结语"
-"绔"
-"绕"
-"绕行"
-"绕过"
-"绗"
-"绘"
-"绘制"
-"绘图"
-"绘本"
-"绘画"
-"给"
-"给予"
-"给人以"
-"给付"
-"给出"
-"给力"
-"给我"
-"绚"
-"绚丽"
-"绚烂"
-"绛"
-"络"
-"绝"
-"绝不"
-"绝不会"
-"绝不能"
-"绝世"
-"绝伦"
-"绝佳"
-"绝地"
-"绝大多数"
-"绝大部分"
-"绝妙"
-"绝对"
-"绝技"
-"绝招"
-"绝无"
-"绝望"
-"绝缘"
-"绝育"
-"绝非"
-"绞"
-"绞痛"
-"统"
-"统一"
-"统帅"
-"统战"
-"统治"
-"统治者"
-"统称"
-"统筹"
-"统统"
-"统考"
-"统计"
-"统计学"
-"统计局"
-"统计数据"
-"统领"
-"绠"
-"绡"
-"绢"
-"绣"
-"绣花"
-"绥"
-"绦"
-"继"
-"继任"
-"继发"
-"继承"
-"继承人"
-"继电"
-"继续"
-"继而"
-"绨"
-"绩"
-"绩效"
-"绩点"
-"绪"
-"绫"
-"续"
-"续约"
-"续续"
-"续航"
-"绮"
-"绯"
-"绯闻"
-"绰"
-"绱"
-"绲"
-"绳"
-"绳子"
-"维"
-"维也纳"
-"维亚"
-"维他命"
-"维修"
-"维克"
-"维吾尔"
-"维基"
-"维基百"
-"维多"
-"维多利亚"
-"维奇"
-"维尔"
-"维度"
-"维护"
-"维拉"
-"维持"
-"维斯"
-"维新"
-"维权"
-"维特"
-"维生素"
-"维系"
-"绵"
-"绵延"
-"绵绵"
-"绵阳"
-"绶"
-"绷"
-"绸"
-"绺"
-"绻"
-"综"
-"综上"
-"综上所"
-"综上所述"
-"综合"
-"综合体"
-"综合征"
-"综合性"
-"综合治理"
-"综合症"
-"综艺"
-"综艺节目"
-"绽"
-"绽放"
-"绾"
-"绿"
-"绿化"
-"绿叶"
-"绿地"
-"绿树"
-"绿水"
-"绿洲"
-"绿灯"
-"绿绿"
-"绿色"
-"绿茶"
-"绿豆"
-"缀"
-"缁"
-"缂"
-"缃"
-"缄"
-"缅"
-"缅怀"
-"缅甸"
-"缆"
-"缇"
-"缈"
-"缉"
-"缌"
-"缎"
-"缑"
-"缒"
-"缓"
-"缓冲"
-"缓和"
-"缓存"
-"缓慢"
-"缓缓"
-"缓解"
-"缔"
-"缔约"
-"缔造"
-"缕"
-"编"
-"编为"
-"编入"
-"编写"
-"编制"
-"编剧"
-"编号"
-"编委"
-"编审"
-"编导"
-"编排"
-"编撰"
-"编曲"
-"编码"
-"编程"
-"编纂"
-"编织"
-"编者"
-"编著"
-"编译"
-"编辑"
-"编造"
-"缗"
-"缘"
-"缘分"
-"缘故"
-"缘由"
-"缘起"
-"缙"
-"缚"
-"缛"
-"缜"
-"缝"
-"缝合"
-"缝纫"
-"缝隙"
-"缟"
-"缠"
-"缠绕"
-"缠绵"
-"缡"
-"缢"
-"缣"
-"缤"
-"缤纷"
-"缥"
-"缦"
-"缧"
-"缨"
-"缩"
-"缩写"
-"缩减"
-"缩小"
-"缩影"
-"缩放"
-"缩水"
-"缩短"
-"缩量"
-"缪"
-"缫"
-"缬"
-"缭"
-"缭乱"
-"缭绕"
-"缮"
-"缯"
-"缰"
-"缱"
-"缲"
-"缳"
-"缴"
-"缴存"
-"缴纳"
-"缴费"
-"缵"
-"缶"
-"缸"
-"缺"
-"缺乏"
-"缺口"
-"缺失"
-"缺少"
-"缺席"
-"缺氧"
-"缺水"
-"缺点"
-"缺爱"
-"缺血"
-"缺陷"
-"罂"
-"罄"
-"罅"
-"罐"
-"罐头"
-"网"
-"网上"
-"网卡"
-"网友"
-"网吧"
-"网址"
-"网店"
-"网易"
-"网格"
-"网民"
-"网游"
-"网点"
-"网状"
-"网球"
-"网盘"
-"网站"
-"网红"
-"网约车"
-"网络"
-"网络安全"
-"网络小说"
-"网络平台"
-"网络游戏"
-"网络管理"
-"网络营销"
-"网络通信"
-"网购"
-"网贷"
-"网路"
-"网速"
-"网银"
-"网页"
-"罔"
-"罕"
-"罕见"
-"罗"
-"罗伯"
-"罗伯特"
-"罗兰"
-"罗列"
-"罗宾"
-"罗拉"
-"罗斯"
-"罗曼"
-"罗杰"
-"罗汉"
-"罗湖"
-"罗辑"
-"罗马"
-"罗马尼亚"
-"罘"
-"罚"
-"罚款"
-"罚球"
-"罟"
-"罡"
-"罢"
-"罢不"
-"罢了"
-"罢免"
-"罢工"
-"罨"
-"罩"
-"罪"
-"罪名"
-"罪恶"
-"罪犯"
-"罪行"
-"置"
-"置业"
-"置于"
-"置信"
-"置换"
-"置疑"
-"置身"
-"置身于"
-"罱"
-"署"
-"署名"
-"罴"
-"罹"
-"罽"
-"罾"
-"羁"
-"羁绊"
-"羅"
-"羊"
-"羊毛"
-"羊绒"
-"羊羊"
-"羊肉"
-"羌"
-"美"
-"美丽"
-"美人"
-"美元"
-"美军"
-"美利坚"
-"美剧"
-"美化"
-"美协"
-"美发"
-"美味"
-"美团"
-"美国"
-"美国政府"
-"美国纽约"
-"美女"
-"美好"
-"美好生活"
-"美妆"
-"美妙"
-"美学"
-"美容"
-"美容美发"
-"美容院"
-"美工"
-"美帝"
-"美式"
-"美德"
-"美感"
-"美方"
-"美景"
-"美术"
-"美术学院"
-"美术家"
-"美术馆"
-"美洲"
-"美满"
-"美甲"
-"美男"
-"美白"
-"美的"
-"美美"
-"美联储"
-"美股"
-"美观"
-"美誉"
-"美貌"
-"美酒"
-"美金"
-"美院"
-"美颜"
-"美食"
-"羑"
-"羔"
-"羚"
-"羞"
-"羞愧"
-"羞涩"
-"羞耻"
-"羞辱"
-"羟"
-"羡"
-"羡慕"
-"群"
-"群众"
-"群体"
-"群发"
-"群山"
-"群岛"
-"群星"
-"群落"
-"群里"
-"群雄"
-"羧"
-"義"
-"羯"
-"羰"
-"羲"
-"羸"
-"羹"
-"羼"
-"羽"
-"羽毛"
-"羽毛球"
-"羽状"
-"羽绒"
-"羽绒服"
-"羿"
-"翁"
-"翅"
-"翅膀"
-"翊"
-"翌"
-"翎"
-"習"
-"翔"
-"翕"
-"翘"
-"翚"
-"翟"
-"翠"
-"翠绿"
-"翡"
-"翡翠"
-"翥"
-"翦"
-"翩"
-"翩翩"
-"翮"
-"翰"
-"翰林"
-"翱"
-"翳"
-"翻"
-"翻倍"
-"翻出"
-"翻唱"
-"翻墙"
-"翻天"
-"翻开"
-"翻新"
-"翻滚"
-"翻炒"
-"翻看"
-"翻翻"
-"翻脸"
-"翻译"
-"翻身"
-"翻转"
-"翻过"
-"翻阅"
-"翼"
-"翼翼"
-"耀"
-"耀眼"
-"老"
-"老一辈"
-"老中医"
-"老乡"
-"老人"
-"老人家"
-"老伴"
-"老先"
-"老公"
-"老兵"
-"老化"
-"老去"
-"老友"
-"老君"
-"老和尚"
-"老外"
-"老大"
-"老天"
-"老太"
-"老太太"
-"老夫"
-"老头"
-"老奶奶"
-"老妈"
-"老娘"
-"老婆"
-"老子"
-"老字号"
-"老实"
-"老客户"
-"老家"
-"老将"
-"老少"
-"老师"
-"老干部"
-"老年"
-"老年人"
-"老总"
-"老抽"
-"老挝"
-"老旧"
-"老是"
-"老朋友"
-"老板"
-"老板娘"
-"老爷"
-"老爸"
-"老牌"
-"老王"
-"老百姓"
-"老罗"
-"老老"
-"老老实实"
-"老者"
-"老虎"
-"老街"
-"老鹰"
-"老鼠"
-"老龄"
-"老龄化"
-"考"
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-"考入"
-"考前"
-"考勤"
-"考取"
-"考古"
-"考古学"
-"考场"
-"考完"
-"考官"
-"考察"
-"考得"
-"考查"
-"考核"
-"考点"
-"考生"
-"考研"
-"考究"
-"考网"
-"考虑"
-"考证"
-"考评"
-"考试"
-"考试成绩"
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-"考量"
-"考题"
-"考验"
-"耄"
-"者"
-"耆"
-"耋"
-"而"
-"而上"
-"而下"
-"而不"
-"而且"
-"而为"
-"而今"
-"而入"
-"而出"
-"而后"
-"而复"
-"而外"
-"而定"
-"而已"
-"而异"
-"而成"
-"而是"
-"而来"
-"而然"
-"而生"
-"而知"
-"而立"
-"而至"
-"而行"
-"而言"
-"而言之"
-"而论"
-"而过"
-"耍"
-"耐"
-"耐久"
-"耐克"
-"耐力"
-"耐劳"
-"耐压"
-"耐受"
-"耐寒"
-"耐心"
-"耐火"
-"耐热"
-"耐用"
-"耐磨"
-"耐腐蚀"
-"耐药"
-"耒"
-"耕"
-"耕作"
-"耕地"
-"耕耘"
-"耖"
-"耗"
-"耗尽"
-"耗时"
-"耗材"
-"耗电"
-"耗费"
-"耘"
-"耙"
-"耜"
-"耠"
-"耢"
-"耥"
-"耦"
-"耦合"
-"耧"
-"耨"
-"耩"
-"耪"
-"耱"
-"耳"
-"耳塞"
-"耳朵"
-"耳机"
-"耳熟"
-"耳环"
-"耳目"
-"耳聋"
-"耳边"
-"耳鸣"
-"耳鼻"
-"耳鼻喉"
-"耶"
-"耶夫"
-"耶律"
-"耶稣"
-"耶鲁"
-"耷"
-"耸"
-"耻"
-"耻辱"
-"耽"
-"耽美"
-"耽误"
-"耿"
-"聂"
-"聃"
-"聆"
-"聆听"
-"聊"
-"聊城"
-"聊天"
-"聊斋"
-"聊聊"
-"聊起"
-"聋"
-"职"
-"职业"
-"职业技能"
-"职业生涯"
-"职业道德"
-"职位"
-"职务"
-"职员"
-"职场"
-"职工"
-"职权"
-"职称"
-"职能"
-"职能部门"
-"职责"
-"职高"
-"聒"
-"联"
-"联保"
-"联储"
-"联军"
-"联动"
-"联合"
-"联合会"
-"联合国"
-"联合国教科文组织"
-"联名"
-"联席"
-"联想"
-"联手"
-"联播"
-"联机"
-"联欢"
-"联盟"
-"联系"
-"联系人"
-"联系方"
-"联系方式"
-"联系电话"
-"联结"
-"联络"
-"联网"
-"联考"
-"联谊"
-"联谊会"
-"联赛"
-"联通"
-"联邦"
-"聖"
-"聘"
-"聘任"
-"聘用"
-"聘请"
-"聚"
-"聚乙烯"
-"聚会"
-"聚合"
-"聚在"
-"聚在一起"
-"聚居"
-"聚氨"
-"聚氨酯"
-"聚焦"
-"聚酯"
-"聚集"
-"聚餐"
-"聞"
-"聩"
-"聪"
-"聪慧"
-"聪明"
-"聪明人"
-"聯"
-"聱"
-"聲"
-"聽"
-"聿"
-"肃"
-"肄"
-"肆"
-"肆意"
-"肆无忌惮"
-"肇"
-"肇事"
-"肇庆"
-"肉"
-"肉丝"
-"肉体"
-"肉桂"
-"肉片"
-"肉眼"
-"肉类"
-"肉质"
-"肉食"
-"肉馅"
-"肋"
-"肋骨"
-"肌"
-"肌瘤"
-"肌肉"
-"肌肤"
-"肓"
-"肖"
-"肖像"
-"肘"
-"肚"
-"肚子"
-"肚皮"
-"肚脐"
-"肛"
-"肛肠"
-"肛门"
-"肝"
-"肝功能"
-"肝气"
-"肝火"
-"肝炎"
-"肝病"
-"肝癌"
-"肝硬"
-"肝硬化"
-"肝经"
-"肝肾"
-"肝胆"
-"肝脏"
-"肟"
-"肠"
-"肠胃"
-"肠道"
-"股"
-"股东"
-"股东大会"
-"股价"
-"股份"
-"股份制"
-"股市"
-"股指"
-"股本"
-"股权"
-"股民"
-"股票"
-"股票市场"
-"股骨"
-"股骨头"
-"肢"
-"肢体"
-"肤"
-"肤浅"
-"肤色"
-"肤质"
-"肥"
-"肥厚"
-"肥大"
-"肥料"
-"肥沃"
-"肥皂"
-"肥肉"
-"肥胖"
-"肩"
-"肩上"
-"肩膀"
-"肩负"
-"肩部"
-"肪"
-"肫"
-"肮"
-"肯"
-"肯定"
-"肯尼迪"
-"肯德"
-"肯德基"
-"肱"
-"育"
-"育人"
-"育儿"
-"育才"
-"育种"
-"肴"
-"肺"
-"肺气"
-"肺炎"
-"肺癌"
-"肺结核"
-"肺腑"
-"肺部"
-"肼"
-"肽"
-"肾"
-"肾上"
-"肾上腺"
-"肾功能"
-"肾小"
-"肾气"
-"肾炎"
-"肾病"
-"肾脏"
-"肾虚"
-"肿"
-"肿块"
-"肿大"
-"肿痛"
-"肿瘤"
-"肿胀"
-"胀"
-"胀痛"
-"胁"
-"胂"
-"胃"
-"胃口"
-"胃溃疡"
-"胃炎"
-"胃病"
-"胃痛"
-"胃癌"
-"胃肠"
-"胃肠道"
-"胃部"
-"胃酸"
-"胄"
-"胆"
-"胆囊"
-"胆固醇"
-"胆子"
-"胆小"
-"胆怯"
-"胆汁"
-"胆碱"
-"胆结石"
-"背"
-"背上"
-"背书"
-"背光"
-"背包"
-"背叛"
-"背后"
-"背影"
-"背心"
-"背景"
-"背着"
-"背离"
-"背诵"
-"背负"
-"背部"
-"背锅"
-"背靠"
-"背面"
-"背驰"
-"胍"
-"胎"
-"胎儿"
-"胎盘"
-"胖"
-"胖子"
-"胖胖"
-"胗"
-"胙"
-"胚"
-"胚胎"
-"胛"
-"胜"
-"胜任"
-"胜出"
-"胜利"
-"胜地"
-"胜率"
-"胜负"
-"胜过"
-"胝"
-"胞"
-"胞胎"
-"胡"
-"胡乱"
-"胡同"
-"胡子"
-"胡椒"
-"胡椒粉"
-"胡歌"
-"胡萝卜"
-"胡萝卜素"
-"胡说"
-"胡适"
-"胡锦"
-"胡锦涛"
-"胤"
-"胥"
-"胧"
-"胨"
-"胪"
-"胫"
-"胬"
-"胭"
-"胯"
-"胰"
-"胰岛"
-"胰岛素"
-"胰腺"
-"胱"
-"胳"
-"胳膊"
-"胴"
-"胶"
-"胶东"
-"胶体"
-"胶原"
-"胶原蛋白"
-"胶囊"
-"胶带"
-"胶水"
-"胶片"
-"胶版"
-"胶粘"
-"胶质"
-"胸"
-"胸前"
-"胸口"
-"胸大"
-"胸怀"
-"胸腔"
-"胸部"
-"胸闷"
-"胺"
-"胼"
-"能"
-"能为"
-"能力"
-"能动"
-"能否"
-"能够"
-"能干"
-"能手"
-"能源"
-"能耐"
-"能耗"
-"能量"
-"脂"
-"脂肪"
-"脂肪肝"
-"脂肪酸"
-"脆"
-"脆弱"
-"脉"
-"脉冲"
-"脉搏"
-"脉络"
-"脊"
-"脊柱"
-"脊椎"
-"脊髓"
-"脍"
-"脍炙"
-"脏"
-"脏器"
-"脏腑"
-"脐"
-"脑"
-"脑中"
-"脑力"
-"脑子"
-"脑子里"
-"脑残"
-"脑洞"
-"脑海"
-"脑海中"
-"脑筋"
-"脑细胞"
-"脑膜"
-"脑血管"
-"脑补"
-"脑袋"
-"脑部"
-"脒"
-"脓"
-"脔"
-"脖"
-"脖子"
-"脘"
-"脚"
-"脚下"
-"脚印"
-"脚尖"
-"脚底"
-"脚本"
-"脚步"
-"脚气"
-"脚趾"
-"脚跟"
-"脚踏"
-"脚踏实地"
-"脚踝"
-"脬"
-"脯"
-"脱"
-"脱俗"
-"脱发"
-"脱口"
-"脱欧"
-"脱毛"
-"脱水"
-"脱硫"
-"脱离"
-"脱胎"
-"脱落"
-"脱衣"
-"脱贫"
-"脱颖"
-"脱颖而出"
-"脲"
-"脸"
-"脸上"
-"脸型"
-"脸庞"
-"脸皮"
-"脸红"
-"脸色"
-"脸蛋"
-"脸部"
-"脸颊"
-"脾"
-"脾气"
-"脾胃"
-"脾虚"
-"腆"
-"腈"
-"腊"
-"腊八"
-"腊月"
-"腊肉"
-"腋"
-"腋下"
-"腋臭"
-"腌"
-"腌制"
-"腐"
-"腐女"
-"腐朽"
-"腐烂"
-"腐蚀"
-"腐蚀性"
-"腐败"
-"腑"
-"腓"
-"腔"
-"腕"
-"腚"
-"腠"
-"腥"
-"腥味"
-"腦"
-"腧"
-"腩"
-"腭"
-"腮"
-"腰"
-"腰带"
-"腰椎"
-"腰椎间盘"
-"腰痛"
-"腰线"
-"腰部"
-"腰酸"
-"腱"
-"腴"
-"腹"
-"腹中"
-"腹泻"
-"腹痛"
-"腹肌"
-"腹胀"
-"腹腔"
-"腹膜"
-"腹部"
-"腺"
-"腻"
-"腼"
-"腾"
-"腾腾"
-"腾讯"
-"腾飞"
-"腿"
-"腿部"
-"膀"
-"膀胱"
-"膂"
-"膈"
-"膊"
-"膏"
-"膑"
-"膘"
-"膙"
-"膛"
-"膜"
-"膜炎"
-"膝"
-"膝关"
-"膝关节"
-"膝盖"
-"膦"
-"膨"
-"膨胀"
-"膳"
-"膳食"
-"膺"
-"膻"
-"臀"
-"臀部"
-"臂"
-"臃"
-"臆"
-"臉"
-"臊"
-"臌"
-"臜"
-"臣"
-"臧"
-"自"
-"自个"
-"自主"
-"自习"
-"自从"
-"自以为"
-"自以为是"
-"自传"
-"自体"
-"自作"
-"自保"
-"自信"
-"自信心"
-"自创"
-"自制"
-"自动"
-"自动化"
-"自助"
-"自助餐"
-"自卑"
-"自卫"
-"自发"
-"自古"
-"自古以来"
-"自在"
-"自大"
-"自如"
-"自娱"
-"自学"
-"自定义"
-"自家"
-"自尊"
-"自尊心"
-"自己"
-"自已"
-"自带"
-"自干"
-"自幼"
-"自弃"
-"自强"
-"自律"
-"自得"
-"自性"
-"自恋"
-"自愈"
-"自愿"
-"自慰"
-"自成"
-"自我"
-"自我介绍"
-"自拍"
-"自拔"
-"自控"
-"自救"
-"自暴"
-"自有"
-"自杀"
-"自来"
-"自来水"
-"自查"
-"自欺"
-"自此"
-"自治"
-"自治区"
-"自治县"
-"自治州"
-"自测"
-"自然"
-"自然人"
-"自然保护区"
-"自然村"
-"自然灾害"
-"自然环境"
-"自然界"
-"自然科学"
-"自然而然"
-"自然资源"
-"自理"
-"自由"
-"自由基"
-"自由职业"
-"自由自在"
-"自由贸易"
-"自知"
-"自私"
-"自称"
-"自立"
-"自答"
-"自考"
-"自营"
-"自行"
-"自行车"
-"自觉"
-"自认"
-"自诩"
-"自语"
-"自豪"
-"自负"
-"自责"
-"自贸"
-"自贸区"
-"自费"
-"自足"
-"自身"
-"自转"
-"自达"
-"自选"
-"自重"
-"自闭"
-"自闭症"
-"自问"
-"自驾"
-"自驾游"
-"臬"
-"臭"
-"臭味"
-"臭氧"
-"至"
-"至上"
-"至于"
-"至今"
-"至关"
-"至关重要"
-"至尊"
-"至少"
-"至极"
-"至此"
-"至终"
-"致"
-"致使"
-"致力"
-"致力于"
-"致命"
-"致富"
-"致敬"
-"致歉"
-"致死"
-"致电"
-"致病"
-"致癌"
-"致谢"
-"致辞"
-"致远"
-"臺"
-"臻"
-"臼"
-"臾"
-"舀"
-"舂"
-"舄"
-"舅"
-"舅舅"
-"舆"
-"舆论"
-"與"
-"興"
-"舉"
-"舌"
-"舌头"
-"舌尖"
-"舌苔"
-"舍"
-"舍不得"
-"舍利"
-"舍弃"
-"舍得"
-"舐"
-"舒"
-"舒展"
-"舒心"
-"舒服"
-"舒畅"
-"舒缓"
-"舒适"
-"舒适度"
-"舒适性"
-"舔"
-"舛"
-"舜"
-"舞"
-"舞台"
-"舞弊"
-"舞曲"
-"舞蹈"
-"舟"
-"舟山"
-"舢"
-"航"
-"航天"
-"航母"
-"航海"
-"航班"
-"航空"
-"航空公司"
-"航空航天"
-"航线"
-"航行"
-"航运"
-"航道"
-"舫"
-"般"
-"般的"
-"般若"
-"舰"
-"舰艇"
-"舰队"
-"舱"
-"舴"
-"舵"
-"舶"
-"舷"
-"舸"
-"船"
-"船上"
-"船只"
-"船舶"
-"船长"
-"舻"
-"艄"
-"艇"
-"艋"
-"艘"
-"艟"
-"艨"
-"艮"
-"良"
-"良多"
-"良好"
-"良心"
-"良性"
-"良机"
-"良知"
-"良种"
-"良药"
-"艰"
-"艰巨"
-"艰苦"
-"艰辛"
-"艰难"
-"色"
-"色彩"
-"色情"
-"色拉"
-"色斑"
-"色泽"
-"色狼"
-"色系"
-"色素"
-"色色"
-"色调"
-"色谱"
-"艳"
-"艳丽"
-"艹"
-"艺"
-"艺人"
-"艺兴"
-"艺术"
-"艺术品"
-"艺术家"
-"艺术界"
-"艺术类"
-"艺术节"
-"艺考"
-"艺谋"
-"艽"
-"艾"
-"艾伦"
-"艾尔"
-"艾斯"
-"艾滋"
-"艾滋病"
-"艾灸"
-"艾玛"
-"艾米"
-"艿"
-"节"
-"节俭"
-"节假"
-"节假日"
-"节制"
-"节后"
-"节奏"
-"节度"
-"节拍"
-"节日"
-"节气"
-"节水"
-"节点"
-"节目"
-"节省"
-"节约"
-"节能"
-"节节"
-"节课"
-"节选"
-"节食"
-"芄"
-"芈"
-"芊"
-"芋"
-"芋头"
-"芍"
-"芎"
-"芑"
-"芒"
-"芒果"
-"芗"
-"芙"
-"芙蓉"
-"芜"
-"芜湖"
-"芝"
-"芝加哥"
-"芝士"
-"芝麻"
-"芟"
-"芡"
-"芥"
-"芦"
-"芦笋"
-"芦苇"
-"芦荟"
-"芨"
-"芩"
-"芪"
-"芫"
-"芬"
-"芬兰"
-"芬芳"
-"芭"
-"芭比"
-"芭蕾"
-"芭蕾舞"
-"芮"
-"芯"
-"芯片"
-"花"
-"花儿"
-"花八"
-"花卉"
-"花园"
-"花岗"
-"花岗岩"
-"花开"
-"花心"
-"花期"
-"花木"
-"花朵"
-"花果"
-"花柱"
-"花样"
-"花椒"
-"花海"
-"花瓣"
-"花瓶"
-"花生"
-"花生油"
-"花生米"
-"花盆"
-"花粉"
-"花纹"
-"花肉"
-"花色"
-"花花"
-"花草"
-"花药"
-"花萼"
-"花费"
-"花都"
-"花钱"
-"花香"
-"花鸟"
-"花鸟画"
-"芳"
-"芳草"
-"芳香"
-"芷"
-"芸"
-"芹"
-"芹菜"
-"芼"
-"芽"
-"芾"
-"苁"
-"苄"
-"苇"
-"苈"
-"苋"
-"苌"
-"苍"
-"苍白"
-"苍蝇"
-"苎"
-"苏"
-"苏东坡"
-"苏丹"
-"苏区"
-"苏南"
-"苏宁"
-"苏州"
-"苏州市"
-"苏打"
-"苏格兰"
-"苏联"
-"苏菲"
-"苏轼"
-"苏醒"
-"苏黎世"
-"苑"
-"苒"
-"苓"
-"苔"
-"苕"
-"苗"
-"苗族"
-"苗木"
-"苗条"
-"苘"
-"苛"
-"苛刻"
-"苜"
-"苞"
-"苟"
-"苟且"
-"苡"
-"苣"
-"苤"
-"若"
-"若干"
-"若惊"
-"若有"
-"若能"
-"苦"
-"苦心"
-"苦恼"
-"苦涩"
-"苦瓜"
-"苦练"
-"苦苦"
-"苦难"
-"苫"
-"苯"
-"英"
-"英伦"
-"英俊"
-"英勇"
-"英国"
-"英国人"
-"英国伦敦"
-"英国皇家"
-"英寸"
-"英尺"
-"英才"
-"英文"
-"英文名"
-"英格兰"
-"英汉"
-"英特尔"
-"英美"
-"英语"
-"英超"
-"英里"
-"英镑"
-"英雄"
-"苴"
-"苷"
-"苹"
-"苹果"
-"苹果公司"
-"苻"
-"茀"
-"茁"
-"茁壮"
-"茂"
-"茂名"
-"茂密"
-"茂盛"
-"范"
-"范例"
-"范儿"
-"范围"
-"范式"
-"范文"
-"范畴"
-"茄"
-"茄子"
-"茅"
-"茅台"
-"茅台酒"
-"茆"
-"茉"
-"茉莉"
-"茌"
-"茎"
-"茏"
-"茑"
-"茓"
-"茔"
-"茕"
-"茗"
-"茚"
-"茜"
-"茧"
-"茨"
-"茫"
-"茫然"
-"茫茫"
-"茬"
-"茭"
-"茯"
-"茯苓"
-"茱"
-"茴"
-"茴香"
-"茵"
-"茶"
-"茶几"
-"茶匙"
-"茶叶"
-"茶园"
-"茶树"
-"茶水"
-"茶艺"
-"茸"
-"茹"
-"茼"
-"荀"
-"荃"
-"荆"
-"荆州"
-"荆棘"
-"荆门"
-"荇"
-"草"
-"草丛"
-"草书"
-"草原"
-"草地"
-"草坪"
-"草木"
-"草本"
-"草根"
-"草案"
-"草泥"
-"草稿"
-"草药"
-"草莓"
-"荏"
-"荐"
-"荑"
-"荒"
-"荒凉"
-"荒唐"
-"荒地"
-"荒山"
-"荒废"
-"荒漠"
-"荒诞"
-"荒谬"
-"荔"
-"荔枝"
-"荚"
-"荜"
-"荞"
-"荞麦"
-"荟"
-"荟萃"
-"荠"
-"荡"
-"荡漾"
-"荡荡"
-"荣"
-"荣华"
-"荣威"
-"荣幸"
-"荣耀"
-"荣获"
-"荣誉"
-"荣誉称号"
-"荤"
-"荥"
-"荦"
-"荧"
-"荧光"
-"荨"
-"荨麻疹"
-"荩"
-"荪"
-"荫"
-"荬"
-"荮"
-"药"
-"药业"
-"药丸"
-"药剂"
-"药品"
-"药学"
-"药师"
-"药店"
-"药性"
-"药房"
-"药效"
-"药方"
-"药材"
-"药水"
-"药物"
-"药理"
-"药用"
-"荷"
-"荷兰"
-"荷叶"
-"荷尔"
-"荷尔蒙"
-"荷花"
-"荸"
-"荻"
-"荼"
-"荽"
-"莅"
-"莆"
-"莆田"
-"莉"
-"莉亚"
-"莉莉"
-"莎"
-"莎士"
-"莒"
-"莓"
-"莘"
-"莛"
-"莜"
-"莞"
-"莠"
-"莨"
-"莩"
-"莪"
-"莫"
-"莫及"
-"莫名"
-"莫名其妙"
-"莫大"
-"莫扎"
-"莫斯科"
-"莫测"
-"莫过于"
-"莱"
-"莱坞"
-"莱德"
-"莱斯"
-"莱昂"
-"莱特"
-"莱茵"
-"莲"
-"莲子"
-"莲花"
-"莲藕"
-"莳"
-"莴"
-"获"
-"获利"
-"获取"
-"获奖"
-"获得"
-"获得成功"
-"获得者"
-"获悉"
-"获批"
-"获益"
-"获省"
-"获胜"
-"莸"
-"莹"
-"莺"
-"莼"
-"莽"
-"菀"
-"菁"
-"菅"
-"菇"
-"菊"
-"菊花"
-"菌"
-"菌丝"
-"菏"
-"菏泽"
-"菔"
-"菖"
-"菘"
-"菜"
-"菜单"
-"菜品"
-"菜市场"
-"菜肴"
-"菜谱"
-"菜鸟"
-"菟"
-"菠"
-"菠菜"
-"菠萝"
-"菡"
-"菩"
-"菩提"
-"菩萨"
-"菪"
-"華"
-"菰"
-"菱"
-"菱形"
-"菲"
-"菲利普"
-"菲尔"
-"菲律宾"
-"菹"
-"菽"
-"萁"
-"萃"
-"萃取"
-"萄"
-"萋"
-"萌"
-"萌芽"
-"萌萌"
-"萍"
-"萎"
-"萎缩"
-"萏"
-"萑"
-"萘"
-"萜"
-"萝"
-"萝卜"
-"萝莉"
-"萤"
-"营"
-"营业"
-"营业执照"
-"营业税"
-"营业部"
-"营业额"
-"营养"
-"营养不良"
-"营养价值"
-"营养学"
-"营养成分"
-"营养物质"
-"营养素"
-"营利"
-"营口"
-"营商"
-"营地"
-"营收"
-"营救"
-"营运"
-"营造"
-"营销"
-"萦"
-"萧"
-"萧山"
-"萧条"
-"萨"
-"萨克"
-"萨尔"
-"萨拉"
-"萨摩"
-"萨斯"
-"萨满"
-"萬"
-"萱"
-"萸"
-"萼"
-"落"
-"落下"
-"落入"
-"落到"
-"落到实处"
-"落叶"
-"落后"
-"落地"
-"落实"
-"落寞"
-"落差"
-"落幕"
-"落成"
-"落户"
-"落日"
-"落水"
-"落泪"
-"落脚"
-"落花"
-"落落"
-"落魄"
-"葆"
-"葉"
-"葑"
-"著"
-"著作"
-"著作权"
-"著名"
-"著述"
-"葚"
-"葛"
-"葛根"
-"葡"
-"葡萄"
-"葡萄干"
-"葡萄牙"
-"葡萄糖"
-"葡萄酒"
-"董"
-"董事"
-"董事会"
-"董事长"
-"董卓"
-"葩"
-"葫"
-"葫芦"
-"葬"
-"葭"
-"葱"
-"葱姜"
-"葱白"
-"葱花"
-"葳"
-"葵"
-"葵花"
-"葶"
-"葸"
-"葺"
-"蒂"
-"蒂斯"
-"蒋"
-"蒋介"
-"蒋介石"
-"蒌"
-"蒗"
-"蒙"
-"蒙古"
-"蒙古人"
-"蒙古族"
-"蒙德"
-"蒙特"
-"蒙蒙"
-"蒜"
-"蒜末"
-"蒟"
-"蒡"
-"蒯"
-"蒲"
-"蒲公英"
-"蒴"
-"蒸"
-"蒸发"
-"蒸气"
-"蒸汽"
-"蒸熟"
-"蒸馏"
-"蒹"
-"蒺"
-"蒽"
-"蒿"
-"蓁"
-"蓄"
-"蓄势"
-"蓄水"
-"蓄电"
-"蓄电池"
-"蓄积"
-"蓉"
-"蓊"
-"蓍"
-"蓐"
-"蓑"
-"蓓"
-"蓖"
-"蓝"
-"蓝光"
-"蓝图"
-"蓝天"
-"蓝牙"
-"蓝筹"
-"蓝筹股"
-"蓝色"
-"蓝莓"
-"蓟"
-"蓠"
-"蓥"
-"蓦"
-"蓬"
-"蓬勃"
-"蓬勃发展"
-"蓬松"
-"蓬莱"
-"蓼"
-"蓿"
-"蔑"
-"蔓"
-"蔓延"
-"蔗"
-"蔚"
-"蔚来"
-"蔟"
-"蔡"
-"蔫"
-"蔬"
-"蔬果"
-"蔬菜"
-"蔷"
-"蔷薇"
-"蔸"
-"蔺"
-"蔻"
-"蔼"
-"蔽"
-"蕃"
-"蕈"
-"蕉"
-"蕊"
-"蕖"
-"蕙"
-"蕞"
-"蕤"
-"蕨"
-"蕲"
-"蕴"
-"蕴含"
-"蕴藏"
-"蕺"
-"蕻"
-"蕾"
-"蕾丝"
-"薄"
-"薄弱"
-"薄片"
-"薄膜"
-"薄荷"
-"薄薄"
-"薄薄的"
-"薅"
-"薇"
-"薏"
-"薏米"
-"薛"
-"薛之谦"
-"薜"
-"薤"
-"薨"
-"薪"
-"薪水"
-"薪资"
-"薪酬"
-"薪金"
-"薪阶"
-"薮"
-"薯"
-"薰"
-"薰衣"
-"薰衣草"
-"薷"
-"薹"
-"藁"
-"藉"
-"藏"
-"藏书"
-"藏传"
-"藏品"
-"藏族"
-"藐"
-"藓"
-"藕"
-"藜"
-"藝"
-"藠"
-"藤"
-"藤原"
-"藩"
-"藻"
-"藻类"
-"藿"
-"蘅"
-"蘑"
-"蘑菇"
-"蘖"
-"蘧"
-"蘩"
-"蘸"
-"虎"
-"虎牙"
-"虏"
-"虐"
-"虐待"
-"虐狗"
-"虑"
-"虔"
-"虔诚"
-"處"
-"虚"
-"虚伪"
-"虚假"
-"虚实"
-"虚寒"
-"虚幻"
-"虚弱"
-"虚心"
-"虚拟"
-"虚拟现实"
-"虚无"
-"虚构"
-"虚空"
-"虚荣"
-"虞"
-"號"
-"虢"
-"虫"
-"虫子"
-"虫害"
-"虫草"
-"虬"
-"虮"
-"虱"
-"虹"
-"虹桥"
-"虺"
-"虻"
-"虼"
-"虽"
-"虽然"
-"虽说"
-"虾"
-"虾仁"
-"虿"
-"蚀"
-"蚁"
-"蚂"
-"蚂蚁"
-"蚊"
-"蚊子"
-"蚋"
-"蚌"
-"蚌埠"
-"蚍"
-"蚓"
-"蚕"
-"蚕丝"
-"蚕桑"
-"蚕豆"
-"蚜"
-"蚝"
-"蚝油"
-"蚣"
-"蚤"
-"蚧"
-"蚨"
-"蚩"
-"蚩尤"
-"蚪"
-"蚬"
-"蚯"
-"蚰"
-"蚱"
-"蚴"
-"蚶"
-"蛀"
-"蛄"
-"蛆"
-"蛇"
-"蛉"
-"蛊"
-"蛋"
-"蛋液"
-"蛋清"
-"蛋白"
-"蛋白质"
-"蛋糕"
-"蛋黄"
-"蛎"
-"蛏"
-"蛐"
-"蛔"
-"蛘"
-"蛙"
-"蛛"
-"蛞"
-"蛟"
-"蛤"
-"蛤蟆"
-"蛩"
-"蛭"
-"蛮"
-"蛰"
-"蛱"
-"蛲"
-"蛳"
-"蛴"
-"蛸"
-"蛹"
-"蛾"
-"蜀"
-"蜀山"
-"蜂"
-"蜂窝"
-"蜂蜜"
-"蜃"
-"蜇"
-"蜈"
-"蜉"
-"蜊"
-"蜍"
-"蜒"
-"蜓"
-"蜕"
-"蜕变"
-"蜗"
-"蜗牛"
-"蜘"
-"蜘蛛"
-"蜚"
-"蜜"
-"蜜月"
-"蜜蜂"
-"蜞"
-"蜡"
-"蜡烛"
-"蜢"
-"蜣"
-"蜥"
-"蜥蜴"
-"蜩"
-"蜮"
-"蜱"
-"蜴"
-"蜷"
-"蜻"
-"蜻蜓"
-"蜿"
-"蜿蜒"
-"蝇"
-"蝈"
-"蝉"
-"蝌"
-"蝎"
-"蝓"
-"蝗"
-"蝙"
-"蝙蝠"
-"蝠"
-"蝣"
-"蝥"
-"蝮"
-"蝰"
-"蝴"
-"蝴蝶"
-"蝶"
-"蝻"
-"蝼"
-"蝽"
-"蝾"
-"螂"
-"螃"
-"螃蟹"
-"螅"
-"螈"
-"螋"
-"融"
-"融为"
-"融为一体"
-"融会"
-"融入"
-"融化"
-"融合"
-"融洽"
-"融融"
-"融资"
-"螟"
-"螠"
-"螨"
-"螫"
-"螬"
-"螭"
-"螯"
-"螳"
-"螳螂"
-"螵"
-"螺"
-"螺丝"
-"螺旋"
-"螺杆"
-"螺栓"
-"螺纹"
-"螽"
-"蟀"
-"蟆"
-"蟊"
-"蟋"
-"蟑"
-"蟑螂"
-"蟒"
-"蟛"
-"蟠"
-"蟥"
-"蟪"
-"蟮"
-"蟹"
-"蟾"
-"蠊"
-"蠓"
-"蠕"
-"蠕动"
-"蠖"
-"蠡"
-"蠢"
-"蠲"
-"蠹"
-"血"
-"血丝"
-"血压"
-"血型"
-"血小"
-"血小板"
-"血战"
-"血栓"
-"血气"
-"血汗"
-"血泪"
-"血流"
-"血浆"
-"血液"
-"血液循环"
-"血淋"
-"血清"
-"血症"
-"血管"
-"血糖"
-"血红"
-"血统"
-"血缘"
-"血肉"
-"血脂"
-"血脉"
-"血腥"
-"血色"
-"血虚"
-"血量"
-"衄"
-"衅"
-"行"
-"行不"
-"行业"
-"行业协会"
-"行业标准"
-"行为"
-"行之"
-"行书"
-"行事"
-"行人"
-"行使"
-"行军"
-"行列"
-"行动"
-"行动计划"
-"行医"
-"行各"
-"行善"
-"行情"
-"行政"
-"行政区"
-"行政区域"
-"行政处罚"
-"行政村"
-"行政诉讼"
-"行政部门"
-"行文"
-"行星"
-"行李"
-"行李箱"
-"行榜"
-"行程"
-"行者"
-"行至"
-"行走"
-"行车"
-"行进"
-"行销"
-"行长"
-"行驶"
-"衍"
-"衍生"
-"術"
-"衔"
-"衔接"
-"街"
-"街上"
-"街区"
-"街头"
-"街机"
-"街边"
-"街道"
-"衙"
-"衙门"
-"衛"
-"衡"
-"衡水"
-"衡量"
-"衡阳"
-"衢"
-"衢州"
-"衣"
-"衣冠"
-"衣库"
-"衣服"
-"衣柜"
-"衣物"
-"衣着"
-"衣草"
-"衣裙"
-"衣裳"
-"衣食"
-"补"
-"补习"
-"补偿"
-"补充"
-"补刀"
-"补助"
-"补救"
-"补气"
-"补水"
-"补益"
-"补短"
-"补给"
-"补考"
-"补肾"
-"补血"
-"补课"
-"补贴"
-"补钙"
-"表"
-"表决"
-"表哥"
-"表妹"
-"表姐"
-"表层"
-"表彰"
-"表态"
-"表情"
-"表扬"
-"表明"
-"表格"
-"表演"
-"表演艺术"
-"表现"
-"表现力"
-"表现形式"
-"表白"
-"表皮"
-"表示"
-"表示感谢"
-"表象"
-"表达"
-"表达方"
-"表达能力"
-"表述"
-"表里"
-"表露"
-"表面"
-"衩"
-"衫"
-"衬"
-"衬托"
-"衬衣"
-"衬衫"
-"衮"
-"衰"
-"衰减"
-"衰弱"
-"衰竭"
-"衰老"
-"衰落"
-"衰退"
-"衲"
-"衷"
-"衷心"
-"衽"
-"衾"
-"衿"
-"袁"
-"袁绍"
-"袂"
-"袄"
-"袅"
-"袈"
-"袋"
-"袋子"
-"袍"
-"袒"
-"袖"
-"袖子"
-"袜"
-"袜子"
-"袢"
-"袤"
-"被"
-"被判"
-"被动"
-"被告"
-"被告人"
-"被困"
-"被套"
-"被子"
-"被害"
-"被害人"
-"被执行人"
-"被捕"
-"被盗"
-"被称作"
-"被窝"
-"被誉为"
-"被迫"
-"袭"
-"袭击"
-"袱"
-"袷"
-"裁"
-"裁决"
-"裁判"
-"裁剪"
-"裁员"
-"裁定"
-"裁缝"
-"裂"
-"裂纹"
-"裂缝"
-"装"
-"装作"
-"装修"
-"装入"
-"装卸"
-"装在"
-"装备"
-"装扮"
-"装有"
-"装机"
-"装满"
-"装潢"
-"装甲"
-"装盘"
-"装箱"
-"装置"
-"装订"
-"装载"
-"装逼"
-"装配"
-"装饰"
-"裆"
-"裉"
-"裎"
-"裏"
-"裒"
-"裔"
-"裕"
-"裘"
-"裙"
-"裙子"
-"補"
-"裝"
-"裟"
-"裡"
-"裢"
-"裤"
-"裤子"
-"裥"
-"裨"
-"裰"
-"裱"
-"裳"
-"裴"
-"裸"
-"裸体"
-"裸裸"
-"裸露"
-"裹"
-"製"
-"裾"
-"褂"
-"褊"
-"褐"
-"褐斑"
-"褐色"
-"褒"
-"褒贬"
-"褓"
-"褙"
-"褚"
-"褛"
-"褟"
-"褡"
-"褥"
-"褪"
-"褪色"
-"褫"
-"褰"
-"褴"
-"褶"
-"褶皱"
-"襁"
-"襄"
-"襄樊"
-"襄阳"
-"襞"
-"襟"
-"襦"
-"襻"
-"西"
-"西亚"
-"西侧"
-"西兰花"
-"西北"
-"西北部"
-"西区"
-"西医"
-"西南"
-"西双版纳"
-"西周"
-"西哥"
-"西域"
-"西夏"
-"西宁"
-"西安"
-"西安交通"
-"西安市"
-"西山"
-"西斯"
-"西方"
-"西方人"
-"西晋"
-"西服"
-"西林"
-"西汉"
-"西洋"
-"西游"
-"西游记"
-"西湖"
-"西班牙"
-"西瓜"
-"西甲"
-"西站"
-"西红"
-"西红柿"
-"西药"
-"西蒙"
-"西藏"
-"西装"
-"西路"
-"西边"
-"西邻"
-"西部"
-"西门"
-"西门子"
-"西雅图"
-"西风"
-"西餐"
-"要"
-"要不是"
-"要不然"
-"要么"
-"要命"
-"要塞"
-"要学"
-"要少"
-"要是"
-"要死"
-"要求"
-"要点"
-"要用"
-"要素"
-"要紧"
-"要花"
-"要说"
-"要钱"
-"要领"
-"覃"
-"覆"
-"覆去"
-"覆盖"
-"覆盖率"
-"覆盖面"
-"見"
-"規"
-"視"
-"親"
-"覺"
-"覺得"
-"觀"
-"见"
-"见义勇为"
-"见习"
-"见于"
-"见到"
-"见底"
-"见得"
-"见效"
-"见解"
-"见证"
-"见识"
-"见过"
-"见闻"
-"见面"
-"观"
-"观世"
-"观世音"
-"观众"
-"观众们"
-"观光"
-"观察"
-"观影"
-"观念"
-"观想"
-"观摩"
-"观景"
-"观望"
-"观止"
-"观测"
-"观点"
-"观看"
-"观赏"
-"观音"
-"规"
-"规划"
-"规则"
-"规定"
-"规律"
-"规格"
-"规模"
-"规模化"
-"规矩"
-"规程"
-"规章"
-"规章制度"
-"规范"
-"规范化"
-"规范性"
-"规避"
-"觅"
-"视"
-"视为"
-"视力"
-"视听"
-"视察"
-"视线"
-"视网"
-"视网膜"
-"视而"
-"视觉"
-"视觉效果"
-"视角"
-"视野"
-"视镜"
-"视频"
-"觇"
-"览"
-"觉"
-"觉察"
-"觉得"
-"觉得很"
-"觉悟"
-"觉醒"
-"觊"
-"觋"
-"觎"
-"觏"
-"觐"
-"觑"
-"角"
-"角度"
-"角度看"
-"角膜"
-"角色"
-"角落"
-"角落里"
-"角质"
-"角质层"
-"角逐"
-"觚"
-"觞"
-"解"
-"解体"
-"解决"
-"解决不了"
-"解决办法"
-"解决方案"
-"解决问题"
-"解冻"
-"解剖"
-"解剖学"
-"解套"
-"解密"
-"解开"
-"解惑"
-"解放"
-"解放军"
-"解救"
-"解散"
-"解构"
-"解析"
-"解毒"
-"解码"
-"解禁"
-"解答"
-"解脱"
-"解说"
-"解读"
-"解调"
-"解释"
-"解锁"
-"解除"
-"解题"
-"觥"
-"触"
-"触动"
-"触及"
-"触发"
-"触手"
-"触控"
-"触摸"
-"触摸屏"
-"触犯"
-"触电"
-"触碰"
-"触觉"
-"觫"
-"觯"
-"觳"
-"言"
-"言之"
-"言情"
-"言行"
-"言论"
-"言语"
-"言谈"
-"言辞"
-"訁"
-"訇"
-"計"
-"討"
-"記"
-"訚"
-"設"
-"許"
-"訾"
-"詈"
-"評"
-"詞"
-"試"
-"話"
-"該"
-"詹"
-"詹姆斯"
-"誉"
-"誉为"
-"誊"
-"認"
-"誓"
-"誓言"
-"語"
-"誤"
-"說"
-"誰"
-"課"
-"調"
-"談"
-"請"
-"論"
-"謂"
-"謇"
-"講"
-"謝"
-"證"
-"識"
-"警"
-"警力"
-"警务"
-"警卫"
-"警告"
-"警备"
-"警官"
-"警察"
-"警察局"
-"警惕"
-"警戒"
-"警报"
-"警方"
-"警示"
-"警觉"
-"警车"
-"譬"
-"譬如"
-"議"
-"護"
-"讀"
-"變"
-"讓"
-"计"
-"计价"
-"计入"
-"计划"
-"计划生育"
-"计数"
-"计时"
-"计生"
-"计算"
-"计算机"
-"计费"
-"计较"
-"计量"
-"订"
-"订单"
-"订婚"
-"订票"
-"订立"
-"订货"
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-"邶"
-"邸"
-"邹"
-"邺"
-"邻"
-"邻居"
-"邻近"
-"邻里"
-"邾"
-"郁"
-"郁郁"
-"郁金"
-"郁闷"
-"郄"
-"郅"
-"郇"
-"郊"
-"郊区"
-"郎"
-"郏"
-"郑"
-"郑和"
-"郑州"
-"郑州市"
-"郑爽"
-"郑重"
-"郓"
-"郗"
-"郛"
-"郜"
-"郝"
-"郡"
-"郢"
-"郤"
-"郦"
-"郧"
-"部"
-"部下"
-"部件"
-"部份"
-"部位"
-"部分"
-"部委"
-"部曲"
-"部级"
-"部署"
-"部落"
-"部长"
-"部门"
-"部队"
-"郫"
-"郭"
-"郭敬"
-"郭沫"
-"郭靖"
-"郯"
-"郴"
-"郴州"
-"郸"
-"都"
-"都市"
-"都市报"
-"都是"
-"都督"
-"郾"
-"郿"
-"鄂"
-"鄂尔多斯"
-"鄄"
-"鄙"
-"鄙夷"
-"鄙视"
-"鄞"
-"鄢"
-"鄯"
-"鄱"
-"酃"
-"酆"
-"酉"
-"酊"
-"酋"
-"酋长"
-"酌"
-"酌情"
-"配"
-"配上"
-"配乐"
-"配以"
-"配件"
-"配偶"
-"配制"
-"配合"
-"配图"
-"配备"
-"配套"
-"配对"
-"配料"
-"配方"
-"配有"
-"配比"
-"配电"
-"配置"
-"配色"
-"配角"
-"配资"
-"配送"
-"配音"
-"配饰"
-"酐"
-"酒"
-"酒业"
-"酒后"
-"酒吧"
-"酒庄"
-"酒店"
-"酒杯"
-"酒类"
-"酒精"
-"酗"
-"酗酒"
-"酚"
-"酝"
-"酝酿"
-"酞"
-"酡"
-"酢"
-"酣"
-"酣畅"
-"酤"
-"酥"
-"酥脆"
-"酩"
-"酪"
-"酬"
-"酮"
-"酯"
-"酰"
-"酰胺"
-"酱"
-"酱汁"
-"酱油"
-"酱紫"
-"酵"
-"酵母"
-"酵素"
-"酶"
-"酷"
-"酷暑"
-"酷炫"
-"酷睿"
-"酸"
-"酸奶"
-"酸性"
-"酸爽"
-"酸甜"
-"酸痛"
-"酸盐"
-"酸碱"
-"酸菜"
-"酸辣"
-"酸酸"
-"酹"
-"酽"
-"酿"
-"酿成"
-"酿造"
-"酿酒"
-"醅"
-"醇"
-"醇厚"
-"醉"
-"醉酒"
-"醋"
-"醌"
-"醍"
-"醐"
-"醒"
-"醒悟"
-"醒来"
-"醒目"
-"醒过"
-"醚"
-"醛"
-"醢"
-"醪"
-"醫"
-"醮"
-"醯"
-"醴"
-"醺"
-"采"
-"采光"
-"采取"
-"采取措施"
-"采摘"
-"采收"
-"采暖"
-"采样"
-"采用"
-"采矿"
-"采纳"
-"采自"
-"采访"
-"采购"
-"采集"
-"釉"
-"释"
-"释义"
-"释放"
-"释迦"
-"释迦牟尼"
-"釋"
-"里"
-"里亚"
-"里克"
-"里头"
-"里奥"
-"里斯"
-"里昂"
-"里程"
-"里程碑"
-"里边"
-"里面"
-"重"
-"重中"
-"重中之重"
-"重于"
-"重仓"
-"重任"
-"重伤"
-"重修"
-"重力"
-"重叠"
-"重合"
-"重启"
-"重回"
-"重在"
-"重型"
-"重塑"
-"重复"
-"重大"
-"重大项目"
-"重工"
-"重庆"
-"重庆市"
-"重度"
-"重建"
-"重心"
-"重担"
-"重新"
-"重来"
-"重构"
-"重温"
-"重点"
-"重点项目"
-"重现"
-"重生"
-"重用"
-"重申"
-"重疾"
-"重疾险"
-"重病"
-"重症"
-"重磅"
-"重约"
-"重组"
-"重置"
-"重者"
-"重装"
-"重要"
-"重要性"
-"重要讲话"
-"重视"
-"重返"
-"重逢"
-"重重"
-"重量"
-"重量级"
-"重金属"
-"重阳"
-"野"
-"野兽"
-"野外"
-"野心"
-"野战"
-"野猪"
-"野生"
-"野生动物"
-"野菜"
-"野蛮"
-"野马"
-"野鸡"
-"量"
-"量产"
-"量化"
-"量子"
-"量身"
-"金"
-"金丝"
-"金价"
-"金光"
-"金刚"
-"金华"
-"金叉"
-"金城"
-"金奖"
-"金子"
-"金字"
-"金字塔"
-"金属"
-"金山"
-"金币"
-"金庸"
-"金星"
-"金曲"
-"金沙"
-"金牌"
-"金牛"
-"金石"
-"金矿"
-"金秋"
-"金色"
-"金莲"
-"金融"
-"金融业"
-"金融危机"
-"金融市场"
-"金融服务"
-"金融机构"
-"金针"
-"金钟"
-"金钱"
-"金银"
-"金银花"
-"金陵"
-"金额"
-"金马"
-"金鱼"
-"金鸡"
-"金鹰"
-"金黄"
-"金黄色"
-"釜"
-"鈴"
-"鉄"
-"鉴"
-"鉴于"
-"鉴别"
-"鉴定"
-"鉴赏"
-"銀"
-"銮"
-"鋆"
-"鋈"
-"錢"
-"錯"
-"錾"
-"鍪"
-"鎏"
-"鏊"
-"鏖"
-"鐾"
-"鑫"
-"钇"
-"针"
-"针刺"
-"针对"
-"针对性"
-"针形"
-"针灸"
-"针织"
-"钉"
-"钉子"
-"钊"
-"钋"
-"钌"
-"钍"
-"钎"
-"钏"
-"钒"
-"钓"
-"钓鱼"
-"钓鱼岛"
-"钕"
-"钗"
-"钙"
-"钙质"
-"钚"
-"钛"
-"钜"
-"钝"
-"钞"
-"钞票"
-"钟"
-"钟声"
-"钟情"
-"钟楼"
-"钟爱"
-"钟表"
-"钠"
-"钡"
-"钢"
-"钢丝"
-"钢厂"
-"钢材"
-"钢板"
-"钢琴"
-"钢笔"
-"钢筋"
-"钢管"
-"钢结构"
-"钢铁"
-"钣"
-"钤"
-"钥"
-"钥匙"
-"钦"
-"钧"
-"钨"
-"钩"
-"钫"
-"钮"
-"钯"
-"钰"
-"钱"
-"钱包"
-"钱币"
-"钱财"
-"钲"
-"钳"
-"钴"
-"钵"
-"钹"
-"钺"
-"钻"
-"钻井"
-"钻孔"
-"钻石"
-"钻研"
-"钻进"
-"钼"
-"钽"
-"钾"
-"钿"
-"铀"
-"铁"
-"铁板"
-"铁矿石"
-"铁血"
-"铁路"
-"铁路局"
-"铁道"
-"铁道部"
-"铂"
-"铃"
-"铃声"
-"铃木"
-"铄"
-"铅"
-"铅笔"
-"铆"
-"铈"
-"铉"
-"铊"
-"铋"
-"铌"
-"铍"
-"铎"
-"铐"
-"铑"
-"铒"
-"铗"
-"铙"
-"铛"
-"铜"
-"铜陵"
-"铝"
-"铝合"
-"铝合金"
-"铟"
-"铠"
-"铡"
-"铢"
-"铣"
-"铤"
-"铧"
-"铨"
-"铩"
-"铪"
-"铫"
-"铬"
-"铭"
-"铭心"
-"铭文"
-"铭记"
-"铮"
-"铯"
-"铰"
-"铱"
-"铲"
-"铳"
-"铵"
-"银"
-"银子"
-"银屑"
-"银屑病"
-"银川"
-"银幕"
-"银杏"
-"银河"
-"银牌"
-"银耳"
-"银联"
-"银色"
-"银行"
-"银行业"
-"银行卡"
-"银行贷款"
-"银魂"
-"铷"
-"铸"
-"铸就"
-"铸造"
-"铸铁"
-"铺"
-"铺垫"
-"铺设"
-"铻"
-"铼"
-"链"
-"链接"
-"链条"
-"链路"
-"铿"
-"销"
-"销售"
-"销售收入"
-"销售量"
-"销售额"
-"销毁"
-"销量"
-"锁"
-"锁定"
-"锂"
-"锂电"
-"锂电池"
-"锃"
-"锄"
-"锅"
-"锅中"
-"锅内"
-"锅底"
-"锅炉"
-"锅盖"
-"锅里"
-"锆"
-"锇"
-"锈"
-"锉"
-"锋"
-"锋利"
-"锋芒"
-"锌"
-"锏"
-"锐"
-"锐意"
-"锑"
-"锒"
-"锔"
-"锕"
-"锗"
-"错"
-"错位"
-"错失"
-"错综"
-"错落"
-"错觉"
-"错误"
-"错过"
-"错题"
-"锚"
-"锛"
-"锜"
-"锝"
-"锞"
-"锟"
-"锡"
-"锢"
-"锣"
-"锤"
-"锤子"
-"锥"
-"锦"
-"锦上"
-"锦州"
-"锦标"
-"锦标赛"
-"锦江"
-"锦绣"
-"锦赛"
-"锨"
-"锩"
-"锭"
-"键"
-"键盘"
-"锯"
-"锯齿"
-"锰"
-"锱"
-"锲"
-"锴"
-"锵"
-"锶"
-"锷"
-"锸"
-"锹"
-"锺"
-"锻"
-"锻炼"
-"锻炼身体"
-"锻造"
-"镀"
-"镀金"
-"镀铬"
-"镀锌"
-"镁"
-"镂"
-"镂空"
-"镇"
-"镇上"
-"镇压"
-"镇守"
-"镇定"
-"镇政府"
-"镇江"
-"镇痛"
-"镇长"
-"镇静"
-"镉"
-"镊"
-"镌"
-"镍"
-"镏"
-"镐"
-"镑"
-"镒"
-"镓"
-"镔"
-"镕"
-"镖"
-"镗"
-"镘"
-"镚"
-"镛"
-"镜"
-"镜像"
-"镜头"
-"镜子"
-"镜片"
-"镜面"
-"镝"
-"镞"
-"镠"
-"镡"
-"镢"
-"镣"
-"镦"
-"镧"
-"镩"
-"镪"
-"镫"
-"镬"
-"镭"
-"镯"
-"镰"
-"镲"
-"镳"
-"镴"
-"镵"
-"镶"
-"镶嵌"
-"長"
-"长"
-"长三角"
-"长久"
-"长假"
-"长出"
-"长发"
-"长城"
-"长处"
-"长大"
-"长子"
-"长安"
-"长官"
-"长宽"
-"长寿"
-"长年"
-"长度"
-"长廊"
-"长征"
-"长得"
-"长成"
-"长效"
-"长效机制"
-"长方形"
-"长时间"
-"长春"
-"长春市"
-"长期"
-"长期以来"
-"长款"
-"长此"
-"长毛"
-"长江"
-"长沙"
-"长沙市"
-"长河"
-"长治"
-"长生"
-"长痘"
-"长白山"
-"长相"
-"长短"
-"长篇"
-"长篇小说"
-"长约"
-"长线"
-"长老"
-"长者"
-"长虹"
-"长袖"
-"长裙"
-"长裤"
-"长足"
-"长跑"
-"长辈"
-"长达"
-"长远"
-"长途"
-"长长"
-"长长的"
-"长颈"
-"門"
-"開"
-"開始"
-"間"
-"関"
-"關"
-"门"
-"门下"
-"门人"
-"门前"
-"门口"
-"门外"
-"门将"
-"门市"
-"门店"
-"门户"
-"门户网"
-"门户网站"
-"门槛"
-"门票"
-"门禁"
-"门窗"
-"门类"
-"门诊"
-"门锁"
-"门面"
-"闩"
-"闪"
-"闪亮"
-"闪光"
-"闪光灯"
-"闪存"
-"闪烁"
-"闪现"
-"闪电"
-"闪耀"
-"闪闪"
-"闫"
-"闭"
-"闭上"
-"闭上眼睛"
-"闭关"
-"闭口"
-"闭合"
-"闭塞"
-"闭幕"
-"闭环"
-"闭路"
-"闭门"
-"问"
-"问下"
-"问世"
-"问候"
-"问到"
-"问卷"
-"问及"
-"问答"
-"问责"
-"问过"
-"问道"
-"问问"
-"问题"
-"闯"
-"闯入"
-"闯关"
-"闯荡"
-"闰"
-"闱"
-"闲"
-"闲暇"
-"闲着"
-"闲置"
-"闲聊"
-"闳"
-"间"
-"间接"
-"间断"
-"间歇"
-"间盘"
-"间谍"
-"间距"
-"间隔"
-"间隙"
-"闵"
-"闵行"
-"闷"
-"闷声"
-"闷热"
-"闸"
-"闹"
-"闹事"
-"闹钟"
-"闹闹"
-"闺"
-"闺女"
-"闺密"
-"闺蜜"
-"闻"
-"闻乐"
-"闻到"
-"闻名"
-"闼"
-"闽"
-"闽东"
-"闽南"
-"闾"
-"闿"
-"阀"
-"阀门"
-"阁"
-"阂"
-"阃"
-"阄"
-"阅"
-"阅兵"
-"阅卷"
-"阅历"
-"阅览"
-"阅读"
-"阆"
-"阇"
-"阈"
-"阈值"
-"阉"
-"阊"
-"阋"
-"阍"
-"阎"
-"阏"
-"阐"
-"阐明"
-"阐述"
-"阐释"
-"阑"
-"阑尾"
-"阒"
-"阔"
-"阔叶"
-"阕"
-"阖"
-"阗"
-"阙"
-"阚"
-"阜"
-"阜阳"
-"阝"
-"队"
-"队伍"
-"队列"
-"队友"
-"队员"
-"队长"
-"阡"
-"阪"
-"阮"
-"阱"
-"防"
-"防伪"
-"防卫"
-"防备"
-"防守"
-"防尘"
-"防弹"
-"防御"
-"防抖"
-"防护"
-"防控"
-"防晒"
-"防晒霜"
-"防止"
-"防水"
-"防汛"
-"防治"
-"防洪"
-"防滑"
-"防潮"
-"防火"
-"防爆"
-"防疫"
-"防病"
-"防癌"
-"防盗"
-"防空"
-"防线"
-"防腐"
-"防腐剂"
-"防范"
-"防锈"
-"防雷"
-"防震"
-"防风"
-"阳"
-"阳光"
-"阳区"
-"阳县"
-"阳台"
-"阳市"
-"阳性"
-"阳明"
-"阳春"
-"阳气"
-"阳痿"
-"阳线"
-"阳虚"
-"阴"
-"阴凉"
-"阴天"
-"阴影"
-"阴性"
-"阴暗"
-"阴极"
-"阴线"
-"阴茎"
-"阴虚"
-"阴谋"
-"阴道"
-"阴道炎"
-"阴阳"
-"阴险"
-"阴雨"
-"阴霾"
-"阵"
-"阵亡"
-"阵地"
-"阵容"
-"阵营"
-"阵阵"
-"阵雨"
-"阶"
-"阶层"
-"阶梯"
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-"阶段性"
-"阶级"
-"阻"
-"阻击"
-"阻力"
-"阻力位"
-"阻塞"
-"阻抗"
-"阻拦"
-"阻挡"
-"阻断"
-"阻止"
-"阻滞"
-"阻燃"
-"阻碍"
-"阿"
-"阿克"
-"阿卡"
-"阿姆斯特"
-"阿姨"
-"阿富汗"
-"阿尔"
-"阿尔卑斯"
-"阿布"
-"阿弥陀佛"
-"阿德"
-"阿拉"
-"阿拉伯"
-"阿拉斯加"
-"阿斯"
-"阿根廷"
-"阿森"
-"阿森纳"
-"阿波罗"
-"阿联"
-"阿胶"
-"阿迪"
-"阿里"
-"阿里巴巴"
-"阿难"
-"陀"
-"陀罗"
-"陀螺"
-"陂"
-"附"
-"附上"
-"附中"
-"附件"
-"附加"
-"附加值"
-"附图"
-"附子"
-"附属"
-"附带"
-"附录"
-"附着"
-"附赠"
-"附近"
-"际"
-"际关系"
-"陆"
-"陆军"
-"陆地"
-"陆游"
-"陆续"
-"陆陆"
-"陇"
-"陈"
-"陈代"
-"陈列"
-"陈奕"
-"陈旧"
-"陈氏"
-"陈皮"
-"陈述"
-"陉"
-"陋"
-"陌"
-"陌生"
-"陌生人"
-"降"
-"降临"
-"降价"
-"降低"
-"降低成本"
-"降准"
-"降到"
-"降压"
-"降噪"
-"降幅"
-"降息"
-"降水"
-"降水量"
-"降温"
-"降级"
-"降至"
-"降落"
-"降血"
-"降血压"
-"降解"
-"降雨"
-"限"
-"限于"
-"限价"
-"限制"
-"限定"
-"限度"
-"限时"
-"限期"
-"限行"
-"限购"
-"限速"
-"限量"
-"限额"
-"陔"
-"陕"
-"陕北"
-"陕西"
-"陕西省"
-"陛"
-"陟"
-"陡"
-"院"
-"院内"
-"院士"
-"院子"
-"院校"
-"院系"
-"院落"
-"院长"
-"除"
-"除了"
-"除去"
-"除夕"
-"除外"
-"除夜"
-"除尘"
-"除恶"
-"除掉"
-"除此"
-"除此之外"
-"除湿"
-"除皱"
-"除草"
-"除非"
-"陨"
-"陨石"
-"险"
-"险恶"
-"陪"
-"陪伴"
-"陪同"
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-"陲"
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-"陶"
-"陶冶"
-"陶器"
-"陶渊"
-"陶瓷"
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-"陶醉"
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-"隋朝"
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-"随地"
-"随处"
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-"随时随地"
-"随机"
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-"隐含"
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-"隐形"
-"隐形眼"
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-"隐秘"
-"隐约"
-"隐蔽"
-"隐藏"
-"隐身"
-"隐隐"
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-"隔壁"
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-"难免会"
-"难关"
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-"难度"
-"难得"
-"难忘"
-"难怪"
-"难易"
-"难民"
-"难点"
-"难看"
-"难过"
-"难道"
-"难题"
-"雀"
-"雀斑"
-"雁"
-"雄"
-"雄伟"
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-"雄心"
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-"雅"
-"雅典"
-"雅思"
-"雅致"
-"雅虎"
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-"集成电路"
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-"集权"
-"集约"
-"集结"
-"集群"
-"集聚"
-"集装箱"
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-"集资"
-"集邮"
-"集锦"
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-"雹"
-"電"
-"雾"
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-"韪"
-"韫"
-"韬"
-"韭"
-"韭菜"
-"音"
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-"音像"
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-"音符"
-"音箱"
-"音色"
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-"韵律"
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-"頫"
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-"预防"
-"颅"
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-"领导班子"
-"领导者"
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-"领悟"
-"领略"
-"领航"
-"领衔"
-"领袖"
-"领跑"
-"领队"
-"颇"
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-"题目"
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-"颜"
-"颜值"
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-"颜色"
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-"颞"
-"颟"
-"颠"
-"颠倒"
-"颠簸"
-"颠覆"
-"颡"
-"颢"
-"颤"
-"颤抖"
-"颦"
-"颧"
-"風"
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-"风俗"
-"风光"
-"风力"
-"风华"
-"风口"
-"风向"
-"风吹"
-"风味"
-"风土"
-"风声"
-"风头"
-"风寒"
-"风尘"
-"风尚"
-"风度"
-"风情"
-"风扇"
-"风控"
-"风景"
-"风景区"
-"风景线"
-"风暴"
-"风机"
-"风格"
-"风气"
-"风水"
-"风波"
-"风流"
-"风湿"
-"风湿性"
-"风琴"
-"风电"
-"风筝"
-"风范"
-"风行"
-"风衣"
-"风貌"
-"风趣"
-"风速"
-"风采"
-"风险"
-"风险管理"
-"风雨"
-"风靡"
-"风顺"
-"飑"
-"飒"
-"飓"
-"飕"
-"飘"
-"飘过"
-"飘逸"
-"飘飘"
-"飘香"
-"飙"
-"飙升"
-"飚"
-"飛"
-"飞"
-"飞利浦"
-"飞天"
-"飞奔"
-"飞快"
-"飞扬"
-"飞机"
-"飞来"
-"飞猛"
-"飞翔"
-"飞舞"
-"飞船"
-"飞行"
-"飞行员"
-"飞起"
-"飞跃"
-"飞车"
-"飞轮"
-"飞过"
-"飞速"
-"飞速发展"
-"飞鸟"
-"飞龙"
-"食"
-"食品"
-"食品安全"
-"食品药品"
-"食堂"
-"食客"
-"食指"
-"食材"
-"食欲"
-"食欲不振"
-"食物"
-"食用"
-"食用油"
-"食用菌"
-"食疗"
-"食盐"
-"食神"
-"食管"
-"食肉"
-"食谱"
-"食道"
-"飧"
-"飨"
-"飯"
-"養"
-"餍"
-"餐"
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-"餐厅"
-"餐桌"
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-"餐馆"
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-"饥"
-"饥渴"
-"饥饿"
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-"饮用水"
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-"香水"
-"香油"
-"香港"
-"香港中文"
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-"香甜"
-"香精"
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-"香菜"
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-"马拉松"
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-"马桶"
-"马特"
-"马甲"
-"马自达"
-"马蓉"
-"马虎"
-"马赛"
-"马赫"
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-"马蹄"
-"马车"
-"马达"
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-"马鞍山"
-"马龙"
-"驭"
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-"驯"
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-"骨干教师"
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-"骨架"
-"骨灰"
-"骨盆"
-"骨科"
-"骨肉"
-"骨质"
-"骨骼"
-"骨髓"
-"骰"
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-"髑"
-"髓"
-"體"
-"高"
-"高下"
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-"高中生"
-"高丽"
-"高举"
-"高于"
-"高产"
-"高人"
-"高价"
-"高估"
-"高位"
-"高低"
-"高傲"
-"高僧"
-"高光"
-"高兴"
-"高冷"
-"高出"
-"高分"
-"高分子"
-"高危"
-"高压"
-"高原"
-"高发"
-"高品质"
-"高地"
-"高处"
-"高大"
-"高宗"
-"高官"
-"高尔"
-"高尔夫"
-"高尔夫球"
-"高尚"
-"高层"
-"高层次"
-"高山"
-"高峰"
-"高峰期"
-"高峰论坛"
-"高度"
-"高度重视"
-"高开"
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-"魔鬼"
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-"鱼儿"
-"鱼头"
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-"鱼尾纹"
-"鱼类"
-"鱼肉"
-"鱼雷"
-"鱼龙"
-"鱿"
-"鱿鱼"
-"鲀"
-"鲁"
-"鲁国"
-"鲁斯"
-"鲁班"
-"鲁能"
-"鲁迅"
-"鲂"
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-"鲜为"
-"鲜卑"
-"鲜奶"
-"鲜嫩"
-"鲜明"
-"鲜活"
-"鲜红"
-"鲜美"
-"鲜肉"
-"鲜艳"
-"鲜花"
-"鲜血"
-"鲞"
-"鲟"
-"鲠"
-"鲡"
-"鲢"
-"鲣"
-"鲤"
-"鲤鱼"
-"鲥"
-"鲧"
-"鲨"
-"鲨鱼"
-"鲩"
-"鲫"
-"鲫鱼"
-"鲮"
-"鲱"
-"鲲"
-"鲳"
-"鲴"
-"鲵"
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-"鲸"
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-"鳀"
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-"鳄鱼"
-"鳅"
-"鳇"
-"鳊"
-"鳌"
-"鳍"
-"鳎"
-"鳏"
-"鳐"
-"鳓"
-"鳔"
-"鳕"
-"鳖"
-"鳗"
-"鳙"
-"鳜"
-"鳝"
-"鳞"
-"鳞片"
-"鳟"
-"鳢"
-"鳥"
-"鸟"
-"鸟儿"
-"鸟巢"
-"鸟类"
-"鸠"
-"鸡"
-"鸡尾酒"
-"鸡毛"
-"鸡汤"
-"鸡皮"
-"鸡精"
-"鸡翅"
-"鸡肉"
-"鸡胸"
-"鸡腿"
-"鸡蛋"
-"鸡血"
-"鸢"
-"鸣"
-"鸣人"
-"鸥"
-"鸦"
-"鸦片"
-"鸨"
-"鸩"
-"鸪"
-"鸫"
-"鸬"
-"鸭"
-"鸭子"
-"鸮"
-"鸯"
-"鸱"
-"鸲"
-"鸳"
-"鸳鸯"
-"鸵"
-"鸶"
-"鸷"
-"鸸"
-"鸹"
-"鸻"
-"鸽"
-"鸽子"
-"鸾"
-"鸿"
-"鹁"
-"鹂"
-"鹃"
-"鹄"
-"鹅"
-"鹆"
-"鹇"
-"鹈"
-"鹉"
-"鹊"
-"鹋"
-"鹌"
-"鹏"
-"鹐"
-"鹑"
-"鹕"
-"鹗"
-"鹘"
-"鹚"
-"鹛"
-"鹜"
-"鹞"
-"鹤"
-"鹦"
-"鹦鹉"
-"鹧"
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-"鹫"
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-"鹰"
-"鹳"
-"鹿"
-"鹿晗"
-"麂"
-"麇"
-"麈"
-"麋"
-"麒"
-"麒麟"
-"麓"
-"麝"
-"麟"
-"麦"
-"麦克"
-"麦克风"
-"麦当劳"
-"麦田"
-"麦芽"
-"麦迪"
-"麸"
-"麻"
-"麻将"
-"麻木"
-"麻油"
-"麻烦"
-"麻疹"
-"麻痹"
-"麻花"
-"麻辣"
-"麻醉"
-"麻雀"
-"麻麻"
-"麻黄"
-"麼"
-"麽"
-"麾"
-"黃"
-"黄"
-"黄冈"
-"黄土"
-"黄埔"
-"黄山"
-"黄帝"
-"黄昏"
-"黄梅"
-"黄河"
-"黄油"
-"黄浦"
-"黄海"
-"黄牛"
-"黄瓜"
-"黄疸"
-"黄石"
-"黄色"
-"黄芪"
-"黄花"
-"黄蓉"
-"黄蜂"
-"黄褐"
-"黄褐斑"
-"黄豆"
-"黄连"
-"黄酒"
-"黄酮"
-"黄金"
-"黄金周"
-"黄鹤"
-"黄龙"
-"黍"
-"黎"
-"黎明"
-"黏"
-"黏膜"
-"黑"
-"黑人"
-"黑名"
-"黑名单"
-"黑夜"
-"黑头"
-"黑客"
-"黑帮"
-"黑幕"
-"黑恶势力"
-"黑暗"
-"黑木耳"
-"黑板"
-"黑洞"
-"黑点"
-"黑白"
-"黑眼圈"
-"黑社会"
-"黑色"
-"黑色素"
-"黑芝麻"
-"黑衣"
-"黑褐"
-"黑豆"
-"黑马"
-"黑龙江"
-"黑龙江省"
-"黒"
-"黔"
-"默"
-"默化"
-"默契"
-"默认"
-"默默"
-"默默地"
-"默默无闻"
-"黛"
-"黛玉"
-"黜"
-"黝"
-"點"
-"黟"
-"黠"
-"黢"
-"黥"
-"黧"
-"黨"
-"黩"
-"黪"
-"黯"
-"黯然"
-"黻"
-"黼"
-"黾"
-"鼋"
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-"鼎"
-"鼎盛"
-"鼎鼎"
-"鼐"
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-"鼓动"
-"鼓励"
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-"鼓掌"
-"鼓楼"
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-"鼓舞"
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-"鼻孔"
-"鼻梁"
-"鼻涕"
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-"鼻翼"
-"鼻腔"
-"鼾"
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-"화"
-"회"
-"fi"
-"️"
-"﹕"
-""
-"!"
-"("
-")"
-","
-"-"
-":"
-";"
-"?"
-"~"
-""
-"�"
-"��"
-"𠳐"
-"𥻗"
-"𬉼"
-""
-""
-""
-""
-""
-""
-""
diff --git a/9G-Train/apps/cpm9g/config/datasets.json b/9G-Train/apps/cpm9g/config/datasets.json
deleted file mode 100644
index c458065..0000000
--- a/9G-Train/apps/cpm9g/config/datasets.json
+++ /dev/null
@@ -1,12 +0,0 @@
-[
- {
- "dataset_name": "wikipedia",
- "task_name": "wikipedia",
- "weight": 1.0,
- "path": "path/to/data",
- "incontext_weight": [
- 1.0
- ],
- "transforms": "/home/USERNAME/cpm9g/apps/cpm9g/config/datasets/wikipedia/script_cpmc.py"
- }
-]
diff --git a/9G-Train/apps/cpm9g/config/datasets/wikipedia/script_cpmc.py b/9G-Train/apps/cpm9g/config/datasets/wikipedia/script_cpmc.py
deleted file mode 100644
index ef081e2..0000000
--- a/9G-Train/apps/cpm9g/config/datasets/wikipedia/script_cpmc.py
+++ /dev/null
@@ -1,9 +0,0 @@
-import random
-
-
-def rand(n: int, r: random.Random):
- return int(r.random() * n)
-
-
-def transform(data, num_sample: int, r: random.Random):
- return {"input": "", "output": data["text"]}
diff --git a/9G-Train/apps/cpm9g/pretrain_cpm9g.py b/9G-Train/apps/cpm9g/pretrain_cpm9g.py
deleted file mode 100644
index f64679d..0000000
--- a/9G-Train/apps/cpm9g/pretrain_cpm9g.py
+++ /dev/null
@@ -1,485 +0,0 @@
-import inspect
-import json
-import math
-import os
-import re
-import sys
-import time
-from collections import defaultdict
-from typing import Any
-from typing import Dict
-from typing import List
-from typing import Union
-
-import bmtrain as bmt
-import torch
-
-sys.path.insert(0, "/home/wangshuo1/code/9G-Train")
-from cpm.arguments import get_args
-from cpm.cpm9g.models import CPM9G
-from cpm.cpm9g.models import CPM9GConfig
-from cpm.cpm9g.tokenizers import CPM9GTokenizer
-from cpm.cpm9g.training_tasks import MixedDataset
-from cpm.utils import allgather_objects
-from cpm.utils import exporter
-from cpm.utils import logger
-from cpm.utils import LogManager
-
-
-def get_tokenizer(args):
- tokenizer = CPM9GTokenizer(path=args.vocab)
- return tokenizer
-
-
-def get_model(args):
- config = CPM9GConfig.from_json_file(args.model_config)
- config.tp = 1 if args.tp != 1 else 0
- if args.flash == "none":
- config.use_flash_attn = False
- else:
- config.use_flash_attn = True
- if args.flash == "1d":
- config.flash_attn_mask_shape = "1d"
- else:
- config.flash_attn_mask_shape = "2d"
- if args.flash == "triton":
- config.flash_impl = "triton"
- elif args.flash == "cuda":
- config.flash_impl = "cuda"
- model = CPM9G(config)
- if args.load is not None:
- bmt.print_rank("args.load is not None, start to load checkpoints" + args.load)
- bmt.load(model, args.load)
- else:
- bmt.print_rank("args.load is None, start to initialize parameters")
- bmt.init_parameters(model)
- return model
-
-
-def get_optimizer(args, model):
- for name, para in model.named_parameters():
- # if not ('input_embedding' in name or 'lm_head' in name):
- # para.requires_grad_(False)
- bmt.print_rank(name, para.requires_grad)
- if args.offload:
- optimizer = bmt.optim.AdamOffloadOptimizer(
- model.parameters(), betas=(0.9, 0.95), weight_decay=args.weight_decay
- )
- else:
- optimizer = bmt.optim.AdamOptimizer(model.parameters(), betas=(0.9, 0.95), weight_decay=args.weight_decay)
- if args.load is not None and args.load_grad:
- start = time.time()
- print(
- sum([1 if re.search(r"-{}.rank-\d+.opt".format(args.start_step), i) else 0 for i in os.listdir(args.save)])
- )
- if (
- sum([1 if re.search(r"-{}.rank-\d+.opt".format(args.start_step), i) else 0 for i in os.listdir(args.save)])
- == bmt.world_size()
- ):
- file_name = os.path.join(
- args.save,
- args.save_name + "-{}.rank-{}.opt".format(args.start_step, bmt.rank()),
- )
- print(file_name)
- if os.path.exists(file_name):
- print("start to load grad ckpt {}".format(file_name))
- states = torch.load(file_name)
- optimizer.load_state_dict(states)
- logger.info("load grad in {:.2f}s".format(time.time() - start))
- return optimizer
-
-
-class Cosine(bmt.lr_scheduler.WarmupLRScheduler):
- r"""
- After a warmup period during which learning rate increases linearly between 0 and the start_lr,
- The decay period performs :math:`\text{lr}=\text{start_lr}\times \dfrac{1+\cos \left( \pi \cdot \dfrac{\text{num_iter}-\text{warmup_iter}}{\text{end_iter}-\text{warmup_iter}}\right)}{2}`
- """
-
- def get_lr_warmup(self, num_iter) -> float:
- return self.start_lr * num_iter / self.warmup_iter
-
- def get_lr_decay(self, num_iter) -> float:
- progress = (num_iter - self.warmup_iter) / max(1, (self.end_iter - self.warmup_iter))
- return max(self.start_lr * 0.1, self.start_lr * (0.1 + 0.45 * (1.0 + math.cos(progress * math.pi))))
-
-
-def get_learning_rate_scheduler(args, optimizer):
- if args.lr_decay_iters is None:
- args.lr_decay_iters = args.train_iters
- # lr_scheduler = bmt.lr_scheduler.Noam(
- lr_scheduler = Cosine(
- optimizer,
- start_lr=args.lr,
- warmup_iter=args.warmup_iters,
- end_iter=args.lr_decay_iters,
- num_iter=args.start_step,
- )
- return lr_scheduler
-
-
-def setup_model_and_optimizer(args):
-
- start = time.time()
- model = get_model(args)
- logger.info("load model in {:.2f}s".format(time.time() - start))
-
- start = time.time()
- tokenizer = get_tokenizer(args)
- bmt.synchronize()
- logger.info("load tokenizer in {:.2f}s".format(time.time() - start))
-
- start = time.time()
- optimizer = get_optimizer(args, model)
- lr_scheduler = get_learning_rate_scheduler(args, optimizer)
- bmt.synchronize()
- logger.info("load lr_scheduler in {:.2f}s".format(time.time() - start))
-
- return tokenizer, model, optimizer, lr_scheduler
-
-
-def initialize():
- args = get_args(pretrain=True)
- bmt.init_distributed(seed=args.seed, zero_level=3)
- if args.save is not None:
- os.makedirs(args.save, exist_ok=True)
- if args.load is not None:
- if args.start_step == 0:
- args.start_step = (int)(re.search("(\d+).pt", args.load)[1])
- return args
-
-
-def see_memory(detail=False):
- if detail:
- res = torch.cuda.memory_summary()
- else:
- res = (
- round(torch.cuda.memory_reserved() / (1024 * 1024 * 1024), 2),
- round(torch.cuda.max_memory_reserved() / (1024 * 1024 * 1024), 2),
- )
- torch.cuda.reset_peak_memory_stats()
- return res
-
-
-def add_mem_time(info, mem_usage, tim_usage):
- torch.cuda.synchronize()
- bmt.synchronize()
- mem_usage[info] = see_memory()
- tim_usage[info] = time.time()
- return mem_usage, tim_usage
-
-
-class LossSpikeDetector:
- def __init__(self, log_path: str) -> None:
- self._last_loss: Dict[str, float] = {}
- self._last_data: List[Any] = [None]
- self._log_path = log_path
-
- def update_data(self, data: Any):
- self._last_data.append(data)
- if len(self._last_data) > 2:
- self._last_data = self._last_data[-2:]
-
- def update_loss(self, iteration: int, loss_map: Dict[str, float]):
- loss_spike_result = []
- for task, loss in loss_map.items():
- if task in self._last_loss:
- if loss > self._last_loss[task] * 3:
- # loss spike!
- loss_spike_result.append(
- {
- "prev": self._last_loss[task],
- "curr": loss,
- "task": task,
- }
- )
- self._last_loss[task] = float(loss)
- if len(loss_spike_result) > 0:
- self._write_log(iteration, self._last_data[-1], loss_spike_result)
-
- def _write_log(self, iteration: int, data: Any, result: List[Dict[str, Any]]):
- while True:
- try:
- with open(self._log_path, "a", encoding="utf-8") as fp:
- fp.write("=" * 20)
- fp.write("\nloss spike at {}\n".format(iteration))
- fp.write("{}\n".format(json.dumps(result, indent=4, ensure_ascii=False)))
- fp.write("data: \n")
- for d in data:
- fp.write("{}\n".format(json.dumps(d, indent=4, ensure_ascii=False)))
- fp.write("\n\n")
- break
- except Exception as e:
- print("cannot output log to the file {}", self._log_path)
-
-
-def pretrain(
- args,
- tokenizer: CPM9GTokenizer,
- model: CPM9G,
- optimizer,
- lr_scheduler: bmt.lr_scheduler.WarmupLRScheduler,
-):
- average_time = bmt.utils.AverageRecorder()
- loss_func = bmt.loss.FusedCrossEntropy(ignore_index=-100)
- optim_manager = bmt.optim.OptimManager(
- loss_scale=None if args.bf16 else args.loss_scale,
- loss_scale_steps=args.loss_scale_steps,
- loss_scale_factor=2,
- max_loss_scale=args.max_loss_scale,
- min_loss_scale=args.min_loss_scale,
- )
- optim_manager.add_optimizer(optimizer, lr_scheduler)
-
- start_step = args.start_step
- lsd = LossSpikeDetector("./log/debug/spile.%d.log" % bmt.rank())
-
- if args.tensorboard is not None and bmt.rank() == 0:
- import distutils.version # noqa: F401
-
- from tensorboardX import SummaryWriter
-
- if not os.path.exists(args.tensorboard):
- os.makedirs(args.tensorboard)
- writer = SummaryWriter(log_dir=args.tensorboard)
-
- if args.log_dir is not None and bmt.rank() == 0:
- log_mgr = LogManager(args.log_dir)
-
- global_token_pass = 0.0
- global_world_size = bmt.world_size()
- dataloader = MixedDataset(args.dataset, args.batch_size, args.max_length, tokenizer, unpad=(args.flash == "cuda"))
-
- if args.load is not None:
- dataset_states_path = args.load.replace(".pt", ".data")
- if os.path.exists(dataset_states_path):
- start = time.time()
- bmt.print_rank("start to load data ckpt")
- dataset_states = torch.load(dataset_states_path)
- logger.info("load data ckpt in {:.2f}s".format(time.time() - start))
-
- start = time.time()
- missing = dataloader.load_state_dict(dataset_states)
- logger.info("load state dict in {:.2f}s".format(time.time() - start))
- if len(missing) > 0:
- bmt.print_rank("Missing keys when loading dataset states: ", missing)
- else:
- bmt.print_rank("cannot find data ckpt {}".format(dataset_states_path))
-
- dataloader.start()
- bmt.print_rank("finish dataset start")
- try:
- total = 0
- hash = {}
- for iteration, data in enumerate(dataloader):
- iteration = iteration + start_step + 1
- input_ids = torch.from_numpy(data["inputs"]).cuda().to(torch.int32)
- input_length = torch.from_numpy(data["length"]).cuda().to(torch.int32)
- targets = torch.from_numpy(data["target"]).cuda().to(torch.int32)
- task_ids = torch.from_numpy(data["task_ids"]).cuda().to(torch.int32)
- task_names = data["task_names"]
- lsd.update_data(data["raw_data"])
- if args.flash == "cuda":
- cu_seqlens = torch.from_numpy(data["cu_seqlens"]).cuda().to(torch.int32)
- max_seqlen = data["max_seqlen"]
- position_ids = torch.from_numpy(data["position_ids"]).cuda().to(torch.int32)
- else:
- input_ids = torch.from_numpy(data["inputs"]).cuda().to(torch.int32)
- input_context = torch.zeros_like(input_ids).cuda().bool()
- input_span = torch.from_numpy(data["spans"]).cuda().to(torch.int32)
-
- # ===========
- optim_manager.zero_grad()
- # torch.cuda.empty_cache()
- mem_usage = {}
- tim_usage = {}
- mem_usage, tim_usage = add_mem_time("init", mem_usage, tim_usage)
-
- # bmt.print_rank(torch.cuda.max_memory_allocated())
- # ===========
- if args.flash == "cuda":
- logits, _ = model(
- input_ids,
- cu_seqlens=cu_seqlens,
- max_seqlen=max_seqlen,
- position_ids=position_ids,
- )
- else:
- logits, _ = model(
- input_ids,
- input_length,
- input_context,
- input_span,
- )
- mem_usage, tim_usage = add_mem_time("forward_1", mem_usage, tim_usage)
- loss = loss_func(logits.view(-1, logits.size(-1)), targets.view(-1))
- global_loss = bmt.sum_loss(loss).item()
- mem_usage, tim_usage = add_mem_time("forward", mem_usage, tim_usage)
-
- # bmt.print_rank(torch.cuda.max_memory_allocated())
- # ===========
- optim_manager.backward(loss)
- mem_usage, tim_usage = add_mem_time("backward", mem_usage, tim_usage)
-
- # bmt.print_rank(torch.cuda.max_memory_allocated())
- # ===========
- grad_norm = optim_manager.clip_grad_norm(optimizer.param_groups, args.clip_grad, norm_type=2)
- optim_manager.step()
- mem_usage, tim_usage = add_mem_time("optim", mem_usage, tim_usage)
- # bmt.print_rank(torch.cuda.max_memory_allocated())
-
- # ==========
- iter_time = tim_usage["optim"] - tim_usage["init"]
- average_time.record(iter_time)
-
- with torch.no_grad():
- task_num = len(task_names)
- targets_tmp = targets.expand(task_num, -1, -1)
- task = torch.arange(task_num, dtype=torch.int32, device="cuda")[:, None, None]
- targets_tmp = torch.where(
- task_ids == task,
- targets_tmp,
- torch.scalar_tensor(-100, dtype=torch.int32, device="cuda"),
- )
-
- task_loss_map: Dict[str, float] = {}
- task_loss_tot: Dict[str, float] = {}
- for i in range(task_num):
- task_loss_map[task_names[i]] = loss_func(
- logits.view(-1, logits.size(-1)), targets_tmp[i, :].view(-1)
- ).item()
- task_loss_tot[task_names[i]] = (targets_tmp[i, :].view(-1) >= 0).sum().float().item()
- gatherd_task_loss_map: List[Dict[str, float]] = allgather_objects(task_loss_map)
- gatherd_task_loss_tot: List[Dict[str, float]] = allgather_objects(task_loss_tot)
-
- global_task_loss_map: Dict[str, Union[List[float], float]] = {}
- global_task_loss_tot: Dict[str, Union[List[float], float]] = {}
-
- for idx, local_task_loss_map in enumerate(gatherd_task_loss_map):
- for task_name, task_loss in local_task_loss_map.items():
- if task_name not in global_task_loss_map:
- global_task_loss_map[task_name] = []
- global_task_loss_map[task_name].append(task_loss)
- for task_name, task_tot in gatherd_task_loss_tot[idx].items():
- if task_name not in global_task_loss_tot:
- global_task_loss_tot[task_name] = []
- global_task_loss_tot[task_name].append(task_tot)
-
- task_loss_map = {}
- for task_name in sorted(list(global_task_loss_map.keys())):
- avg_loss = 0.0
- sum_token = sum(global_task_loss_tot[task_name])
- for loss, token in zip(global_task_loss_map[task_name], global_task_loss_tot[task_name]):
- avg_loss += loss * token / sum_token
- task_loss_map[task_name] = avg_loss
-
- local_total_rate = torch.Tensor([input_length.float().mean() / args.max_length]).cuda()
- local_total_rate = bmt.sum_loss(local_total_rate).item()
- global_token_pass += global_world_size * local_total_rate * args.max_length * args.batch_size
- avg_time = average_time.value
- lsd.update_loss(iteration, task_loss_map)
-
- for task_id in data["task_ids"]:
- for task in task_id:
- if task != -1:
- if not data["task_names"][task] in hash:
- hash[data["task_names"][task]] = 0
- hash[data["task_names"][task]] += 1.0
- total += 1.0
-
- gathered_hash = allgather_objects(hash)
- sum_total = sum(allgather_objects(total))
-
- final_hash = defaultdict(int)
- for local_hash in gathered_hash:
- for task, num in local_hash.items():
- final_hash[task] += num
-
- # for i in final_hash:
- # bmt.print_rank(i, final_hash[i] / sum_total)
- # bmt.print_rank("=========================================")
-
- train_info = {
- "time": tim_usage["init"],
- "iteration": iteration,
- "loss": global_loss,
- "lr": lr_scheduler.current_lr,
- "lr_scale": int(optim_manager.loss_scale),
- "time_usage": tim_usage,
- "mem_usage": mem_usage,
- "avg_time": avg_time,
- "token_max": local_total_rate,
- "token_pass": global_token_pass,
- "throughout": args.max_length * args.batch_size * local_total_rate / avg_time,
- "grad_norm": grad_norm.item(),
- "mask_max": ((targets >= 0).sum(-1).float().mean() / args.max_length).item(),
- "num_gpus": global_world_size,
- "task_loss": task_loss_map,
- }
- # bmt.print_rank(time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()))
- bmt.print_rank(
- (
- "| Iter: {:6d} | loss: {:.4f} | lr: {:.4e}, scale: {:10.4f} | avg_time: {:.4f}; cur_time:{:.4f}={:.4f}+{:.4f} |"
- + " token/max: {:.4f} | mask/max: {:.4f} | grad_norm: {:.4f} | mem: {:.2f} |"
- ).format(
- iteration,
- global_loss,
- lr_scheduler.current_lr,
- int(optim_manager.loss_scale),
- iter_time,
- tim_usage["optim"] - tim_usage["init"],
- tim_usage["backward"] - tim_usage["init"],
- tim_usage["optim"] - tim_usage["backward"],
- input_length.float().mean() / args.max_length / (args.batch_size if args.flash == "cuda" else 1),
- (targets >= 0).sum(-1).float().mean()
- / args.max_length
- / (args.batch_size if args.flash == "cuda" else 1),
- grad_norm,
- max(mem_usage["forward"][1], mem_usage["backward"][1]),
- )
- )
-
- bmt.print_rank(
- "| "
- + " | ".join(["{}: {:.4f}".format(task_name, loss) for task_name, loss in task_loss_map.items()])
- + " |"
- )
-
- if iteration % args.inspect_iters == 0:
- model_inspect = bmt.inspect.inspect_model(model, "*")
- bmt.print_rank(bmt.inspect.format_summary(model_inspect))
- train_info["model_inspect"] = model_inspect
-
- if args.log_dir is not None and bmt.rank() == 0:
- log_mgr.write(**train_info)
- if args.tensorboard is not None and bmt.rank() == 0:
- writer.add_scalar("Loss/train", global_loss, iteration)
- writer.add_scalar("Optimizer/lr", lr_scheduler.current_lr, iteration)
- writer.add_scalar("Optimizer/scale", optim_manager.loss_scale, iteration)
- writer.add_scalar("Optimizer/grad_norm", grad_norm.item(), iteration)
- for task_name, loss in task_loss_map.items():
- writer.add_scalar("Loss/train/{}".format(task_name), loss, iteration)
-
- # -------- save file. If need to backup by Klara platform, use export.xx_save --------
- if args.save is not None and iteration % args.save_iters == 0:
- exporter.export(model, dataloader, optimizer, iteration, args, final_save=False)
-
- if iteration >= args.train_iters:
- break
-
- except Exception as e:
- print(f"train loop err: {e}")
- raise e
- finally:
- dataloader.close()
- exporter.export(model, dataloader, optimizer, -1, args, final_save=False)
-
-
-def main():
- args = initialize()
- tokenizer, model, optimizer, lr_scheduler = setup_model_and_optimizer(args)
- bmt.print_rank("finish loading")
- pretrain(args, tokenizer, model, optimizer, lr_scheduler)
-
-
-if __name__ == "__main__":
- main()
diff --git a/9G-Train/apps/cpm9g/pretrain_cpm9g_11b.sh b/9G-Train/apps/cpm9g/pretrain_cpm9g_11b.sh
deleted file mode 100644
index 8c1a754..0000000
--- a/9G-Train/apps/cpm9g/pretrain_cpm9g_11b.sh
+++ /dev/null
@@ -1,60 +0,0 @@
-#! /bin/bash
-#SBATCH --nodes=1
-#SBATCH --ntasks-per-node=8
-#SBATCH --gres=gpu:8
-#SBATCH --cpus-per-task=8
-#SBATCH --mem=512GB
-
-# use 8 GPU for example, pretrain may need 32 GPU
-export MASTER_ADDR=`hostname`
-export MASTER_PORT=12345
-
-mkdir -p /home/${USERNAME}/logs/debug
-mkdir -p /home/${USERNAME}/logs/tensorboard/cpm9g/
-
-cd apps/cpm9g
-CONFIG_NAME="config/11b"
-# --------------- 运行参数 ---------------
-OPTS=""
-OPTS+=" --model-config ${CONFIG_NAME}/config.json"
-OPTS+=" --vocab ${CONFIG_NAME}/vocab.txt"
-OPTS+=" --batch-size 4"
-OPTS+=" --train-iters 400000"
-OPTS+=" --save-iters 250"
-OPTS+=" --save-name cpm9g_checkpoint"
-OPTS+=" --max-length 4096"
-OPTS+=" --lr 1.5e-5"
-OPTS+=" --inspect-iters 100"
-OPTS+=" --warmup-iters 2000"
-OPTS+=" --lr-decay-style noam"
-OPTS+=" --weight-decay 0.1"
-OPTS+=" --clip-grad 1.0"
-OPTS+=" --loss-scale 1048576"
-OPTS+=" --loss-scale-steps 32"
-OPTS+=" --offload"
-OPTS+=" --flash cuda"
-# OPTS+=" --load-grad"
-
-# --------------- 写文件路径 ---------------
-## checkpoint
-OPTS+=" --save /home/${USERNAME}/checkpoints/cpm9g/"
-OPTS+=" --save-model /home/${USERNAME}/models/cpm9g/"
-
-## logs,/local/logs 等价于 /data/logs(软链)
-OPTS+=" --log-dir /home/${USERNAME}/logs/train/"
-OPTS+=" --tensorboard /home/${USERNAME}/tensorboard/cpm9g/"`date +"%Y%m%d%H%M%S"`
-
-# --------------- 读文件路径 ---------------
-OPTS+=" --dataset config/datasets.json"
-OPTS+=" --load ${CHECKPOINT}"
-OPTS+=" --start-step 1"
-
-# --------------- 透传参数 ---------------
-OPTS+=" $@"
-
-# --------------- 最终指令 ---------------
-
-CMD="torchrun --nnodes=1 --nproc_per_node=8 --rdzv_id=1 --rdzv_backend=c10d --rdzv_endpoint=${MASTER_ADDR}:${MASTER_PORT} pretrain_cpm9g.py ${OPTS}"
-echo "${CMD}"
-
-$CMD
diff --git a/9G-Train/apps/cpm9g/pretrain_cpm9g_7b.sh b/9G-Train/apps/cpm9g/pretrain_cpm9g_7b.sh
deleted file mode 100644
index e140f3b..0000000
--- a/9G-Train/apps/cpm9g/pretrain_cpm9g_7b.sh
+++ /dev/null
@@ -1,59 +0,0 @@
-#! /bin/bash
-#SBATCH --nodes=1
-#SBATCH --ntasks-per-node=8
-#SBATCH --gres=gpu:8
-#SBATCH --cpus-per-task=8
-
-# use 8 GPU for example, pretrain may need 32 GPU
-export MASTER_ADDR=`hostname`
-export MASTER_PORT=12345
-
-mkdir -p /home/${USERNAME}/logs/debug
-mkdir -p /home/${USERNAME}/logs/tensorboard/cpm9g/
-
-cd apps/cpm9g
-CONFIG_NAME="config/7b"
-# --------------- 运行参数 ---------------
-OPTS=""
-OPTS+=" --model-config ${CONFIG_NAME}/config.json"
-OPTS+=" --vocab ${CONFIG_NAME}/vocab.txt"
-OPTS+=" --batch-size 4"
-OPTS+=" --train-iters 400000"
-OPTS+=" --save-iters 250"
-OPTS+=" --save-name cpm9g_checkpoint"
-OPTS+=" --max-length 4096"
-OPTS+=" --lr 1.5e-5"
-OPTS+=" --inspect-iters 100"
-OPTS+=" --warmup-iters 2000"
-OPTS+=" --lr-decay-style noam"
-OPTS+=" --weight-decay 0.1"
-OPTS+=" --clip-grad 1.0"
-OPTS+=" --loss-scale 1048576"
-OPTS+=" --loss-scale-steps 32"
-OPTS+=" --offload"
-OPTS+=" --flash cuda"
-# OPTS+=" --load-grad"
-
-# --------------- 写文件路径 ---------------
-## checkpoint
-OPTS+=" --save /home/${USERNAME}/checkpoints/cpm9g/"
-OPTS+=" --save-model /home/${USERNAME}/models/cpm9g/"
-
-## logs,/local/logs 等价于 /data/logs(软链)
-OPTS+=" --log-dir /home/${USERNAME}/logs/train/"
-OPTS+=" --tensorboard /home/${USERNAME}/tensorboard/cpm9g/"`date +"%Y%m%d%H%M%S"`
-
-# --------------- 读文件路径 ---------------
-OPTS+=" --dataset config/datasets.json"
-OPTS+=" --load ${CHECKPOINT}"
-OPTS+=" --start-step 1"
-
-# --------------- 透传参数 ---------------
-OPTS+=" $@"
-
-# --------------- 最终指令 ---------------
-
-CMD="torchrun --nnodes=1 --nproc_per_node=8 --rdzv_id=1 --rdzv_backend=c10d --rdzv_endpoint=${MASTER_ADDR}:${MASTER_PORT} pretrain_cpm9g.py ${OPTS}"
-echo "${CMD}"
-
-$CMD
diff --git a/9G-Train/apps/cpm9g/sft_cpm9g.py b/9G-Train/apps/cpm9g/sft_cpm9g.py
deleted file mode 100644
index caa9190..0000000
--- a/9G-Train/apps/cpm9g/sft_cpm9g.py
+++ /dev/null
@@ -1,484 +0,0 @@
-# coding=utf-8
-# Copyright 2022 The OpenBMB team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-import inspect
-import json
-import math
-import os
-import re
-import sys
-import time
-from typing import Any
-from typing import Dict
-from typing import List
-from typing import Union
-
-import bmtrain as bmt
-import torch
-
-sys.path.insert(0, "/home/wangshuo1/code/9G-Train")
-from cpm.arguments import get_args
-from cpm.cpm9g.models import CPM9G
-from cpm.cpm9g.models import CPM9GConfig
-from cpm.cpm9g.tokenizers import CPM9GTokenizer
-from cpm.cpm9g.training_tasks import FinetuneDataset
-from cpm.utils import allgather_objects
-from cpm.utils import logger
-import shutil
-
-def get_tokenizer(args):
- tokenizer = CPM9GTokenizer(path=args.vocab)
- return tokenizer
-
-
-def get_model(args):
- config = CPM9GConfig.from_json_file(args.model_config)
- if args.flash == "none":
- config.use_flash_attn = False
- else:
- config.use_flash_attn = True
- if args.flash == "1d":
- config.flash_attn_mask_shape = "1d"
- else:
- config.flash_attn_mask_shape = "2d"
- if args.flash == "triton":
- config.flash_impl = "triton"
- elif args.flash == "cuda":
- config.flash_impl = "cuda"
- model = CPM9G(config)
- if args.load is not None:
- bmt.init_parameters(model)
- bmt.synchronize()
- bmt.print_rank("args.load is not None, start to load checkpoints" + args.load)
- bmt.load(model, args.load, strict=False)
-
- model_inspect = bmt.inspect.inspect_model(model, "*")
- bmt.print_rank(bmt.inspect.format_summary(model_inspect))
- else:
- bmt.print_rank("args.load is None, start to initialize parameters")
- bmt.init_parameters(model)
- return model
-
-
-def get_optimizer(args, model):
- if args.offload:
- optimizer = bmt.optim.AdamOffloadOptimizer(
- model.parameters(), betas=(0.9, 0.95), weight_decay=args.weight_decay
- )
- else:
- optimizer = bmt.optim.AdamOptimizer(model.parameters(), betas=(0.9, 0.95), weight_decay=args.weight_decay)
- if args.load is not None and args.load_grad:
- start = time.time()
- print(
- sum(
- [
- 1
- if i.find(".opt") != -1 and i.find("-{}.rank".format(args.start_step % (args.save_iters * 5))) != -1
- else 0
- for i in os.listdir(args.save)
- ]
- )
- )
- if (
- sum(
- [
- 1
- if i.find(".opt") != -1 and i.find("-{}.rank".format(args.start_step % (args.save_iters * 5))) != -1
- else 0
- for i in os.listdir(args.save)
- ]
- )
- == bmt.world_size()
- ):
- file_name = os.path.join(
- args.save,
- args.save_name + "-{}.rank-{}.opt".format(args.start_step % (args.save_iters * 5), bmt.rank()),
- )
- print(file_name)
- if os.path.exists(file_name):
- print("start to load grad ckpt {}".format(file_name))
- states = torch.load(file_name)
- optimizer.load_state_dict(states)
- logger.info("load grad in {:.2f}s".format(time.time() - start))
- return optimizer
-
-
-class Cosine(bmt.lr_scheduler.WarmupLRScheduler):
- r"""
- After a warmup period during which learning rate increases linearly between 0 and the start_lr,
- The decay period performs :math:`\text{lr}=\text{start_lr}\times \dfrac{1+\cos \left( \pi \cdot \dfrac{\text{num_iter}-\text{warmup_iter}}{\text{end_iter}-\text{warmup_iter}}\right)}{2}`
- """
-
- def get_lr_warmup(self, num_iter) -> float:
- return self.start_lr * num_iter / self.warmup_iter
-
- def get_lr_decay(self, num_iter) -> float:
- progress = (num_iter - self.warmup_iter) / max(1, (self.end_iter - self.warmup_iter))
- return max(self.start_lr * 0.1, self.start_lr * (0.1 + 0.45 * (1.0 + math.cos(progress * math.pi))))
-
-
-def get_learning_rate_scheduler(args, optimizer):
- if args.lr_decay_iters is None:
- args.lr_decay_iters = args.train_iters
- # lr_scheduler = bmt.lr_scheduler.Noam(
- lr_scheduler = Cosine(
- optimizer,
- start_lr=args.lr,
- warmup_iter=args.warmup_iters,
- end_iter=args.lr_decay_iters,
- num_iter=args.start_step,
- )
- return lr_scheduler
-
-
-def setup_model_and_optimizer(args):
- start = time.time()
- model = get_model(args)
- logger.info("load model in {:.2f}s".format(time.time() - start))
-
- start = time.time()
- tokenizer = get_tokenizer(args)
- bmt.synchronize()
- logger.info("load tokenizer in {:.2f}s".format(time.time() - start))
-
- start = time.time()
- optimizer = get_optimizer(args, model)
- lr_scheduler = get_learning_rate_scheduler(args, optimizer)
- bmt.synchronize()
- logger.info("load lr_scheduler in {:.2f}s".format(time.time() - start))
-
- return tokenizer, model, optimizer, lr_scheduler
-
-
-def initialize():
- args = get_args(finetune=True)
-
- # hack
- if "checkpointing" in inspect.signature(bmt.init_distributed).parameters:
- bmt.init_distributed(checkpointing=False, seed=args.seed)
- else:
- bmt.init_distributed(seed=args.seed)
- if args.save is not None:
- os.makedirs(args.save, exist_ok=True)
- # if args.load is not None:
- # if args.start_step == 0:
- # args.start_step = (int)(re.search("(\d+).pt", args.load)[1])
- return args
-
-
-def see_memory(detail=False):
- if detail:
- res = torch.cuda.memory_summary()
- else:
- res = (
- round(torch.cuda.memory_allocated() / (1024 * 1024 * 1024), 2),
- round(torch.cuda.max_memory_allocated() / (1024 * 1024 * 1024), 2),
- )
- torch.cuda.reset_peak_memory_stats()
- return res
-
-
-def add_mem_time(info, mem_usage, tim_usage):
- torch.cuda.synchronize()
- mem_usage[info] = see_memory()
- tim_usage[info] = time.time()
- return mem_usage, tim_usage
-
-
-class LossSpikeDetector:
- def __init__(self, log_path: str) -> None:
- self._last_loss: Dict[str, float] = {}
- self._last_data: List[Any] = [None]
- self._log_path = log_path
-
- def update_data(self, data: Any):
- self._last_data.append(data)
- if len(self._last_data) > 2:
- self._last_data = self._last_data[-2:]
-
- def update_loss(self, iteration: int, loss_map: Dict[str, float]):
- loss_spike_result = []
- for task, loss in loss_map.items():
- if task in self._last_loss:
- if loss > self._last_loss[task] * 3:
- # loss spike!
- loss_spike_result.append(
- {
- "prev": self._last_loss[task],
- "curr": loss,
- "task": task,
- }
- )
- self._last_loss[task] = float(loss)
- if len(loss_spike_result) > 0:
- self._write_log(iteration, self._last_data[-1], loss_spike_result)
-
- def _write_log(self, iteration: int, data: Any, result: List[Dict[str, Any]]):
- return
- with open(self._log_path, "a", encoding="utf-8") as fp:
- fp.write("=" * 20)
- fp.write("\nloss spike at {}\n".format(iteration))
- fp.write("{}\n".format(json.dumps(result, indent=4, ensure_ascii=False)))
- fp.write("data: \n")
- for d in data:
- fp.write("{}\n".format(json.dumps(d, indent=4, ensure_ascii=False)))
- fp.write("\n\n")
-
-
-def finetune(
- args,
- bin_file: str,
- tokenizer: CPM9GTokenizer,
- model: CPM9G,
- optimizer,
- lr_scheduler: bmt.lr_scheduler.WarmupLRScheduler,
-):
- average_time = bmt.utils.AverageRecorder()
- loss_func = bmt.loss.FusedCrossEntropy(ignore_index=-100)
- optim_manager = bmt.optim.OptimManager(loss_scale=args.loss_scale, loss_scale_steps=args.loss_scale_steps, loss_scale_factor=2, max_loss_scale=args.max_loss_scale, min_loss_scale=args.min_loss_scale,)
- optim_manager.add_optimizer(optimizer, lr_scheduler)
-
- if args.tensorboard is not None and bmt.rank() == 0:
- import distutils.version # noqa: F401
- from tensorboardX import SummaryWriter
- if not os.path.exists(args.tensorboard):
- os.makedirs(args.tensorboard)
- writer = SummaryWriter(log_dir=args.tensorboard)
-
- global_token_pass = 0.0
- global_world_size = bmt.world_size()
-
- for epoch in range(args.epoch):
- epoch = epoch + 1
- last_data = None
- dataloader = FinetuneDataset(
- bin_file, args.batch_size, args.max_length, tokenizer, unpad=(args.flash == "cuda"), task_name="task", drop_last=True
- )
- optim_manager.zero_grad()
- for iteration, data in enumerate(dataloader):
- iteration = iteration + 1
- skip_this_batch = False
- if data is None:
- if last_data is None:
- raise RuntimeError(
- "Dataset is too small, please use a smaller batch size or sequence length!"
- )
- data = last_data # use last data
- skip_this_batch = True
- else:
- last_data = data
-
- assert data["inputs"].shape[0] == args.batch_size
- input_ids = torch.from_numpy(data["inputs"]).cuda().to(torch.int32)
- input_length = torch.from_numpy(data["length"]).cuda().to(torch.int32)
- targets = torch.from_numpy(data["target"]).cuda().long()
- # bmt.print_rank(input_ids[0].tolist())
- # bmt.print_rank(targets[0].tolist())
- # bmt.print_rank(data["spans"].tolist())
- # bmt.print_rank(tokenizer.decode(input_ids[0]))
- # bmt.print_rank(tokenizer.path)
- # bmt.synchronize()
- # exit()
- task_ids = torch.from_numpy(data["task_ids"]).cuda().to(torch.int32)
- task_names = data["task_names"]
- if args.flash == "cuda":
- cu_seqlens = torch.from_numpy(data["cu_seqlens"]).cuda().to(torch.int32)
- max_seqlen = data["max_seqlen"]
- position_ids = torch.from_numpy(data["position_ids"]).cuda().to(torch.int32)
- else:
- input_context = torch.zeros_like(input_ids).cuda().bool()
- input_span = torch.from_numpy(data["spans"]).cuda().to(torch.int32)
-
- # ===========
- # optim_manager.zero_grad()
- # torch.cuda.empty_cache()
- mem_usage = {}
- tim_usage = {}
- mem_usage, tim_usage = add_mem_time("init", mem_usage, tim_usage)
-
- # ===========
- if args.flash == "cuda":
- logits, _ = model(
- input_ids,
- cu_seqlens=cu_seqlens,
- max_seqlen=max_seqlen,
- position_ids=position_ids,
- )
- else:
- logits, _ = model(
- input_ids,
- input_length,
- input_context,
- input_span,
- )
- mem_usage, tim_usage = add_mem_time("forward_1", mem_usage, tim_usage)
- loss = loss_func(logits.view(-1, logits.size(-1)), targets.view(-1))
- if skip_this_batch:
- loss = loss * 0
- mem_usage, tim_usage = add_mem_time("forward", mem_usage, tim_usage)
-
- # ===========
- optim_manager.backward(loss)
- mem_usage, tim_usage = add_mem_time("backward", mem_usage, tim_usage)
-
- # ===========
- if iteration % args.gradient_accumulation_steps == 0:
- grad_norm = optim_manager.clip_grad_norm(optimizer.param_groups, args.clip_grad, norm_type=2)
- optim_manager.step()
- mem_usage, tim_usage = add_mem_time("optim", mem_usage, tim_usage)
- optim_manager.zero_grad()
- else:
- grad_norm = None
- mem_usage, tim_usage = add_mem_time("optim", mem_usage, tim_usage)
-
- # ==========
- iter_time = tim_usage["optim"] - tim_usage["init"]
- average_time.record(iter_time)
-
- with torch.no_grad():
- task_num = len(task_names)
- targets_tmp = targets.expand(task_num, -1, -1)
- task = torch.arange(task_num, dtype=torch.long, device="cuda")[:, None, None]
- targets_tmp = torch.where(
- task_ids == task,
- targets_tmp,
- torch.scalar_tensor(-100, dtype=torch.long, device="cuda"),
- )
-
- task_loss_map: Dict[str, float] = {}
- if not skip_this_batch:
- for i in range(task_num):
- task_loss = loss_func(logits.view(-1, logits.size(-1)), targets_tmp[i, :].view(-1))
- task_loss_map[task_names[i]] = task_loss.item()
- gatherd_task_loss_map: List[Dict[str, float]] = allgather_objects(task_loss_map)
-
- global_task_loss_map: Dict[str, Union[List[float], float]] = {}
- for local_task_loss_map in gatherd_task_loss_map:
- for task_name, task_loss in local_task_loss_map.items():
- if task_name not in global_task_loss_map:
- global_task_loss_map[task_name] = []
- global_task_loss_map[task_name].append(task_loss)
-
- task_loss_map = {}
- for task_name in sorted(list(global_task_loss_map.keys())):
- avg_loss = sum(global_task_loss_map[task_name]) / len(global_task_loss_map[task_name])
- task_loss_map[task_name] = avg_loss
-
- local_total_rate = torch.Tensor([input_length.float().mean() / args.max_length]).cuda()
- local_total_rate = bmt.sum_loss(local_total_rate).item()
- global_token_pass += global_world_size * local_total_rate * args.max_length * args.batch_size
- avg_time = average_time.value
-
- train_info = {
- "time": tim_usage["init"],
- "epoch": epoch,
- "iteration": iteration,
- "loss": task_loss_map[args.task_name],
- "lr": lr_scheduler.current_lr,
- "lr_scale": int(optim_manager.loss_scale),
- "time_usage": tim_usage,
- "mem_usage": mem_usage,
- "avg_time": avg_time,
- "token_max": local_total_rate,
- "token_pass": global_token_pass,
- "throughout": args.max_length * args.batch_size * local_total_rate / avg_time,
- "grad_norm": 0 if grad_norm == None else grad_norm.item(),
- "mask_max": ((targets >= 0).sum(-1).float().mean() / args.max_length).item(),
- "num_gpus": global_world_size,
- "task_loss": task_loss_map,
- }
- # bmt.print_rank(time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()))
- bmt.print_rank(
- (
- "| Epoch: {:3d} | Iter: {:6d}/{:6d} | loss: {:.4f} | lr: {:.6e} | scale: {:10.0f} | time: {:.1f} |"
- + " token/max: {:.3f} | mask/max: {:.3f} | grad_norm: {:.3f}"
- ).format(
- epoch,
- iteration,
- args.train_iters,
- task_loss_map[args.task_name],
- lr_scheduler.current_lr,
- int(optim_manager.loss_scale),
- avg_time,
- input_length.float().mean() / args.max_length,
- (targets >= 0).sum(-1).float().mean() / args.max_length,
- 0 if grad_norm == None else grad_norm,
- )
- )
-
- bmt.print_rank(
- "| "
- + " | ".join(["{}: {:.4f}".format(task_name, loss) for task_name, loss in task_loss_map.items()])
- + " |"
- )
- if iteration % args.inspect_iters == 0:
- model_inspect = bmt.inspect.inspect_model(model, "*")
- bmt.print_rank(bmt.inspect.format_summary(model_inspect))
- train_info["model_inspect"] = model_inspect
-
- # save_folder_name = f"{args.save}{epoch}{iteration}"
- # model_fname = os.path.join(save_folder_name, f"{args.save_name}-iter-{iteration}.pt")
- # os.makedirs(os.path.dirname(model_fname), exist_ok=True)
- # if bmt.rank() == 0:
- # shutil.copy(args.model_config, os.path.join(save_folder_name, "config.json"))
- # shutil.copy(args.vocab, os.path.join(save_folder_name, "vocabs.txt"))
-
- if args.tensorboard is not None and bmt.rank() == 0:
- writer.add_scalar(f"Loss/train/{epoch}", task_loss_map[args.task_name], iteration)
- writer.add_scalar(f"Optimizer/lr/{epoch}", lr_scheduler.current_lr, iteration)
- writer.add_scalar(f"Optimizer/scale/{epoch}", optim_manager.loss_scale, iteration)
- writer.add_scalar(f"Optimizer/grad_norm/{epoch}", 0 if grad_norm == None else grad_norm.item(), iteration)
-
- # if iteration % 10 == 0:
- # save_folder_name = f"{args.save}{epoch}"
- # model_fname = os.path.join(save_folder_name, f"{args.save_name}-epoch-{epoch}.pt")
- # os.makedirs(os.path.dirname(model_fname), exist_ok=True)
- # bmt.save(model, model_fname)
- # if bmt.rank() == 0:
- # shutil.copy(args.model_config, os.path.join(save_folder_name, "config.json"))
- # shutil.copy(args.vocab, os.path.join(save_folder_name, "vocabs.txt"))
-
- save_folder_name = f"{args.save}-epoch-{epoch}"
- model_fname = os.path.join(save_folder_name, f"{args.save_name}-epoch-{epoch}.pt")
- os.makedirs(os.path.dirname(model_fname), exist_ok=True)
- bmt.save(model, model_fname)
- if bmt.rank() == 0:
- shutil.copy(args.model_config, os.path.join(save_folder_name, "config.json"))
- shutil.copy(args.vocab, os.path.join(save_folder_name, "vocabs.txt"))
-
-
-def main():
- args = initialize()
- # To Be Specified
- bin_file = args.dataset
- bmt.print_rank(f"dataset: {bin_file}")
-
- tokenizer = get_tokenizer(args)
- dataloader = FinetuneDataset(
- bin_file, args.batch_size, args.max_length, tokenizer, unpad=(args.flash == "cuda"), task_name="task", drop_last=True
- )
- bmt.print_rank(f"#batch: {len(dataloader)}")
- total_steps = len(dataloader) * args.epoch
-
- setattr(args, 'train_iters', int(total_steps))
- setattr(args, 'warmup_iters', max(int(total_steps*0.02), args.warmup_iters))
- bmt.print_rank(json.dumps(vars(args), indent=2, sort_keys=True))
- tokenizer, model, optimizer, lr_scheduler = setup_model_and_optimizer(args)
-
- bmt.print_rank("finish loading")
- finetune(args, bin_file, tokenizer, model, optimizer, lr_scheduler)
-
-
-if __name__ == "__main__":
- main()
diff --git a/9G-Train/apps/cpm9g/sft_cpm9g_11b.sh b/9G-Train/apps/cpm9g/sft_cpm9g_11b.sh
deleted file mode 100644
index 9ca7a07..0000000
--- a/9G-Train/apps/cpm9g/sft_cpm9g_11b.sh
+++ /dev/null
@@ -1,55 +0,0 @@
-#! /bin/bash
-#SBATCH --partition=gpu3-1
-#SBATCH --nodes=1
-#SBATCH --ntasks-per-node=8
-#SBATCH --gres=gpu:8
-#SBATCH --cpus-per-task=8
-
-export MASTER_ADDR=g3002
-export MASTER_PORT=12345
-
-CPM_PATH="/data/groups/QY_LLM_Core/projects/202311-release/Models/11B-Chat/9G-Train"
-CONFIG_NAME="${CPM_PATH}/apps/cpm9g/config/11b"
-EXP_PATH=./exp
-mkdir -p $EXP_PATH
-MODEL_NAME="cpm9g-11b-sft"
-
-OPTS=""
-OPTS+=" --model-config ${CONFIG_NAME}/config.json"
-OPTS+=" --vocab ${CONFIG_NAME}/vocab.txt"
-
-OPTS+=" --train-iters 10000"
-OPTS+=" --inspect-iters 200"
-OPTS+=" --warmup-iters 500"
-
-OPTS+=" --lr-decay-style cosine"
-OPTS+=" --weight-decay 0.1"
-OPTS+=" --clip-grad 1.0"
-OPTS+=" --loss-scale 1048576"
-OPTS+=" --max-loss-scale 33554432"
-OPTS+=" --min-loss-scale 1"
-OPTS+=" --loss-scale-steps 32"
-
-OPTS+=" --offload"
-OPTS+=" --batch-size 4"
-OPTS+=" --max-length 4096"
-OPTS+=" --lr 2e-5"
-OPTS+=" --start-step 0"
-OPTS+=" --epoch 8"
-OPTS+=" --load /data/groups/QY_LLM_Core/models/20231010/11b-base/11b.pt"
-OPTS+=" --dataset /data/groups/QY_LLM_Core/datasets/sft/20231025/merge_qy_sft_bin"
-# TODO 这些 /data 在启元机器上需要改成 /home 下的路径
-OPTS+=" --save ${EXP_PATH}/checkpoints"
-OPTS+=" --save-name ${MODEL_NAME}"
-# OPTS+=" --tensorboard /data/logs/tensorboard/${MODEL_NAME}/${CUR_DATE}/"
-# OPTS+=" --flash triton"
-# OPTS+=" --flash cuda"
-# OPTS+=" --load-grad"
-
-OPTS+=" $@"
-
-
-CMD="torchrun --nnodes=4 --nproc_per_node=8 --rdzv_id=1 --rdzv_backend=c10d --rdzv_endpoint=${MASTER_ADDR}:${MASTER_PORT} ${CPM_PATH}/apps/cpm9g/sft_cpm9g.py ${OPTS}"
-
-echo "${CMD}"
-$CMD
diff --git a/9G-Train/apps/cpm9g/sft_cpm9g_delta.py b/9G-Train/apps/cpm9g/sft_cpm9g_delta.py
deleted file mode 100644
index 5df8114..0000000
--- a/9G-Train/apps/cpm9g/sft_cpm9g_delta.py
+++ /dev/null
@@ -1,515 +0,0 @@
-# coding=utf-8
-# Copyright 2022 The OpenBMB team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-import inspect
-import json
-import math
-import os
-import re
-import sys
-import time
-from typing import Any
-from typing import Dict
-from typing import List
-from typing import Union
-
-import bmtrain as bmt
-import torch
-
-sys.path.insert(0, "/home/wangshuo1/code/9G-Train")
-from cpm.arguments import get_args
-from cpm.cpm9g.models import CPM9G
-from cpm.cpm9g.models import CPM9GConfig
-from cpm.cpm9g.tokenizers import CPM9GTokenizer
-from cpm.cpm9g.training_tasks import FinetuneDataset
-from cpm.utils import allgather_objects
-from cpm.utils import logger
-import shutil
-
-import opendelta as od
-from opendelta import LoraModel, AdapterModel, CompacterModel, LowRankAdapterModel, BitFitModel, ParallelAdapterModel
-from opendelta.utils.inspect import inspect_optimizer_statistics
-from bigmodelvis import Visualization
-
-
-def get_tokenizer(args):
- tokenizer = CPM9GTokenizer(path=args.vocab)
- return tokenizer
-
-
-def get_model(args):
- config = CPM9GConfig.from_json_file(args.model_config)
- if args.flash == "none":
- config.use_flash_attn = False
- else:
- config.use_flash_attn = True
- if args.flash == "1d":
- config.flash_attn_mask_shape = "1d"
- else:
- config.flash_attn_mask_shape = "2d"
- if args.flash == "triton":
- config.flash_impl = "triton"
- elif args.flash == "cuda":
- config.flash_impl = "cuda"
- model = CPM9G(config)
- if args.load is not None:
- bmt.init_parameters(model)
- bmt.synchronize()
- bmt.print_rank("args.load is not None, start to load checkpoints" + args.load)
- bmt.load(model, args.load, strict=False)
-
- model_inspect = bmt.inspect.inspect_model(model, "*")
- bmt.print_rank(bmt.inspect.format_summary(model_inspect))
- else:
- bmt.print_rank("args.load is None, start to initialize parameters")
- bmt.init_parameters(model)
-
- if args.delta_type != None:
- from bigmodelvis import Visualization #0813
- if bmt.rank() == 0:
- Visualization(model).structure_graph()
- print("finetuned layers: ")
- print(args.lora_layer)
-
- if args.delta_type == "lora":
- delta_model = LoraModel(backbone_model=model, modified_modules=args.lora_layer, backend='bmt', lora_r=args.lora_r, lora_alpha=args.lora_alpha, lora_dropout=args.lora_dropout)
-
- if bmt.rank() == 0:
- print("Before freeze: ")
- delta_model.log()
-
- delta_model.freeze_module(exclude=["deltas"], set_state_dict=True)
-
- if bmt.rank() == 0:
- print("After freeze: ")
- delta_model.log()
-
- return model
-
-
-def get_optimizer(args, model):
- if args.offload:
- optimizer = bmt.optim.AdamOffloadOptimizer(
- model.parameters(), betas=(0.9, 0.95), weight_decay=args.weight_decay
- )
- else:
- optimizer = bmt.optim.AdamOptimizer(model.parameters(), betas=(0.9, 0.95), weight_decay=args.weight_decay)
- if args.load is not None and args.load_grad:
- start = time.time()
- print(
- sum(
- [
- 1
- if i.find(".opt") != -1 and i.find("-{}.rank".format(args.start_step % (args.save_iters * 5))) != -1
- else 0
- for i in os.listdir(args.save)
- ]
- )
- )
- if (
- sum(
- [
- 1
- if i.find(".opt") != -1 and i.find("-{}.rank".format(args.start_step % (args.save_iters * 5))) != -1
- else 0
- for i in os.listdir(args.save)
- ]
- )
- == bmt.world_size()
- ):
- file_name = os.path.join(
- args.save,
- args.save_name + "-{}.rank-{}.opt".format(args.start_step % (args.save_iters * 5), bmt.rank()),
- )
- print(file_name)
- if os.path.exists(file_name):
- print("start to load grad ckpt {}".format(file_name))
- states = torch.load(file_name)
- optimizer.load_state_dict(states)
- logger.info("load grad in {:.2f}s".format(time.time() - start))
- return optimizer
-
-
-class Cosine(bmt.lr_scheduler.WarmupLRScheduler):
- r"""
- After a warmup period during which learning rate increases linearly between 0 and the start_lr,
- The decay period performs :math:`\text{lr}=\text{start_lr}\times \dfrac{1+\cos \left( \pi \cdot \dfrac{\text{num_iter}-\text{warmup_iter}}{\text{end_iter}-\text{warmup_iter}}\right)}{2}`
- """
-
- def get_lr_warmup(self, num_iter) -> float:
- return self.start_lr * num_iter / self.warmup_iter
-
- def get_lr_decay(self, num_iter) -> float:
- progress = (num_iter - self.warmup_iter) / max(1, (self.end_iter - self.warmup_iter))
- return max(self.start_lr * 0.1, self.start_lr * (0.1 + 0.45 * (1.0 + math.cos(progress * math.pi))))
-
-
-def get_learning_rate_scheduler(args, optimizer):
- if args.lr_decay_iters is None:
- args.lr_decay_iters = args.train_iters
- # lr_scheduler = bmt.lr_scheduler.Noam(
- lr_scheduler = Cosine(
- optimizer,
- start_lr=args.lr,
- warmup_iter=args.warmup_iters,
- end_iter=args.lr_decay_iters,
- num_iter=args.start_step,
- )
- return lr_scheduler
-
-
-def setup_model_and_optimizer(args):
- start = time.time()
- model = get_model(args)
- logger.info("load model in {:.2f}s".format(time.time() - start))
-
- start = time.time()
- tokenizer = get_tokenizer(args)
- bmt.synchronize()
- logger.info("load tokenizer in {:.2f}s".format(time.time() - start))
-
- start = time.time()
- optimizer = get_optimizer(args, model)
-
- if args.delta_type != None:
- inspect_optimizer_statistics(optimizer)
-
- lr_scheduler = get_learning_rate_scheduler(args, optimizer)
- bmt.synchronize()
- logger.info("load lr_scheduler in {:.2f}s".format(time.time() - start))
-
- return tokenizer, model, optimizer, lr_scheduler
-
-
-def initialize():
- args = get_args(finetune=True)
-
- # hack
- if "checkpointing" in inspect.signature(bmt.init_distributed).parameters:
- bmt.init_distributed(checkpointing=False, seed=args.seed)
- else:
- bmt.init_distributed(seed=args.seed)
- if args.save is not None:
- os.makedirs(args.save, exist_ok=True)
- # if args.load is not None:
- # if args.start_step == 0:
- # args.start_step = (int)(re.search("(\d+).pt", args.load)[1])
- return args
-
-
-def see_memory(detail=False):
- if detail:
- res = torch.cuda.memory_summary()
- else:
- res = (
- round(torch.cuda.memory_allocated() / (1024 * 1024 * 1024), 2),
- round(torch.cuda.max_memory_allocated() / (1024 * 1024 * 1024), 2),
- )
- torch.cuda.reset_peak_memory_stats()
- return res
-
-
-def add_mem_time(info, mem_usage, tim_usage):
- torch.cuda.synchronize()
- mem_usage[info] = see_memory()
- tim_usage[info] = time.time()
- return mem_usage, tim_usage
-
-
-class LossSpikeDetector:
- def __init__(self, log_path: str) -> None:
- self._last_loss: Dict[str, float] = {}
- self._last_data: List[Any] = [None]
- self._log_path = log_path
-
- def update_data(self, data: Any):
- self._last_data.append(data)
- if len(self._last_data) > 2:
- self._last_data = self._last_data[-2:]
-
- def update_loss(self, iteration: int, loss_map: Dict[str, float]):
- loss_spike_result = []
- for task, loss in loss_map.items():
- if task in self._last_loss:
- if loss > self._last_loss[task] * 3:
- # loss spike!
- loss_spike_result.append(
- {
- "prev": self._last_loss[task],
- "curr": loss,
- "task": task,
- }
- )
- self._last_loss[task] = float(loss)
- if len(loss_spike_result) > 0:
- self._write_log(iteration, self._last_data[-1], loss_spike_result)
-
- def _write_log(self, iteration: int, data: Any, result: List[Dict[str, Any]]):
- return
- with open(self._log_path, "a", encoding="utf-8") as fp:
- fp.write("=" * 20)
- fp.write("\nloss spike at {}\n".format(iteration))
- fp.write("{}\n".format(json.dumps(result, indent=4, ensure_ascii=False)))
- fp.write("data: \n")
- for d in data:
- fp.write("{}\n".format(json.dumps(d, indent=4, ensure_ascii=False)))
- fp.write("\n\n")
-
-
-def finetune(
- args,
- bin_file: str,
- tokenizer: CPM9GTokenizer,
- model: CPM9G,
- optimizer,
- lr_scheduler: bmt.lr_scheduler.WarmupLRScheduler,
-):
- average_time = bmt.utils.AverageRecorder()
- loss_func = bmt.loss.FusedCrossEntropy(ignore_index=-100)
- optim_manager = bmt.optim.OptimManager(loss_scale=args.loss_scale, loss_scale_steps=args.loss_scale_steps, loss_scale_factor=2, max_loss_scale=args.max_loss_scale, min_loss_scale=args.min_loss_scale,)
- optim_manager.add_optimizer(optimizer, lr_scheduler)
-
- if args.tensorboard is not None and bmt.rank() == 0:
- import distutils.version # noqa: F401
- from tensorboardX import SummaryWriter
- if not os.path.exists(args.tensorboard):
- os.makedirs(args.tensorboard)
- writer = SummaryWriter(log_dir=args.tensorboard)
-
- global_token_pass = 0.0
- global_world_size = bmt.world_size()
-
- for epoch in range(args.epoch):
- epoch = epoch + 1
- last_data = None
- dataloader = FinetuneDataset(
- bin_file, args.batch_size, args.max_length, tokenizer, unpad=(args.flash == "cuda"), task_name="task", drop_last=True
- )
- for iteration, data in enumerate(dataloader):
- iteration = iteration + 1
- skip_this_batch = False
- if data is None:
- if last_data is None:
- raise RuntimeError(
- "Dataset is too small, please use a smaller batch size or sequence length!"
- )
- data = last_data # use last data
- skip_this_batch = True
- else:
- last_data = data
-
- assert data["inputs"].shape[0] == args.batch_size
- input_ids = torch.from_numpy(data["inputs"]).cuda().to(torch.int32)
- input_length = torch.from_numpy(data["length"]).cuda().to(torch.int32)
- targets = torch.from_numpy(data["target"]).cuda().long()
- # bmt.print_rank(input_ids[0].tolist())
- # bmt.print_rank(targets[0].tolist())
- # bmt.print_rank(data["spans"].tolist())
- # bmt.print_rank(tokenizer.decode(input_ids[0]))
- # bmt.print_rank(tokenizer.path)
- # bmt.synchronize()
- # exit()
- task_ids = torch.from_numpy(data["task_ids"]).cuda().to(torch.int32)
- task_names = data["task_names"]
- if args.flash == "cuda":
- cu_seqlens = torch.from_numpy(data["cu_seqlens"]).cuda().to(torch.int32)
- max_seqlen = data["max_seqlen"]
- position_ids = torch.from_numpy(data["position_ids"]).cuda().to(torch.int32)
- else:
- input_context = torch.zeros_like(input_ids).cuda().bool()
- input_span = torch.from_numpy(data["spans"]).cuda().to(torch.int32)
-
- # ===========
- optim_manager.zero_grad()
- # torch.cuda.empty_cache()
- mem_usage = {}
- tim_usage = {}
- mem_usage, tim_usage = add_mem_time("init", mem_usage, tim_usage)
-
- # ===========
- if args.flash == "cuda":
- logits, _ = model(
- input_ids,
- cu_seqlens=cu_seqlens,
- max_seqlen=max_seqlen,
- position_ids=position_ids,
- )
- else:
- logits, _ = model(
- input_ids,
- input_length,
- input_context,
- input_span,
- )
- mem_usage, tim_usage = add_mem_time("forward_1", mem_usage, tim_usage)
- loss = loss_func(logits.view(-1, logits.size(-1)), targets.view(-1))
- if skip_this_batch:
- loss = loss * 0
- mem_usage, tim_usage = add_mem_time("forward", mem_usage, tim_usage)
-
- # ===========
- optim_manager.backward(loss)
- mem_usage, tim_usage = add_mem_time("backward", mem_usage, tim_usage)
-
- # ===========
- grad_norm = optim_manager.clip_grad_norm(optimizer.param_groups, args.clip_grad, norm_type=2)
- optim_manager.step()
- mem_usage, tim_usage = add_mem_time("optim", mem_usage, tim_usage)
-
- # ==========
- iter_time = tim_usage["optim"] - tim_usage["init"]
- average_time.record(iter_time)
-
- with torch.no_grad():
- task_num = len(task_names)
- targets_tmp = targets.expand(task_num, -1, -1)
- task = torch.arange(task_num, dtype=torch.long, device="cuda")[:, None, None]
- targets_tmp = torch.where(
- task_ids == task,
- targets_tmp,
- torch.scalar_tensor(-100, dtype=torch.long, device="cuda"),
- )
-
- task_loss_map: Dict[str, float] = {}
- if not skip_this_batch:
- for i in range(task_num):
- task_loss = loss_func(logits.view(-1, logits.size(-1)), targets_tmp[i, :].view(-1))
- task_loss_map[task_names[i]] = task_loss.item()
- gatherd_task_loss_map: List[Dict[str, float]] = allgather_objects(task_loss_map)
-
- global_task_loss_map: Dict[str, Union[List[float], float]] = {}
- for local_task_loss_map in gatherd_task_loss_map:
- for task_name, task_loss in local_task_loss_map.items():
- if task_name not in global_task_loss_map:
- global_task_loss_map[task_name] = []
- global_task_loss_map[task_name].append(task_loss)
-
- task_loss_map = {}
- for task_name in sorted(list(global_task_loss_map.keys())):
- avg_loss = sum(global_task_loss_map[task_name]) / len(global_task_loss_map[task_name])
- task_loss_map[task_name] = avg_loss
-
- local_total_rate = torch.Tensor([input_length.float().mean() / args.max_length]).cuda()
- local_total_rate = bmt.sum_loss(local_total_rate).item()
- global_token_pass += global_world_size * local_total_rate * args.max_length * args.batch_size
- avg_time = average_time.value
-
- train_info = {
- "time": tim_usage["init"],
- "epoch": epoch,
- "iteration": iteration,
- "loss": task_loss_map[args.task_name],
- "lr": lr_scheduler.current_lr,
- "lr_scale": int(optim_manager.loss_scale),
- "time_usage": tim_usage,
- "mem_usage": mem_usage,
- "avg_time": avg_time,
- "token_max": local_total_rate,
- "token_pass": global_token_pass,
- "throughout": args.max_length * args.batch_size * local_total_rate / avg_time,
- "grad_norm": grad_norm.item(),
- "mask_max": ((targets >= 0).sum(-1).float().mean() / args.max_length).item(),
- "num_gpus": global_world_size,
- "task_loss": task_loss_map,
- }
- # bmt.print_rank(time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()))
- bmt.print_rank(
- (
- "| Epoch: {:3d} | Iter: {:6d}/{:6d} | loss: {:.4f} | lr: {:.6e} | scale: {:10.0f} | time: {:.1f} |"
- + " token/max: {:.3f} | mask/max: {:.3f} | grad_norm: {:.3f}"
- ).format(
- epoch,
- iteration,
- args.train_iters,
- task_loss_map[args.task_name],
- lr_scheduler.current_lr,
- int(optim_manager.loss_scale),
- avg_time,
- input_length.float().mean() / args.max_length,
- (targets >= 0).sum(-1).float().mean() / args.max_length,
- grad_norm,
- )
- )
-
- bmt.print_rank(
- "| "
- + " | ".join(["{}: {:.4f}".format(task_name, loss) for task_name, loss in task_loss_map.items()])
- + " |"
- )
- if iteration % args.inspect_iters == 0:
- model_inspect = bmt.inspect.inspect_model(model, "*")
- bmt.print_rank(bmt.inspect.format_summary(model_inspect))
- train_info["model_inspect"] = model_inspect
-
- # save_folder_name = f"{args.save}{epoch}{iteration}"
- # model_fname = os.path.join(save_folder_name, f"{args.save_name}-iter-{iteration}.pt")
- # os.makedirs(os.path.dirname(model_fname), exist_ok=True)
- # if bmt.rank() == 0:
- # shutil.copy(args.model_config, os.path.join(save_folder_name, "config.json"))
- # shutil.copy(args.vocab, os.path.join(save_folder_name, "vocabs.txt"))
-
- if args.tensorboard is not None and bmt.rank() == 0:
- writer.add_scalar(f"Loss/train/{epoch}", task_loss_map[args.task_name], iteration)
- writer.add_scalar(f"Optimizer/lr/{epoch}", lr_scheduler.current_lr, iteration)
- writer.add_scalar(f"Optimizer/scale/{epoch}", optim_manager.loss_scale, iteration)
- writer.add_scalar(f"Optimizer/grad_norm/{epoch}", grad_norm.item(), iteration)
-
- # if iteration % 10 == 0:
- # save_folder_name = f"{args.save}{epoch}"
- # model_fname = os.path.join(save_folder_name, f"{args.save_name}-epoch-{epoch}.pt")
- # os.makedirs(os.path.dirname(model_fname), exist_ok=True)
- # bmt.save(model, model_fname)
- # if bmt.rank() == 0:
- # shutil.copy(args.model_config, os.path.join(save_folder_name, "config.json"))
- # shutil.copy(args.vocab, os.path.join(save_folder_name, "vocabs.txt"))
-
- save_folder_name = f"{args.save}/epoch-{epoch}"
- model_fname = os.path.join(save_folder_name, f"{args.save_name}.pt")
- os.makedirs(os.path.dirname(model_fname), exist_ok=True)
- state_dict = model.state_dict()
- if args.delta_type == None or args.save_origin_model == True :
- print("saving base model...")
- bmt.save(model, model_fname)
- if args.delta_type != None and bmt.rank() == 0:
- print("saving delta model...")
- torch.save(state_dict, os.path.join(save_folder_name, f"{args.save_name}-delta.pt"))
- if bmt.rank() == 0:
- shutil.copy(args.model_config, os.path.join(save_folder_name, "config.json"))
- shutil.copy(args.vocab, os.path.join(save_folder_name, "vocabs.txt"))
-
-
-def main():
- args = initialize()
- # To Be Specified
- bin_file = args.dataset
- bmt.print_rank(f"dataset: {bin_file}")
-
- tokenizer = get_tokenizer(args)
- dataloader = FinetuneDataset(
- bin_file, args.batch_size, args.max_length, tokenizer, unpad=(args.flash == "cuda"), task_name="task", drop_last=True
- )
- bmt.print_rank(f"#batch: {len(dataloader)}")
- total_steps = len(dataloader) * args.epoch
-
- setattr(args, 'train_iters', int(total_steps))
- setattr(args, 'warmup_iters', max(int(total_steps*0.02), args.warmup_iters))
- bmt.print_rank(json.dumps(vars(args), indent=2, sort_keys=True))
- tokenizer, model, optimizer, lr_scheduler = setup_model_and_optimizer(args)
-
- bmt.print_rank("finish loading")
- finetune(args, bin_file, tokenizer, model, optimizer, lr_scheduler)
-
-
-if __name__ == "__main__":
- main()
diff --git a/9G-Train/apps/cpm9g/sft_cpm9g_delta_11b.sh b/9G-Train/apps/cpm9g/sft_cpm9g_delta_11b.sh
deleted file mode 100644
index 7613f1b..0000000
--- a/9G-Train/apps/cpm9g/sft_cpm9g_delta_11b.sh
+++ /dev/null
@@ -1,63 +0,0 @@
-#! /bin/bash
-#SBATCH --partition=gpu3-1
-#SBATCH --nodes=1
-#SBATCH --ntasks-per-node=8
-#SBATCH --gres=gpu:8
-#SBATCH --cpus-per-task=8
-
-export MASTER_ADDR=`hostname`
-export MASTER_PORT=12345
-echo $MASTER_ADDR
-
-CPM_PATH="./9G-Train"
-CONFIG_NAME="${CPM_PATH}/apps/cpm9g/config/11b"
-EXP_PATH=./exp
-mkdir -p $EXP_PATH
-MODEL_NAME="cpm9g-11b-sft"
-
-OPTS=""
-OPTS+=" --model-config ${CONFIG_NAME}/config.json"
-OPTS+=" --vocab ${CONFIG_NAME}/vocab.txt"
-
-OPTS+=" --train-iters 10000"
-OPTS+=" --inspect-iters 200"
-OPTS+=" --warmup-iters 500"
-
-OPTS+=" --lr-decay-style cosine"
-OPTS+=" --weight-decay 0.1"
-OPTS+=" --clip-grad 1.0"
-OPTS+=" --loss-scale 1048576"
-OPTS+=" --max-loss-scale 33554432"
-OPTS+=" --min-loss-scale 1"
-OPTS+=" --loss-scale-steps 32"
-
-OPTS+=" --offload"
-OPTS+=" --batch-size 4"
-OPTS+=" --max-length 4096"
-OPTS+=" --lr 2e-5"
-OPTS+=" --start-step 0"
-OPTS+=" --epoch 8"
-OPTS+=" --load /data/groups/QY_LLM_Core/models/20231010/11b-base/11b.pt"
-OPTS+=" --dataset /data/groups/QY_LLM_Core/datasets/sft/20231025/merge_qy_sft_bin"
-# TODO 这些 /data 在启元机器上需要改成 /home 下的路径
-OPTS+=" --save ${EXP_PATH}/checkpoints"
-OPTS+=" --save-name ${MODEL_NAME}"
-# OPTS+=" --tensorboard /data/logs/tensorboard/${MODEL_NAME}/${CUR_DATE}/"
-# OPTS+=" --flash triton"
-# OPTS+=" --flash cuda"
-# OPTS+=" --load-grad"
-
-OPTS+=" --delta-tuning" #开启delta-tuning
-OPTS+=" --delta-type lora" #目前仅支持lora
-OPTS+=" --lora-r 64" #lora矩阵的维度,默认为8
-OPTS+=" --lora-dropout 0.05" #默认为0
-OPTS+=" --lora-alpha 64" #lora对原模型的影响比例,默认为8
-OPTS+=" --lora-layer project_q project_v project_k w_0 w_1 w_out" #参与lora的线性层,默认为project_q,project_k
-OPTS+=" --save-origin-model" #是否在每个epoch储存基座模型
-
-OPTS+=" $@"
-
-CMD="torchrun --nnodes=1 --nproc_per_node=8 --rdzv_id=1 --rdzv_backend=c10d --rdzv_endpoint=${MASTER_ADDR}:${MASTER_PORT} ${CPM_PATH}/apps/cpm9g/sft_cpm9g_delta.py ${OPTS}"
-
-echo "${CMD}"
-$CMD
diff --git a/9G-Train/cpm/cpm9g/__init__.py b/9G-Train/cpm/cpm9g/__init__.py
deleted file mode 100644
index 9fca741..0000000
--- a/9G-Train/cpm/cpm9g/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-from .models import CPM9G
-from .models import CPM9GConfig
-
-from .tokenizers import CPM9GTokenizer
-from .training_tasks import MixedDataset
diff --git a/9G-Train/cpm/cpm9g/config/cpm9g-7b.json b/9G-Train/cpm/cpm9g/config/cpm9g-7b.json
deleted file mode 100644
index 9473b78..0000000
--- a/9G-Train/cpm/cpm9g/config/cpm9g-7b.json
+++ /dev/null
@@ -1,16 +0,0 @@
-{
- "vocab_size": 119696,
- "dropout_p": 0.0,
- "eps": 1e-05,
- "half": true,
- "use_flash_attn": false,
- "flash_attn_mask_shape": "1d",
- "dim_model": 4096,
- "dim_ff": 12288,
- "dim_head": 128,
- "num_heads": 32,
- "num_kv_heads": 32,
- "num_layers": 32,
- "activate_fn": "silu",
- "scale": false
-}
diff --git a/9G-Train/cpm/cpm9g/generation/cpm9g.py b/9G-Train/cpm/cpm9g/generation/cpm9g.py
deleted file mode 100644
index 8e49bcf..0000000
--- a/9G-Train/cpm/cpm9g/generation/cpm9g.py
+++ /dev/null
@@ -1,658 +0,0 @@
-from typing import Any
-from typing import Dict
-from typing import List
-from typing import Tuple
-
-import numpy as np
-import torch
-import torch.nn.functional as F
-
-from ...generation import apply_repetition_penalty
-from ...generation import BeamHypotheses
-from ...generation import top_k_top_p_filtering
-
-from ...utils import pad
-from ..models import CPM9GTorch
-from ..tokenizers.cpm9g import CPM9GTokenizer
-
-
-class CPM9GGeneration:
- def __init__(
- self, model: CPM9GTorch, tokenizer: CPM9GTokenizer, max_in_len=1024, use_nbce: bool = False
- ):
- model.eval()
- self.model = model
- self.tokenizer = tokenizer
- self.max_in_len = max_in_len
-
- def _convert_to_tensors(self, data: Any):
- input_ids = self.tokenizer.encode(
- data["input"]
- ) # [self.tokenizer.bos_token_id] + self.tokenizer.encode(data["input"])
- model_input = {}
-
- model_input["input_ids"] = torch.tensor(input_ids[: self.max_in_len], dtype=torch.int32).unsqueeze(0)
- model_input["context"] = torch.zeros(
- (model_input["input_ids"].shape[0], model_input["input_ids"].shape[1]), dtype=torch.int16
- )
- model_input["span"] = torch.ones((model_input["input_ids"].shape[1],), dtype=torch.int16).unsqueeze(0)
- model_input["length"] = torch.tensor([model_input["input_ids"].shape[1]], dtype=torch.int16).unsqueeze(0)
- return model_input
-
- def _process_list(self, data_list: List[Any]):
- input_tensors = list(map(self._convert_to_tensors, data_list))
- keys = set(input_tensors[0].keys())
- padded = {}
- for key in keys:
- padded[key] = pad(input_tensors, key, padding_side="left").cuda()
- return padded
-
- def generate(self, data_list, **kwargs):
- origin_data_list = data_list.copy()
- model_inputs = self._process_list(data_list)
- with torch.inference_mode():
- result_ids = self._decode(model_inputs, **kwargs)
-
- return result_ids
-
- def _decode(self, model_inputs, **kwargs):
- raise NotImplementedError("_decode is not implemented.")
-
-
-class CPM9GBeamSearch(CPM9GGeneration):
- def _decode(
- self,
- model_inputs,
- beam_size=4,
- max_length=100,
- repetition_penalty=1.2,
- repetition_window=None,
- ):
- """
- Beam search
- Args:
- model_inputs (dict): input ids.
- beam_size (int, optional, defaults to 3): beam size of beam search.
- generate_length (int, optional, defaults to 100): maximum generation length.
- repetition_penalty (float, optional, defaults to 1.0): repetition penalty coefficient, 1.0 means no penalty.
- repetition_window (int, optional, defaults to None): window size of repetition penalty, None means that all output tokens are penalized.
- """ # noqa: E501
- # generate_length + 1 for EOS token
- max_length += 1
- # expand dimmension
- batch_size = model_inputs["input_ids"].size(0)
- input: torch.Tensor = (
- model_inputs["input_ids"]
- .unsqueeze(1)
- .expand(batch_size, beam_size, -1)
- .contiguous()
- .view(batch_size * beam_size, -1)
- )
- length = (
- model_inputs["length"]
- .squeeze(1)
- .unsqueeze(1)
- .expand(batch_size, beam_size)
- .contiguous()
- .view(
- batch_size * beam_size,
- )
- )
- span: torch.Tensor = (
- model_inputs["span"]
- .unsqueeze(1)
- .expand(batch_size, beam_size, -1)
- .contiguous()
- .view(batch_size * beam_size, -1)
- )
- context: torch.Tensor = (
- model_inputs["context"]
- .unsqueeze(1)
- .expand(batch_size, beam_size, -1)
- .contiguous()
- .view(batch_size * beam_size, -1)
- )
-
- done = [False for _ in range(batch_size)]
-
- beam_scores = torch.zeros((batch_size, beam_size), dtype=torch.float, device=input.device)
- beam_scores[:, 1:] = -1e9
- beam_scores = beam_scores.view(-1)
-
- # generated hypotheses
- generated_hyps = [
- BeamHypotheses(beam_size, max_length, length_penalty=1, early_stopping=False) for _ in range(batch_size)
- ]
-
- pred_start_index = input.size(-1)
- past_key_values = None
-
- for i in range(max_length + 1):
- if i == 0:
- logits, _, past_key_values = self.model.inference(
- input=input,
- context=context,
- span=span,
- length=length,
- past_key_values=past_key_values,
- )
- else:
- logits, _, past_key_values = self.model.inference(
- input=input[:, -1:],
- context=context,
- span=span,
- length=length,
- past_key_values=past_key_values,
- )
- # skip all steps when we are done with each sentence
- if all(done):
- break
-
- # (batch * beam, seqlen, model_dim)
- logits = logits[:, -1, :]
- if i == 0:
- logits[:, self.tokenizer.bos_token_id] = -float("inf")
- # logits[:, self.tokenizer.newline_id] = -float("inf")
- apply_repetition_penalty(
- logits,
- batch_size,
- beam_size,
- input,
- repetition_penalty,
- pred_start_index,
- input.size(-1) - 1,
- repetition_window,
- )
-
- scores = F.log_softmax(logits, dim=-1)
-
- next_scores = scores + beam_scores[:, None].expand_as(scores) # (batch_size * beam_size, vocab_size)
-
- # re-organize to group the beam together (we are keeping top hypothesis accross beams)
- next_scores = next_scores.view(batch_size, -1) # (batch_size, beam_size * vocab_size)
- next_scores, next_words = torch.topk(next_scores, 2 * beam_size, dim=1, largest=True, sorted=True)
-
- assert next_scores.size() == next_words.size() == (batch_size, 2 * beam_size)
- next_batch_beam = []
-
- for sent_id in range(batch_size):
- # if we are done with this sentence
- done[sent_id] = done[sent_id] or generated_hyps[sent_id].is_done(next_scores[sent_id].max().item(), i)
- if done[sent_id]:
- next_batch_beam.extend([(0, 0, 0)] * beam_size) # pad the batch
- continue
-
- # next sentence beam content
- next_sent_beam = []
-
- # next words for this sentence
- for idx, value in zip(next_words[sent_id], next_scores[sent_id]):
- # get beam and word IDs
- beam_id = torch.div(idx, scores.size(-1), rounding_mode="floor")
- word_id = idx % scores.size(-1)
-
- # end of sentence, or next word
- if word_id == self.tokenizer.bos_token_id or i == max_length:
- generated_hyps[sent_id].add(
- input[sent_id * beam_size + beam_id, pred_start_index:].clone().cpu().tolist(),
- value.item(),
- )
- else:
- next_sent_beam.append((value, word_id, sent_id * beam_size + beam_id))
-
- # the beam for next step is full
- if len(next_sent_beam) == beam_size:
- break
-
- # update next beam content
- assert len(next_sent_beam) == 0 if i == max_length else beam_size
- if len(next_sent_beam) == 0:
- next_sent_beam = [(0, 0, 0)] * beam_size # pad the batch
- next_batch_beam.extend(next_sent_beam)
- assert len(next_batch_beam) == beam_size * (sent_id + 1)
-
- # we have reached the last step
- if i == max_length:
- break
-
- # sanity check / prepare next batch
- assert len(next_batch_beam) == batch_size * beam_size
- beam_scores = beam_scores.new([x[0] for x in next_batch_beam])
- beam_words = input.new([x[1] for x in next_batch_beam])
- beam_idx = torch.tensor([x[2] for x in next_batch_beam], device=input.device).long()
- # re-order batch and internal states
- input = input[beam_idx, :]
-
- past_key_values["buffer"] = [list(each) if each is not None else each for each in past_key_values["buffer"]] # type: ignore # noqa: E501
- for key_value_layer in past_key_values["buffer"]:
- if key_value_layer is not None:
- key_value_layer[0] = key_value_layer[0][beam_idx]
- key_value_layer[1] = key_value_layer[1][beam_idx]
-
- input = torch.cat([input, beam_words.unsqueeze(1)], dim=-1)
- context = torch.cat(
- [context, context[:, -1:]],
- dim=-1,
- )
- length += 1
-
- span = torch.cat([span, span[:, -1:]], dim=-1)
-
- # select the best hypotheses
-
- results = []
- for i, hypotheses in enumerate(generated_hyps):
- best_hyp = max(hypotheses.hyp, key=lambda x: x[0])[1]
- results.append(best_hyp)
-
- result_text = list(map(self.tokenizer.decode, results))
- return result_text
-
-
-class CPM9GRandomSampling(CPM9GGeneration):
- def _decode(
- self,
- model_inputs,
- max_length=100,
- top_k=0,
- top_p=0.9,
- temperature=0.9,
- repetition_penalty=1.0,
- repetition_window=None,
- **kwargs,
- ):
- """
- Top-k and top-p sampling.
- Args:
- model_inputs (dict): input ids
- generate_length (int, optional, defaults to 100): maximum generation length
- top_k (int, optional, defaults to 0): keep only top k tokens with highest probability. 0 means keeping all tokens.
- top_p (int, optional, defaults to 0.9): keep the top tokens with cumulative probability >= top_p.
- temperature (int, optional, defaults to 0.9): the value that can cool down the logits distribution.
- repetition_penalty (float, optional, defaults to 1.0): repetition penalty coefficient, 1.0 means no penalty.
- repetition_window (int, optional, defaults to None): window size of repetition penalty, None means that all output tokens are penalized.
- """ # noqa: E501
- # generate_length + 1 for EOS token
- max_length += 1
-
- input = model_inputs["input_ids"]
- context = model_inputs["context"]
-
- length = model_inputs["length"].squeeze(1)
- span = model_inputs["span"]
- batch_size = input.size(0)
-
- pred_start_index = input.size(-1)
- past_key_values = None
- done = [False for _ in range(batch_size)]
- results = [None for _ in range(batch_size)]
- for i in range(max_length):
- if i == 0:
- logits, _, past_key_values = self.model.inference(
- input=input,
- context=context,
- length=length,
- span=span,
- past_key_values=past_key_values,
- )
- else:
- logits, _, past_key_values = self.model.inference(
- input=input[:, -1:],
- context=context,
- length=length,
- span=span,
- past_key_values=past_key_values,
- )
-
- logits = logits[:, -1, :]
-
- if i == 0:
- logits[:, self.tokenizer.bos_token_id] = -float("inf")
- # logits[:, self.tokenizer.newline_id] = -float("inf")
-
- apply_repetition_penalty(
- logits,
- batch_size,
- 1,
- input,
- repetition_penalty,
- pred_start_index,
- input.size(-1) - 1,
- repetition_window,
- )
-
- logits = logits / temperature
- logits = top_k_top_p_filtering(logits, top_k=top_k, top_p=top_p)
-
- probs = F.softmax(logits, dim=-1)
- next_token = torch.multinomial(probs, num_samples=1)
-
- for idx in range(batch_size):
- if not done[idx] and (next_token[idx].item() == self.tokenizer.bos_token_id or i == max_length - 1):
- done[idx] = True
- results[idx] = input[idx, pred_start_index:].clone().cpu().tolist() # type: ignore # noqa: E501
-
- if sum(done) == batch_size:
- break
-
- # update input ids
- input = torch.cat([input, next_token], dim=-1)
- length += 1
-
- context = torch.cat(
- [context, context[:, -1:]],
- dim=-1,
- )
- span = torch.cat(
- [span, span[:, -1:]],
- dim=-1,
- )
-
- result_text = list(map(self.tokenizer.decode, results))
- return result_text
-
-
-class CPM9GBeamSearchNBCE(CPM9GGeneration):
- def _decode(
- self,
- model_inputs,
- beam_size=5,
- max_length=100,
- repetition_penalty=1.0,
- repetition_window=None,
- ):
- """
- Beam search
- Args:
- model_inputs (dict): input ids.
- beam_size (int, optional, defaults to 3): beam size of beam search.
- generate_length (int, optional, defaults to 100): maximum generation length.
- repetition_penalty (float, optional, defaults to 1.0): repetition penalty coefficient, 1.0 means no penalty.
- repetition_window (int, optional, defaults to None): window size of repetition penalty, None means that all output tokens are penalized.
- """ # noqa: E501
- # generate_length + 1 for EOS token
- max_length += 1
-
- # expand dimmension
- batch_size = model_inputs["input_ids"].size(0)
- input: torch.Tensor = (
- model_inputs["input_ids"]
- .unsqueeze(1)
- .expand(batch_size, beam_size, -1)
- .contiguous()
- .view(batch_size * beam_size, -1)
- )
- length = (
- model_inputs["length"]
- .squeeze(1)
- .unsqueeze(1)
- .expand(batch_size, beam_size)
- .contiguous()
- .view(
- batch_size * beam_size,
- )
- )
- span: torch.Tensor = (
- model_inputs["span"]
- .unsqueeze(1)
- .expand(batch_size, beam_size, -1)
- .contiguous()
- .view(batch_size * beam_size, -1)
- )
- context: torch.Tensor = (
- model_inputs["context"]
- .unsqueeze(1)
- .expand(batch_size, beam_size, -1)
- .contiguous()
- .view(batch_size * beam_size, -1)
- )
-
- done = [False]
-
- beam_scores = torch.zeros((1, beam_size), dtype=torch.float, device=input.device)
- beam_scores[:, 1:] = -1e9
- beam_scores = beam_scores.view(-1)
-
- # generated hypotheses
- generated_hyps = [
- BeamHypotheses(beam_size, max_length, length_penalty=1, early_stopping=False) for _ in range(1)
- ]
-
- pred_start_index = input.size(-1)
- past_key_values = None
-
- for i in range(max_length + 1):
- if i == 0:
- logits, _, past_key_values = self.model.inference(
- input=input,
- context=context,
- span=span,
- length=length,
- past_key_values=past_key_values,
- )
- else:
- logits, _, past_key_values = self.model.inference(
- input=input[:, -1:],
- context=context,
- span=span,
- length=length,
- past_key_values=past_key_values,
- )
- # skip all steps when we are done with each sentence
-
- if all(done):
- break
-
- # (batch * beam, seqlen, model_dim)
- assert logits.size(0) > 1, "nbce needs to ensure that the length of logits 0 is greater than 1"
- logits = NBCE(logits) # [vocab_size]
- logits = logits.tile(beam_size, 1)
-
- if i == 0:
- logits[:, self.tokenizer.bos_token_id] = -float("inf")
- apply_repetition_penalty(
- logits,
- 1,
- beam_size,
- input,
- repetition_penalty,
- pred_start_index,
- input.size(-1) - 1,
- repetition_window,
- )
-
- scores = F.log_softmax(logits, dim=-1)
-
- next_scores = scores + beam_scores[:, None].expand_as(scores) # (batch_size * beam_size, vocab_size)
-
- # re-organize to group the beam together (we are keeping top hypothesis accross beams)
- next_scores = next_scores.view(1, -1) # (batch_size, beam_size * vocab_size)
- next_scores, next_words = torch.topk(next_scores, 2 * beam_size, dim=1, largest=True, sorted=True)
-
- assert next_scores.size() == next_words.size() == (1, 2 * beam_size)
- next_batch_beam = []
-
- for sent_id in range(1):
- # if we are done with this sentence
- done[sent_id] = done[sent_id] or generated_hyps[sent_id].is_done(next_scores[sent_id].max().item(), i)
- if done[sent_id]:
- next_batch_beam.extend([(0, 0, 0)] * beam_size) # pad the batch
- continue
-
- # next sentence beam content
- next_sent_beam = []
-
- # next words for this sentence
- for idx, value in zip(next_words[sent_id], next_scores[sent_id]):
- # get beam and word IDs
- beam_id = torch.div(idx, scores.size(-1), rounding_mode="floor")
- word_id = idx % scores.size(-1)
-
- # end of sentence, or next word
- if word_id == self.tokenizer.bos_token_id or i == max_length:
- generated_hyps[sent_id].add(
- input[sent_id * beam_size + beam_id, pred_start_index:].clone().cpu().tolist(),
- value.item(),
- )
- else:
- next_sent_beam.append((value, word_id, sent_id * beam_size + beam_id))
-
- # the beam for next step is full
- if len(next_sent_beam) == beam_size:
- break
-
- # update next beam content
- assert len(next_sent_beam) == 0 if i == max_length else beam_size
- if len(next_sent_beam) == 0:
- next_sent_beam = [(0, 0, 0)] * beam_size # pad the batch
- next_batch_beam.extend(next_sent_beam)
- assert len(next_batch_beam) == beam_size * (sent_id + 1)
-
- # we have reached the last step
- if i == max_length:
- break
-
- # sanity check / prepare next batch
- assert len(next_batch_beam) == batch_size * beam_size
- beam_scores = beam_scores.new([x[0] for x in next_batch_beam])
- beam_words = input.new([x[1] for x in next_batch_beam])
- beam_idx = torch.tensor([x[2] for x in next_batch_beam], device=input.device).long()
- beam_idx *= batch_size
- # re-order batch and internal states
- input = input[beam_idx, :]
-
- past_key_values["buffer"] = [list(each) if each is not None else each for each in past_key_values["buffer"]] # type: ignore # noqa: E501
- for key_value_layer in past_key_values["buffer"]:
- if key_value_layer is not None:
- key_value_layer[0] = key_value_layer[0][beam_idx]
- key_value_layer[1] = key_value_layer[1][beam_idx]
-
- input = torch.cat([input, beam_words.unsqueeze(1)], dim=-1)
- context = torch.cat(
- [context, torch.zeros((context.size(0), 1), dtype=torch.int16, device=context.device)],
- dim=-1,
- )
- length = past_key_values["buffer_length"]
- length = torch.cat(
- [length, torch.ones((length.size(0), 1), dtype=torch.int16, device=length.device)],
- dim=-1,
- )
- span = torch.cat([span, span[:, -1:]], dim=-1)
-
- # select the best hypotheses
- results = []
- for i, hypotheses in enumerate(generated_hyps):
- best_hyp = max(hypotheses.hyp, key=lambda x: x[0])[1]
- results.append(best_hyp)
-
- result_text = list(map(self.tokenizer.decode, results))
- return result_text
-
-
-class CPM9GRandomSamplingNBCE(CPM9GGeneration):
- def _decode(
- self,
- model_inputs,
- max_length=100,
- top_k=0,
- top_p=0.9,
- temperature=0.9,
- repetition_penalty=1.0,
- repetition_window=None,
- **kwargs,
- ):
- """
- Top-k and top-p sampling.
- Args:
- model_inputs (dict): input ids
- generate_length (int, optional, defaults to 100): maximum generation length
- top_k (int, optional, defaults to 0): keep only top k tokens with highest probability. 0 means keeping all tokens.
- top_p (int, optional, defaults to 0.9): keep the top tokens with cumulative probability >= top_p.
- temperature (int, optional, defaults to 0.9): the value that can cool down the logits distribution.
- repetition_penalty (float, optional, defaults to 1.0): repetition penalty coefficient, 1.0 means no penalty.
- repetition_window (int, optional, defaults to None): window size of repetition penalty, None means that all output tokens are penalized.
- """ # noqa: E501
- # generate_length + 1 for EOS token
- max_length += 1
-
- input = model_inputs["input_ids"]
- context = model_inputs["context"]
-
- length = model_inputs["length"].squeeze(1)
- span = model_inputs["span"]
- batch_size = input.size(0)
-
- pred_start_index = input.size(-1)
- past_key_values = None
- done = [False]
- results = [None]
- for i in range(max_length):
- if i == 0:
- logits, _, past_key_values = self.model.inference(
- input=input,
- context=context,
- length=length,
- span=span,
- past_key_values=past_key_values,
- )
- else:
- logits, _, past_key_values = self.model.inference(
- input=input[:, -1:],
- context=context,
- length=length,
- span=span,
- past_key_values=past_key_values,
- )
-
- assert logits.size(0) > 1, "nbce needs to ensure that the length of logits 0 is greater than 1"
- logits = NBCE(logits) # [vocab_size]
- logits = logits[None]
-
- if i == 0:
- logits[:, self.tokenizer.bos_token_id] = -float("inf")
- # logits[:, self.tokenizer.newline_id] = -float("inf")
-
- apply_repetition_penalty(
- logits,
- 1,
- 1,
- input,
- repetition_penalty,
- pred_start_index,
- input.size(-1) - 1,
- repetition_window,
- )
-
- logits = logits / temperature
- logits = top_k_top_p_filtering(logits, top_k=top_k, top_p=top_p)
-
- probs = F.softmax(logits, dim=-1)
- next_token = torch.multinomial(probs, num_samples=1)
-
- for idx in range(1):
- if not done[idx] and (next_token[idx].item() == self.tokenizer.bos_token_id or i == max_length - 1):
- done[idx] = True
- results[idx] = input[idx, pred_start_index:].clone().cpu().tolist() # type: ignore # noqa: E501
-
- if sum(done) == 1:
- break
- next_token = next_token.tile(batch_size, 1)
- # update input ids
- input = torch.cat([input, next_token], dim=-1)
- length = past_key_values["buffer_length"]
- length = torch.cat(
- [length, torch.ones((length.size(0), 1), dtype=torch.int32, device=length.device)],
- dim=-1,
- )
- # length += 1
- context = torch.cat(
- [context, torch.zeros((context.size(0), 1), dtype=torch.int16, device=context.device)],
- dim=-1,
- )
- span = torch.cat(
- [span, torch.zeros((span.size(0), 1), dtype=torch.int32, device=span.device)],
- dim=-1,
- )
-
- result_text = list(map(self.tokenizer.decode, results))
- return result_text
diff --git a/9G-Train/cpm/cpm9g/models/__init__.py b/9G-Train/cpm/cpm9g/models/__init__.py
deleted file mode 100644
index 4732e0d..0000000
--- a/9G-Train/cpm/cpm9g/models/__init__.py
+++ /dev/null
@@ -1,3 +0,0 @@
-from .cpm9g import CPM9G
-from .cpm9g import CPM9GConfig
-from .cpm9g_torch import CPM9GTorch
diff --git a/9G-Train/cpm/cpm9g/models/cpm9g.py b/9G-Train/cpm/cpm9g/models/cpm9g.py
deleted file mode 100644
index 881faff..0000000
--- a/9G-Train/cpm/cpm9g/models/cpm9g.py
+++ /dev/null
@@ -1,272 +0,0 @@
-from typing import List
-from typing import Optional
-from typing import Tuple
-
-import bmtrain as bmt
-import torch
-import torch.nn.functional as F
-from typing_extensions import TypedDict
-
-from ...layers import Embedding
-from ...layers import Encoder
-from ...layers import RotaryEmbeddingESM
-from ...utils import Config
-from ...utils import gradient_shrink
-
-
-class CPM9GInferenceState(TypedDict):
- buffer_context: torch.Tensor
- buffer_sample_ids: torch.Tensor
- buffer: List[Tuple[torch.Tensor, torch.Tensor]]
-
-
-class CPM9GConfig(Config):
- def __init__(
- self,
- vocab_size=32000,
- dim_model=4096,
- num_heads=32,
- num_kv_heads=32,
- dim_head=128,
- dim_ff=11008,
- num_layers=32,
- dropout_p=0.0,
- activate_fn="silu",
- scale=True,
- eps=1e-5,
- half: bool = True,
- bf16: bool = False,
- mask_modules: Optional[List[Tuple[bool, bool]]] = None,
- use_flash_attn: bool = True,
- flash_attn_mask_shape="1d",
- flash_impl="cuda",
- base=10000,
- tp=0,
- disabled_checkpoint=None,
- ):
- super().__init__()
- self.vocab_size = vocab_size
- self.dim_model = dim_model
- self.num_heads = num_heads
- self.num_kv_heads = num_kv_heads
- self.dim_head = dim_head
- self.dim_ff = dim_ff
- self.num_layers = num_layers
- self.dropout_p = dropout_p
- self.activate_fn = activate_fn
- self.scale = scale
- self.eps = eps
- if half:
- if bf16:
- self.dtype = torch.bfloat16
- else:
- self.dtype = torch.half
- else:
- self.dtype = torch.float
- self.flash_impl = flash_impl
- self.mask_modules = mask_modules
- self.use_flash_attn = use_flash_attn
- self.flash_attn_mask_shape = flash_attn_mask_shape
- self.base = base
- self.tp = tp
- self.disabled_checkpoint = disabled_checkpoint
-
-
-class CPM9G(bmt.DistributedModule):
- def __init__(self, config: CPM9GConfig):
- super().__init__()
-
- self.encoder = Encoder(
- num_layers=config.num_layers,
- dim_model=config.dim_model,
- dim_ff=config.dim_ff,
- num_heads=config.num_heads,
- num_kv_heads=config.num_kv_heads,
- dim_head=config.dim_head,
- activate_fn=config.activate_fn,
- dtype=config.dtype,
- eps=config.eps,
- dropout_p=config.dropout_p,
- scale=config.scale,
- mask_modules=config.mask_modules,
- use_flash_attn=config.use_flash_attn,
- tp=config.tp,
- disabled_checkpoint=config.disabled_checkpoint,
- )
-
- self.input_embedding = Embedding(
- vocab_size=config.vocab_size,
- embedding_size=config.dim_model,
- scale=config.scale,
- dtype=config.dtype,
- init_std=0.02,
- )
-
- self.position_bias = RotaryEmbeddingESM(
- dim=config.dim_head, dtype=config.dtype, base=config.base, persistent=False, mixed_precision=True
- )
-
- self.lm_head = Embedding(
- vocab_size=config.vocab_size,
- embedding_size=config.dim_model,
- scale=config.scale,
- dtype=config.dtype,
- init_std=0.02,
- )
- self.flash_impl = config.flash_impl
- self.use_flash_attn = config.use_flash_attn
- self.flash_attn_mask_shape = config.flash_attn_mask_shape
- self.config = config
-
- def forward(
- self,
- input: torch.Tensor, # (batch, seqlen) int32
- length: torch.Tensor = None, # (batch) int32
- context: torch.Tensor = None, # (batch, seqlen) bool
- span: torch.Tensor = None, # (batch, seqlen) int32
- cu_seqlens: torch.Tensor = None, # (real_batch+2) int32
- max_seqlen: int = None,
- position_ids: torch.Tensor = None, # (batch, seqlen) int32
- ):
- batch = input.size(0)
- seqlen = input.size(1)
- device = input.device
-
- if length is not None and length.dim() == 1:
- length = torch.arange(seqlen, device=device)[None, :].repeat(batch, 1) < length[:, None]
-
- # processing masks and position bias bucket
- if not self.use_flash_attn or (self.flash_attn_mask_shape == "2d" and self.flash_impl == "triton"):
- with torch.no_grad():
- # directional mask
- directional_mask_2d = torch.arange(seqlen, device=device) <= torch.arange(seqlen, device=device).view(
- -1, 1
- )
- # context mask
- attention_mask = context[:, None, :] | (
- context[:, :, None].logical_not() & directional_mask_2d.view(1, seqlen, seqlen)
- )
- # span mask
- attention_mask = attention_mask & (span[:, None, :] == span[:, :, None])
- # length mask
- attention_mask = length.view(batch, seqlen, 1) & length.view(batch, 1, seqlen) & attention_mask
- else:
- attention_mask = None
-
- hidden_states = self.input_embedding(input)
-
- if self.config.tp:
- with torch.no_grad():
- if length is not None:
- length = bmt.distributed.all_gather(length, comm=bmt.config["tp_comm"]).flatten(0, 1)
- if attention_mask is not None:
- attention_mask = bmt.distributed.all_gather(attention_mask, comm=bmt.config["tp_comm"]).flatten(
- 0, 1
- )
- if cu_seqlens is not None:
- lens = bmt.distributed.all_gather(
- torch.tensor([cu_seqlens.numel()]).cuda(), comm=bmt.config["tp_comm"]
- )
- mx = lens.max().item()
- cu_seq = torch.zeros(mx).int().cuda()
- cu_seq[: cu_seqlens.numel()] = cu_seqlens
- all_seq = bmt.distributed.all_gather(cu_seq, comm=bmt.config["tp_comm"])
- cu_seqlens = [0]
- for i, l in enumerate(lens):
- for j in range(1, l - 1):
- cu_seqlens.append(all_seq[i][j] + i * seqlen)
- cu_seqlens.append((i + 1) * seqlen)
- cu_seqlens = torch.tensor(cu_seqlens, dtype=torch.int32).cuda()
- if max_seqlen is not None:
- max_seqlen = bmt.distributed.all_reduce(
- torch.tensor([max_seqlen]).cuda(), "max", comm=bmt.config["tp_comm"]
- )[0].item()
- if position_ids is not None:
- position_ids = bmt.distributed.all_gather(position_ids, comm=bmt.config["tp_comm"]).flatten(0, 1)
-
- if self.use_flash_attn:
- if self.flash_attn_mask_shape == "1d":
- hidden_states = self.encoder(
- hidden_states,
- attention_mask=None,
- position_bias=self.position_bias,
- pos_bias_type="rotary",
- length_mask=length,
- )
- else:
- if self.flash_impl == "triton":
- mask = attention_mask.unsqueeze(dim=1).contiguous()
- attention_mask_bias = torch.zeros_like(mask, device="cuda", dtype=torch.float16)
- attention_mask_bias[mask == False] -= torch.inf
- else:
- attention_mask_bias = None
- assert cu_seqlens is not None, "cu_seqlens are needed in Flash Attention cuda impl"
- hidden_states = self.encoder(
- hidden_states,
- attention_mask=None,
- position_bias=self.position_bias,
- pos_bias_type="rotary",
- length_mask=None,
- attention_mask_bias=attention_mask_bias,
- cu_seqlens=cu_seqlens,
- max_seqlen=max_seqlen,
- position_ids=position_ids,
- )
- else:
- hidden_states = self.encoder(
- hidden_states, attention_mask=attention_mask, position_bias=self.position_bias, pos_bias_type="rotary"
- )
-
- logits = self.lm_head.projection(hidden_states)
-
- return logits, hidden_states
-
- def inference(
- self,
- input: torch.Tensor, # (batch, len_q) int32
- length: torch.Tensor, # (batch) int32
- context: torch.Tensor, # (batch, seqlen) int16
- span: torch.Tensor, # (batch, seqlen) int32
- past_key_values: Optional[CPM9GInferenceState] = None,
- ) -> Tuple[torch.Tensor, torch.Tensor, CPM9GInferenceState]:
- batch = input.size(0)
- len_q = input.size(1)
- len_buffer = 0
- if past_key_values is None:
- present_buffer = None
- else:
- present_buffer = past_key_values["buffer"]
- len_buffer = present_buffer[0][0].shape[-2]
- seqlen = len_buffer + len_q
- with torch.no_grad():
- device = input.device
- if length.dim() == 1:
- length = torch.arange(seqlen, device=device)[None, :].repeat(batch, 1) < length[:, None]
- directional_mask_2d = torch.arange(seqlen, device=device) <= torch.arange(seqlen, device=device).view(-1, 1)
- # context mask
- attention_mask = context[:, None, :] | (
- context[:, :, None].logical_not() & directional_mask_2d.view(1, seqlen, seqlen)
- )
- # span mask
- attention_mask = attention_mask & (span[:, None, :] == span[:, :, None])
- # length mask
- attention_mask = length.view(batch, seqlen, 1) & length.view(batch, 1, seqlen) & attention_mask
-
- hidden_states = self.input_embedding(input)
-
- hidden_states, present_key_values, _ = self.encoder(
- hidden_states,
- attention_mask=attention_mask[:, len_buffer:],
- position_bias=self.position_bias,
- use_cache=True,
- past_key_values=present_buffer,
- pos_bias_type="rotary",
- )
-
- logits = self.lm_head.projection(hidden_states)
-
- return (
- logits,
- hidden_states,
- {"buffer": present_key_values},
- )
diff --git a/9G-Train/cpm/cpm9g/models/cpm9g_torch.py b/9G-Train/cpm/cpm9g/models/cpm9g_torch.py
deleted file mode 100644
index 0f717a6..0000000
--- a/9G-Train/cpm/cpm9g/models/cpm9g_torch.py
+++ /dev/null
@@ -1,186 +0,0 @@
-from typing import List
-from typing import Optional
-from typing import Tuple
-
-import torch
-import torch.nn.functional as F
-from typing_extensions import TypedDict
-
-from ...native_layers import Embedding
-from ...native_layers import Encoder
-from ...native_layers import RotaryEmbeddingESM
-from ...utils import Config
-from ...utils import gradient_shrink
-from .cpm9g import CPM9GConfig
-
-
-class CPM9GInferenceState(TypedDict):
- buffer_context: torch.Tensor
- buffer_sample_ids: torch.Tensor
- buffer: List[Tuple[torch.Tensor, torch.Tensor]]
-
-
-class CPM9GTorch(torch.nn.Module):
- def __init__(self, config: CPM9GConfig):
- super().__init__()
-
- self.encoder = Encoder(
- num_layers=config.num_layers,
- dim_model=config.dim_model,
- dim_ff=config.dim_ff,
- num_heads=config.num_heads,
- num_kv_heads=config.num_kv_heads,
- dim_head=config.dim_head,
- activate_fn=config.activate_fn,
- dtype=config.dtype,
- eps=config.eps,
- dropout_p=config.dropout_p,
- scale=config.scale,
- mask_modules=config.mask_modules,
- use_flash_attn=config.use_flash_attn,
- )
-
- self.input_embedding = Embedding(
- vocab_size=config.vocab_size,
- embedding_size=config.dim_model,
- scale=config.scale,
- dtype=config.dtype,
- init_std=0.02,
- )
-
- self.position_bias = RotaryEmbeddingESM(
- dim=config.dim_head, dtype=config.dtype, base=config.base, persistent=False, mixed_precision=True
- )
-
- self.lm_head = Embedding(
- vocab_size=config.vocab_size,
- embedding_size=config.dim_model,
- scale=config.scale,
- dtype=config.dtype,
- init_std=0.02,
- )
- self.flash_impl = False
- self.use_flash_attn = False
- self.flash_attn_mask_shape = "1d"
-
- def forward(
- self,
- input: torch.Tensor, # (batch, seqlen) int32
- length: torch.Tensor = None, # (batch) int32
- context: torch.Tensor = None, # (batch, seqlen) bool
- span: torch.Tensor = None, # (batch, seqlen) int32
- cu_seqlens: torch.Tensor = None, # (real_batch+2) int32
- max_seqlen: int = None,
- position_ids: torch.Tensor = None, # (batch, seqlen) int32
- ):
- batch = input.size(0)
- seqlen = input.size(1)
- device = input.device
-
- if length is not None and length.dim() == 1:
- length = torch.arange(seqlen, device=device)[None, :].repeat(batch, 1) < length[:, None]
-
- # processing masks and position bias bucket
- if not self.use_flash_attn or (self.flash_attn_mask_shape == "2d" and self.flash_impl == "triton"):
- with torch.no_grad():
- # directional mask
- directional_mask_2d = torch.arange(seqlen, device=device) <= torch.arange(seqlen, device=device).view(
- -1, 1
- )
- # context mask
- attention_mask = context[:, None, :] | (
- context[:, :, None].logical_not() & directional_mask_2d.view(1, seqlen, seqlen)
- )
- # span mask
- attention_mask = attention_mask & (span[:, None, :] == span[:, :, None])
- # length mask
- attention_mask = length.view(batch, seqlen, 1) & length.view(batch, 1, seqlen) & attention_mask
-
- hidden_states = self.input_embedding(input)
-
- if self.use_flash_attn:
- if self.flash_attn_mask_shape == "1d":
- hidden_states = self.encoder(
- hidden_states,
- attention_mask=None,
- position_bias=self.position_bias,
- pos_bias_type="rotary",
- length_mask=length,
- )
- else:
- if self.flash_impl == "triton":
- mask = attention_mask.unsqueeze(dim=1).contiguous()
- attention_mask_bias = torch.zeros_like(mask, device="cuda", dtype=torch.float16)
- attention_mask_bias[mask == False] -= torch.inf
- else:
- attention_mask_bias = None
- assert cu_seqlens is not None, "cu_seqlens are needed in Flash Attention cuda impl"
- hidden_states = self.encoder(
- hidden_states,
- attention_mask=None,
- position_bias=self.position_bias,
- pos_bias_type="rotary",
- length_mask=None,
- attention_mask_bias=attention_mask_bias,
- cu_seqlens=cu_seqlens,
- max_seqlen=max_seqlen,
- position_ids=position_ids,
- )
- else:
- hidden_states = self.encoder(
- hidden_states, attention_mask=attention_mask, position_bias=self.position_bias, pos_bias_type="rotary"
- )
-
- logits = self.lm_head.projection(hidden_states)
-
- return logits, hidden_states
-
- def inference(
- self,
- input: torch.Tensor, # (batch, len_q) int32
- length: torch.Tensor, # (batch) int32
- context: torch.Tensor, # (batch, seqlen) int16
- span: torch.Tensor, # (batch, seqlen) int32
- past_key_values: Optional[CPM9GInferenceState] = None,
- ) -> Tuple[torch.Tensor, torch.Tensor, CPM9GInferenceState]:
- batch = input.size(0)
- len_q = input.size(1)
- len_buffer = 0
- if past_key_values is None:
- present_buffer = None
- else:
- present_buffer = past_key_values["buffer"]
- len_buffer = present_buffer[0][0].shape[-2]
- seqlen = len_buffer + len_q
- with torch.no_grad():
- device = input.device
- if length.dim() == 1:
- length = (torch.arange(seqlen, device=device)[None, :].repeat(batch, 1) + length[:, None]) >= seqlen
- directional_mask_2d = torch.arange(seqlen, device=device) <= torch.arange(seqlen, device=device).view(-1, 1)
- # context mask
- attention_mask = context[:, None, :] | (
- context[:, :, None].logical_not() & directional_mask_2d.view(1, seqlen, seqlen)
- )
- # span mask
- attention_mask = attention_mask & (span[:, None, :] == span[:, :, None])
- # length mask
- attention_mask = length.view(batch, seqlen, 1) & length.view(batch, 1, seqlen) & attention_mask
-
- hidden_states = self.input_embedding(input)
-
- hidden_states, present_key_values, _ = self.encoder(
- hidden_states,
- attention_mask=attention_mask[:, len_buffer:],
- position_bias=self.position_bias,
- use_cache=True,
- past_key_values=present_buffer,
- pos_bias_type="rotary",
- )
-
- logits = self.lm_head.projection(hidden_states)
-
- return (
- logits,
- hidden_states,
- {"buffer": present_key_values},
- )
diff --git a/9G-Train/cpm/cpm9g/tokenizers/__init__.py b/9G-Train/cpm/cpm9g/tokenizers/__init__.py
deleted file mode 100644
index 4c62955..0000000
--- a/9G-Train/cpm/cpm9g/tokenizers/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from .cpm9g import CPM9GTokenizer
diff --git a/9G-Train/cpm/cpm9g/training_tasks/__init__.py b/9G-Train/cpm/cpm9g/training_tasks/__init__.py
deleted file mode 100644
index 684f707..0000000
--- a/9G-Train/cpm/cpm9g/training_tasks/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from .pretrain import MixedDataset
-from .finetune import FinetuneDataset
diff --git a/9G-Train/cpm/cpm9g/training_tasks/finetune.py b/9G-Train/cpm/cpm9g/training_tasks/finetune.py
deleted file mode 100644
index d16e3db..0000000
--- a/9G-Train/cpm/cpm9g/training_tasks/finetune.py
+++ /dev/null
@@ -1,87 +0,0 @@
-import bmtrain as bmt
-
-from ...dataset import SimpleDataset
-from ..tokenizers import CPM9GTokenizer
-from .pretrain import _MixedDatasetBatchPacker
-from .pretrain import _MixedDatasetConfig
-from .pretrain import CPM9GBatch
-
-
-class FinetuneDataset:
- def __init__(
- self,
- dataset_path: str,
- batch_size: int,
- max_length: int,
- tokenizer: CPM9GTokenizer,
- unpad: bool = False,
- task_name: str = "task",
- drop_last: bool = False,
- ) -> None:
- self._world_size = bmt.world_size()
- self._rank = bmt.rank()
- self._batch_size = batch_size
- self._unpad = unpad
- self._max_length = max_length
-
- self._packer = _MixedDatasetBatchPacker(batch_size * self._world_size, max_length, tokenizer, unpad)
- self._drop_last = drop_last
-
- ds = SimpleDataset(dataset_path, shuffle=False)
- self._ds_cfg: _MixedDatasetConfig = {
- "weight": 1.0,
- "path": dataset_path,
- "transforms": [],
- "task_name": task_name,
- "dataset_name": "finetune",
- "incontext_weight": [1.0],
- "lines": len(ds),
- "dataset": ds,
- }
- self._nlines = len(ds)
- self._nbytes = ds.get_bytes()
-
- def __batch_iter(self):
- while True:
- try:
- batch = self._packer.add_data(self._ds_cfg)
- except EOFError:
- break
- if batch is None:
- continue
- yield batch
- if len(self._packer) > 0:
- batch = self._packer.pack_batch(force=True)
- if not self._drop_last:
- yield batch
- self._ds_cfg["dataset"]._repeat_times = 0
-
- def __iter__(self):
- if not self._unpad:
- batch_st = self._batch_size * self._rank
- batch_end = self._batch_size * (self._rank + 1)
- for batch in self.__batch_iter():
- batch_size = batch["inputs"].shape[0]
- if batch_size <= batch_st:
- yield None
- else:
- ret: CPM9GBatch = {
- kw: val[batch_st:batch_end] # type: ignore
- for kw, val in batch.items()
- if kw not in ["task_names", "raw_data", "cu_seqlens", "max_seqlen"]
- } # type: ignore
- ret["task_names"] = batch["task_names"]
- ret["raw_data"] = batch["raw_data"]
- ret["cu_seqlens"] = batch["cu_seqlens"]
- ret["max_seqlen"] = batch["max_seqlen"]
- yield ret
- else:
- for batch in self.__batch_iter():
- assert batch["inputs"].shape[0] == 1
- yield batch
-
- def __len__(self):
- iter_count = int(self._nbytes // (3.6 * self._batch_size * self._world_size * self._max_length))
- if not self._drop_last:
- iter_count += 1
- return iter_count
\ No newline at end of file
diff --git a/9G-Train/cpm/cpm9g/training_tasks/pretrain.py b/9G-Train/cpm/cpm9g/training_tasks/pretrain.py
deleted file mode 100644
index e36e93b..0000000
--- a/9G-Train/cpm/cpm9g/training_tasks/pretrain.py
+++ /dev/null
@@ -1,736 +0,0 @@
-import importlib.machinery
-import importlib.util
-import json
-import multiprocessing
-import os
-import random
-import time
-import types
-from collections import OrderedDict
-from queue import Empty
-from typing import Any
-from typing import Callable
-from typing import Dict
-from typing import List
-from typing import Optional
-from typing import Set
-from typing import Tuple
-from typing import Union
-
-import bmtrain as bmt
-import numpy as np
-import torch
-from numpy.typing import NDArray
-from typing_extensions import TypedDict
-
-from ...dataset import DistributedDataset
-from ..tokenizers import CPM9GTokenizer
-
-
-class _MixedDatasetConfig(TypedDict):
- weight: float
- path: str
- transforms: Union[List[Dict[str, Any]], str]
- task_name: str
- dataset_name: str
- incontext_weight: List[float]
- lines: int
- dataset: DistributedDataset
-
-
-CPM9GInputType = Union[str, Dict[str, "CPM9GInputType"]]
-
-
-class _TransformFuncDict(TypedDict):
- loader: importlib.machinery.SourceFileLoader
- module: types.ModuleType
- last_m: float
-
-
-_TransformFunction = Callable[[CPM9GInputType, int, random.Random], CPM9GInputType]
-
-
-class CPM9GBatch(TypedDict):
- inputs: NDArray[np.int32]
- length: NDArray[np.int32]
- context: NDArray[np.bool_]
- sample_ids: NDArray[np.int32]
- spans: NDArray[np.int32]
- target: NDArray[np.int32]
- task_ids: NDArray[np.int32]
- task_names: List[str]
- raw_data: List[Any]
-
-
-def convert_data_to_id(tokenizer: CPM9GTokenizer, data: Any):
- input_ids = tokenizer.encode(data["input"])
- output_ids = tokenizer.encode(data["output"])
- ids = [tokenizer.bos_id] + input_ids + output_ids + [tokenizer.eos_id]
- ids = np.array(ids, dtype=np.int32)
- context = np.zeros((ids.shape[0],), dtype=np.int8)
- context[: len(input_ids) + 1] = 1
- return ids, context
-
-
-def _dataset_identity(c: _MixedDatasetConfig):
- return "{}.{}".format(c["task_name"], c["dataset_name"])
-
-
-class _MixedDatasetBatchPacker:
- def __init__(
- self,
- batch_size: int,
- max_length: int,
- tokenizer: CPM9GTokenizer,
- unpad: bool = False,
- ) -> None:
- self._batch_size = batch_size
- self._max_length = max_length
- self._clip_length = max_length
- self.tokenizer = tokenizer
- self._transform_func_table: Dict[str, _TransformFuncDict] = {}
- self._unpad = unpad
- if unpad:
- self._max_length = max_length * batch_size
- self._batch_size = 1
-
- self._inputs: List[NDArray[np.int32]] = []
- self._context: List[NDArray[np.int8]] = []
- self._sample_ids: List[NDArray[np.int32]] = []
- self._spans: List[List[int]] = []
- self._task_ids: List[List[str]] = []
- self._raw_data: List[List[Any]] = []
-
- def __len__(self):
- return len(self._inputs)
-
- def apply_transform(
- self,
- data: CPM9GInputType,
- transform: Union[Dict[str, Any], Callable[[CPM9GInputType], CPM9GInputType], None],
- ) -> CPM9GInputType:
- if transform is None:
- return data
- if not isinstance(transform, dict):
- return transform(data) # transform function
-
- mapping_list: List[Tuple[str, str]] = []
-
- def _walk_transform_dict(data: Union[Dict[str, Any], str], prefix: str = ""):
- if isinstance(data, dict):
- for k, v in data.items():
- if len(prefix) > 0:
- _walk_transform_dict(v, prefix + "." + k)
- else:
- _walk_transform_dict(v, k)
- else:
- assert isinstance(data, str), "Invalid transform {}".format(data)
- mapping_list.append((prefix, data))
-
- _walk_transform_dict(transform)
-
- expanded_mapping_list: List[Tuple[str, Any]] = []
-
- def _expand_mapping(data: CPM9GInputType, stars: List[str], path: List[str], target: List[str]):
- if len(path) == 0:
- num_stars = 0
- for it in target:
- if it == "*":
- num_stars += 1
- if num_stars != len(stars):
- raise ValueError("Invalid transform {}".format(".".join(target)))
-
- nw_tgt = []
- num_stars = 0
- for it in target:
- if it == "*":
- nw_tgt.append(stars[num_stars])
- num_stars += 1
- else:
- nw_tgt.append(it)
- expanded_mapping_list.append((".".join(nw_tgt), data))
- else:
- if not isinstance(data, dict):
- raise ValueError("Invalid data {}".format(data))
- if path[0] == "*":
- for k, v in data.items():
- _expand_mapping(v, stars + [k], path[1:], target)
- else:
- _expand_mapping(data[path[0]], stars, path[1:], target)
-
- # expand mapping list
- for tgt, src in mapping_list:
- if src.startswith("$"):
- # copy from src
- _expand_mapping(data, [], src[1:].split("."), tgt.split("."))
- else:
- if "*" in tgt:
- raise ValueError("Constant value is not allowed to have `*` in prefix")
- expanded_mapping_list.append((tgt, src))
-
- ret = {}
- for tgt, val in expanded_mapping_list:
- tgt = tgt.split(".")
- cur = ret
- while len(tgt) > 1:
- cur = cur[tgt[0]]
- tgt = tgt[1:]
- cur[tgt[0]] = val
- return ret
-
- def data_to_id(self, data: Any):
- return convert_data_to_id(self.tokenizer, data)
-
- def _ensure_transform_function(self, module_name: str, transform_script_path: str) -> _TransformFunction:
- module_name = "cpm_live.transforms.{}".format(module_name)
- if transform_script_path not in self._transform_func_table:
- loader = importlib.machinery.SourceFileLoader(module_name, transform_script_path)
- spec = importlib.util.spec_from_loader(loader.name, loader)
- if spec is None:
- raise RuntimeError("spec is none! {}".format(module_name))
- mod = importlib.util.module_from_spec(spec)
- self._transform_func_table[transform_script_path] = {
- "loader": loader,
- "module": mod,
- "last_m": 0,
- }
-
- transform_script_info = self._transform_func_table[transform_script_path]
- curr_m_time = float(transform_script_info["loader"].path_stats(transform_script_path)["mtime"])
- if curr_m_time > transform_script_info["last_m"]:
- transform_script_info["last_m"] = curr_m_time
- transform_script_info["loader"].exec_module(transform_script_info["module"])
- transform_func = getattr(transform_script_info["module"], "transform", None)
- if transform_func is None:
-
- def _empty_transform_func(data: CPM9GInputType, num_sample: int, r: random.Random):
- raise NotImplementedError("Transform func for dataset {} not implemented".format(module_name))
-
- return _empty_transform_func
- else:
- return transform_func
-
- def build_instance(self, config: _MixedDatasetConfig):
- _sample_weight = np.array(config["incontext_weight"], dtype=np.float32)
- _sample_weight = _sample_weight / _sample_weight.sum()
- num_incontext = np.random.choice(_sample_weight.shape[0], p=_sample_weight)
- ds = config["dataset"]
- transforms = config["transforms"]
- if isinstance(transforms, str):
- if not os.path.exists(transforms):
- raise RuntimeError("transform script {} file not exists".format(transforms))
- # load transform script
- transform_func = self._ensure_transform_function(_dataset_identity(config), transforms)
- seed = random.random()
-
- def _transform(data: CPM9GInputType):
- r = random.Random(seed)
- return transform_func(data, num_incontext, r)
-
- transform = _transform
- elif len(transforms) == 0:
- transform = None
- else:
- transform = transforms[np.random.choice(len(transforms))]
-
- raw_data = {}
- while True:
- inp = ds.read()
- inp = self.apply_transform(inp, transform)
- # if None, skip this one
- if inp is None:
- continue
- input_ids, context = self.data_to_id(inp)
- if input_ids.shape[0] > self._clip_length:
- continue # too long
- input_ids = input_ids[: self._clip_length]
- context = context[: self._clip_length]
- raw_data["input"] = inp
- raw_data["samples"] = []
- break
-
- sample_ids = np.zeros(input_ids.shape, dtype=np.int32)
- i = 0
- while True:
- if i == num_incontext or input_ids.shape[0] >= self._clip_length:
- break # early break
- sample = ds.read()
- sample = self.apply_transform(sample, transform)
- # if sample is None, skip this one
- if sample is None:
- continue
- sample_input_ids, _ = self.data_to_id(sample)
- if input_ids.shape[0] + sample_input_ids.shape[0] > self._clip_length:
- break # too long, break
- raw_data["samples"].append(sample)
- input_ids = np.concatenate([input_ids, sample_input_ids], axis=0)
- context = np.concatenate([context, np.ones(sample_input_ids.shape, dtype=np.int8)], axis=0)
- sample_ids = np.concatenate([sample_ids, np.full(sample_input_ids.shape, i + 1, dtype=np.int32)], axis=0)
-
- i += 1
-
- return (
- input_ids,
- context,
- sample_ids,
- raw_data,
- )
-
- def pack_batch(self, force: bool = False, unilm=False) -> CPM9GBatch:
- # pack batch
- if len(self._inputs) < self._batch_size:
- if not force:
- raise RuntimeError("Batch insufficient")
- batch_size = len(self._inputs)
- else:
- batch_size = self._batch_size
- max_length = self._max_length # self._spans[0][-1] if self._unpad else self._max_length
- inputs = np.zeros((batch_size, max_length), dtype=np.int32)
- context = np.zeros((batch_size, max_length), dtype=np.int8)
- sample_ids = np.zeros((batch_size, max_length), dtype=np.int32)
- tgt = np.full((batch_size, max_length), -100, dtype=np.int32)
-
- spans = np.zeros((batch_size, max_length), dtype=np.int32)
- length = np.zeros((batch_size,), dtype=np.int32)
- task_ids = np.full((batch_size, max_length), -1, dtype=np.int32)
- if self._spans[0][-1] != max_length:
- cu_seqlens = np.array([0] + self._spans[0] + [max_length], dtype=np.int32)
- else:
- cu_seqlens = np.array([0] + self._spans[0], dtype=np.int32)
- max_seqlen = int(np.max(cu_seqlens[1:] - cu_seqlens[:-1]))
- position_ids = np.zeros((batch_size, max_length), dtype=np.int32)
-
- all_task_names: Set[str] = set()
- for i in range(batch_size):
- for task_name in self._task_ids[i]:
- all_task_names.add(task_name)
- task_names: List[str] = list(all_task_names)
- task_name_to_id = {name: i for i, name in enumerate(task_names)}
-
- raw_data_list: List[Any] = []
- for i in range(batch_size):
- instance_length = self._inputs[i].shape[0]
- inputs[i, :instance_length] = self._inputs[i]
- sample_ids[i, :instance_length] = self._sample_ids[i]
- if unilm:
- context[i, :instance_length] = self._context[i]
-
- span_begin = 0
- for span_id, (span_end, task_name) in enumerate(zip(self._spans[i], self._task_ids[i])):
- spans[i, span_begin:span_end] = span_id
- position_ids[i, span_begin:span_end] = np.arange(span_end - span_begin)
- task_ids[i, span_begin:span_end] = task_name_to_id[task_name]
- span_begin = span_end
- length[i] = instance_length
- raw_data_list.extend(self._raw_data[i])
-
- for j in range(instance_length):
- if j > 1 and self._context[i][j] == 0:
- if self._inputs[i][j] != self.tokenizer.bos_id and self._inputs[i][j - 1] != self.tokenizer.eos_id:
- tgt[i, j - 1] = self._inputs[i][j]
-
- self._inputs = self._inputs[batch_size:]
- self._context = self._context[batch_size:]
- self._sample_ids = self._sample_ids[batch_size:]
- self._spans = self._spans[batch_size:]
- self._task_ids = self._task_ids[batch_size:]
- self._raw_data = self._raw_data[batch_size:]
- return {
- "inputs": inputs,
- "length": length,
- "context": context > 0,
- "sample_ids": sample_ids,
- "spans": spans,
- "cu_seqlens": cu_seqlens,
- "max_seqlen": max_seqlen,
- "position_ids": position_ids,
- "target": tgt,
- "task_ids": task_ids,
- "task_names": task_names,
- "raw_data": raw_data_list,
- }
-
- def add_data(self, config: _MixedDatasetConfig) -> Optional[CPM9GBatch]:
- (
- input_ids,
- context,
- sample_ids,
- raw_data,
- ) = self.build_instance(config)
-
- # add to batch
- best_fit: Union[None, int] = None
- best_fit_space: Union[None, int] = None
- for i in range(len(self._inputs)):
- space = self._max_length - self._inputs[i].shape[0]
- if input_ids.shape[0] <= space:
- if best_fit_space is None:
- best_fit = i
- best_fit_space = space
- elif best_fit_space > space:
- best_fit = i
- best_fit_space = space
- if best_fit is None:
- # add a new instance
- self._inputs.append(input_ids)
- self._context.append(context)
- self._sample_ids.append(sample_ids)
- self._spans.append([input_ids.shape[0]])
- self._task_ids.append([config["task_name"]])
- self._raw_data.append([raw_data])
- else:
- # add to existing instance
- self._inputs[best_fit] = np.concatenate([self._inputs[best_fit], input_ids], axis=0)
- self._context[best_fit] = np.concatenate([self._context[best_fit], context], axis=0)
- self._sample_ids[best_fit] = np.concatenate([self._sample_ids[best_fit], sample_ids], axis=0)
- self._spans[best_fit].append(self._inputs[best_fit].shape[0])
- self._task_ids[best_fit].append(config["task_name"])
- self._raw_data[best_fit].append(raw_data)
-
- if len(self._inputs) > self._batch_size:
- return self.pack_batch()
- else:
- return None # not ready
-
-
-class _MixedDatasetConfigMananger:
- def __init__(self, config_path: str) -> None:
- self._config_path: str = config_path
- self._config: Union[List[_MixedDatasetConfig], None] = None
- self._last_m = 0
-
- def changed(self):
- while True:
- try:
- m_time = os.stat(self._config_path).st_mtime
- if m_time > self._last_m:
- # try to load new config
- try:
- self._config = json.load(open(self._config_path, "r", encoding="utf-8"))
- except Exception:
- # failed to load config
- return False
- # new config loaded
- self._last_m = m_time
- return True
- return False
- except Exception:
- print("Error: reading info list! {}".format(self._config_path))
- time.sleep(30)
-
- def get_config(self) -> List[_MixedDatasetConfig]:
- if self._config is None:
- if not self.changed():
- raise RuntimeError("Failed to load config")
- if self._config is None:
- raise RuntimeError("Failed to load config")
- return self._config
-
-
-def _mixed_dataset_process(
- config_path: str,
- q_cmd: multiprocessing.Queue,
- q_cmd_out: multiprocessing.Queue,
- q_data: multiprocessing.Queue,
- rank: int,
- world_size: int,
- packer: _MixedDatasetBatchPacker,
- max_repeat_times: int = None,
- random_state=None,
-):
- for _ in range(rank):
- np.random.random()
- random.setstate(random_state)
- # ignore SIGINT
- import signal
-
- signal.signal(signal.SIGINT, signal.SIG_IGN)
- config_base_path = os.path.dirname(os.path.abspath(config_path))
-
- # fix libgomp threading deadlock after fork(), use single-thread in data process.
- # REF: https://github.com/pytorch/pytorch/issues/17199
- torch.set_num_threads(1)
-
- def _convert_to_abs_path(transform_path: str):
- if transform_path.startswith("/"):
- return transform_path
- else:
- return os.path.join(config_base_path, transform_path)
-
- def _build_sample_weights(config: List[_MixedDatasetConfig]):
- if len(config) == 0:
- return np.array([], dtype=np.float32)
- weights = [c["weight"] * c["lines"] for c in config]
- weights = np.array(weights, dtype=np.float32)
- sm_weight = weights.sum()
- if sm_weight > 0:
- weights = weights / sm_weight
- return weights
- else:
- raise RuntimeError("Empty datasets")
-
- cfg_mgr = _MixedDatasetConfigMananger(config_path)
- config = cfg_mgr.get_config()
-
- for c in config:
- ds = DistributedDataset(
- _convert_to_abs_path(c["path"]),
- rank,
- world_size,
- max_repeat_times=max_repeat_times,
- )
-
- c["lines"] = ds._nlines
- c["dataset"] = ds
- if "weight" not in c:
- c["weight"] = 1.0
- if "transforms" not in c:
- c["transforms"] = []
- elif isinstance(c["transforms"], str):
- c["transforms"] = _convert_to_abs_path(c["transforms"])
- if "incontext_weight" not in c:
- c["incontext_weight"] = [1.0]
-
- weights = _build_sample_weights(config)
-
- should_stop = False
- should_start = False
-
- while not should_stop:
- # update config first
- if cfg_mgr.changed():
- path_ds_map: Dict[str, _MixedDatasetConfig] = {}
- nw_path_set: Set[str] = set()
-
- # load new config
- nw_config = cfg_mgr.get_config()
-
- # build path -> dataset map
- for c in config:
- path_ds_map[_dataset_identity(c)] = c
-
- # add new datasets
- for c in nw_config:
- if _dataset_identity(c) in path_ds_map:
- # update values only
- if "weight" in c:
- path_ds_map[_dataset_identity(c)]["weight"] = c["weight"]
- if "transform" in c:
- if isinstance(c["transforms"], str):
- path_ds_map[_dataset_identity(c)]["transforms"] = _convert_to_abs_path(c["transforms"])
- else:
- path_ds_map[_dataset_identity(c)]["transforms"] = c["transforms"]
- if "incontext_weight" in c:
- path_ds_map[_dataset_identity(c)]["incontext_weight"] = c["incontext_weight"]
- else:
- # new dataset
- ds = DistributedDataset(
- _convert_to_abs_path(c["path"]),
- rank,
- world_size,
- max_repeat_times=max_repeat_times,
- )
- c["lines"] = ds._nlines
- c["dataset"] = ds
- if "weight" not in c:
- c["weight"] = 1.0
- if "transforms" not in c:
- c["transforms"] = []
- elif isinstance(c["transforms"], str):
- c["transforms"] = _convert_to_abs_path(c["transforms"])
- if "incontext_weight" not in c:
- c["incontext_weight"] = [1.0]
- path_ds_map[_dataset_identity(c)] = c
- nw_path_set.add(_dataset_identity(c))
-
- # remove unused datasets
- for c in config:
- if _dataset_identity(c) not in nw_path_set:
- del path_ds_map[_dataset_identity(c)]
-
- config: List[_MixedDatasetConfig] = []
- for c in nw_config:
- config.append(path_ds_map[_dataset_identity(c)])
- del path_ds_map
- del nw_path_set
- del nw_config
-
- weights = _build_sample_weights(config)
-
- # get cmds
- while True:
- try:
- cmd = q_cmd.get_nowait()
- except Empty:
- break
- if cmd == "stop":
- should_stop = True
- q_cmd_out.put(True)
- break
- elif cmd == "state_dict":
- ret = OrderedDict()
- for c in config:
- ds_name = _dataset_identity(c)
- ret[ds_name] = c["dataset"]._state_dict()
- q_cmd_out.put(ret)
- elif cmd == "load_state_dict":
- state_dict = q_cmd.get()
- missing = []
- for idx, c in enumerate(config):
- ds_name = _dataset_identity(c)
- print(f"loading {idx}/{len(config)} {ds_name}")
- if ds_name in state_dict:
- c["dataset"].load_state_dict(state_dict[ds_name], strict=False)
- else:
- # new dataset
- missing.append(ds_name)
- q_cmd_out.put(missing)
- elif cmd == "start":
- should_start = True
- q_cmd_out.put(True)
- else:
- raise RuntimeError("Unknown command: {}".format(cmd))
-
- if should_stop:
- break
-
- if not should_start:
- # wait for start cmd
- time.sleep(1)
- continue
-
- if len(config) == 0:
- # no dataset available
- time.sleep(1)
- continue
-
- if q_data.full():
- # queue full
- time.sleep(1)
- continue
-
- # sample a dataset
- ds_id: int = 0
-
- while True:
- ds_id = np.random.choice(weights.shape[0], p=weights)
- if config[ds_id]["dataset"]._nlines != config[ds_id]["lines"]:
- # dataset size changed
- for c in config:
- c["lines"] = c["dataset"]._nlines
- weights = _build_sample_weights(config)
- continue
- else:
- break
-
- batch = packer.add_data(config[ds_id])
- if batch is not None:
- # new batch comming
- q_data.put(batch)
-
- # clean queue
- while True:
- try:
- q_data.get_nowait()
- except Empty:
- break
-
-
-class MixedDataset:
- def __init__(
- self,
- config_path: str,
- batch_size: int,
- max_length: int,
- tokenizer: CPM9GTokenizer,
- unpad: bool = False,
- max_repeat_times: int = None,
- ) -> None:
- self._q_cmd = multiprocessing.Queue()
- self._q_cmd_out = multiprocessing.Queue()
- self._q_data = multiprocessing.Queue(maxsize=2)
- self._packer = _MixedDatasetBatchPacker(batch_size, max_length, tokenizer, unpad)
- self._p = multiprocessing.Process(
- target=_mixed_dataset_process,
- args=(
- config_path,
- self._q_cmd,
- self._q_cmd_out,
- self._q_data,
- bmt.rank(),
- bmt.world_size(),
- self._packer,
- max_repeat_times,
- random.getstate(),
- ),
- )
- self._p.start()
- self._closed = False
-
- def close(self):
- if not self._closed:
- self._closed = True
- self._q_cmd.put("stop")
- assert self._q_cmd_out.get(), "Failed to stop process"
- self._p.join()
-
- @property
- def closed(self):
- return self._closed
-
- def start(self):
- self._q_cmd.put("start")
- return self._q_cmd_out.get()
-
- def state_dict(self):
- self._q_cmd.put("state_dict")
- states = self._q_cmd_out.get()
- if not isinstance(states, OrderedDict):
- raise RuntimeError("Invalid state dict {}".format(states))
- if bmt.world_size() == 1:
- for val in states.values():
- val["states"].unsqueeze_(0)
- val["block"].unsqueeze_(0)
- return states
-
- ret = OrderedDict()
- for k, v in states.items():
- num_unused_block = v["states"].size(0)
- gpu_num_unused_block = torch.tensor([num_unused_block], dtype=torch.long).cuda()
- max_unused_blocks = bmt.distributed.all_reduce(gpu_num_unused_block, op="max").cpu().item()
- if max_unused_blocks == 0:
- max_unused_blocks = 1
- gpu_states = torch.full((max_unused_blocks,), -1, dtype=torch.long).cuda()
- gpu_states[:num_unused_block] = v["states"].cuda()
-
- gpu_block = v["block"].cuda()
- global_states = bmt.distributed.all_gather(gpu_states).cpu() # (world_size, max_unused_blocks)
- global_block = bmt.distributed.all_gather(gpu_block).cpu() # (world_size, 4)
- ret[k] = {"states": global_states, "block": global_block}
- return ret
-
- def load_state_dict(self, data: OrderedDict, strict: bool = False):
- self._q_cmd.put("load_state_dict")
- self._q_cmd.put(data)
- missing = self._q_cmd_out.get()
- if strict:
- if len(missing) > 0:
- raise RuntimeError("Missing dataset state: {}".format(missing))
- return missing
-
- def get(self) -> CPM9GBatch:
- ret: CPM9GBatch = self._q_data.get(timeout=300) # type: ignore
- if not isinstance(ret, dict):
- raise RuntimeError("Invalid data {}".format(ret))
- return ret
-
- def __iter__(self):
- while True:
- yield self.get()
-
- def __del__(self):
- if not self.closed:
- try:
- self.close()
- except Exception:
- pass
diff --git a/9G-Train/cpm/cpm9g/vocabs/cpm9g.txt b/9G-Train/cpm/cpm9g/vocabs/cpm9g.txt
deleted file mode 100644
index 24fc772..0000000
--- a/9G-Train/cpm/cpm9g/vocabs/cpm9g.txt
+++ /dev/null
@@ -1,119696 +0,0 @@
-""
-""
-""
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-" Enchant"
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-" Encrypt"
-" Encryption"
-" Encyc"
-" Encyclop"
-" Encyclopedia"
-" End"
-" Ende"
-" Ended"
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-" Gym"
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-" HAM"
-" HAN"
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-" HANDLE"
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-" HAVE"
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-" HBO"
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-" HDF"
-" HDInsight"
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-" HEAD"
-" HEADER"
-" HEAP"
-" HEIGHT"
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-" HELL"
-" HELP"
-" HER"
-" HERE"
-" HERO"
-" HEX"
-" HF"
-" HG"
-" HH"
-" HHS"
-" HI"
-" HID"
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-" Mir"
-" Mira"
-" Miracle"
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-" Miranda"
-" Mirror"
-" Mis"
-" Misc"
-" Miscellaneous"
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-" Missouri"
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-" Miz"
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-" Mockito"
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-" Modal"
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-" ModelAndView"
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-" Modified"
-" Modifier"
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-" Mohammad"
-" Mohammed"
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-" Moj"
-" Mol"
-" Mold"
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-" Molecular"
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-" Moment"
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-" Mon"
-" Monaco"
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-" Monday"
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-" Monetary"
-" Money"
-" Mong"
-" Mongo"
-" MongoDB"
-" Mongol"
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-" Monica"
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-" Monitoring"
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-" MonoBehaviour"
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-" Monsanto"
-" Monsieur"
-" Monster"
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-" Monstrous"
-" Mont"
-" Montana"
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-" Montgomery"
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-" Monthly"
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-" Moreno"
-" Moreover"
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-" Mortal"
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-" Mother"
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-" Nike"
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-" Optical"
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-" Organic"
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-" Origin"
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-" Psal"
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-" Psychology"
-" Pt"
-" Ptr"
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-" Pulitzer"
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-" Related"
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-" Repeat"
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-" Replace"
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-" Rescue"
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-" Retail"
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-" Reve"
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-" Rub"
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-" Rwanda"
-" Rx"
-" Ry"
-" Ryan"
-" Ryder"
-" Rye"
-" Ryu"
-" Ryzen"
-" S"
-" SA"
-" SAF"
-" SAL"
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-" SUMMARY"
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-" Sauce"
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-" Sawyer"
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-" Scrib"
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-" Se"
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-" Sek"
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-" Sequ"
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-" Seraph"
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-" Serve"
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-" Session"
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-" Set"
-" SetFontSize"
-" SetTextColor"
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-" Seventh"
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-" Several"
-" Severity"
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-" Sew"
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-" Sexual"
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-" Seymour"
-" Sgt"
-" Sh"
-" Sha"
-" Shack"
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-" Shah"
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-" Shakespeare"
-" Shal"
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-" Sham"
-" Shaman"
-" Shame"
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-" Shanahan"
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-" Shanghai"
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-" Shannon"
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-" Shape"
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-" Shapiro"
-" Shar"
-" Shard"
-" Shards"
-" Share"
-" SharePoint"
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-" SharedPreferences"
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-" Sharia"
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-" Sharing"
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-" She"
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-" Shelby"
-" Sheldon"
-" Shell"
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-" Shelter"
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-" Shepherd"
-" Sher"
-" Sheridan"
-" Sheriff"
-" Sherlock"
-" Sherman"
-" Shi"
-" Shia"
-" Shib"
-" Shield"
-" Shields"
-" Shift"
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-" Shim"
-" Shin"
-" Shine"
-" Shining"
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-" Shinra"
-" Shiny"
-" Shinzo"
-" Ship"
-" Shipping"
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-" Shir"
-" Shirley"
-" Shirt"
-" Shit"
-" Shiv"
-" Shiva"
-" Sho"
-" Shoals"
-" Shock"
-" Shoes"
-" Shogun"
-" Shoot"
-" Shooter"
-" Shooting"
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-" Shopping"
-" Shore"
-" Short"
-" Shortly"
-" Shot"
-" Shotgun"
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-" Should"
-" Show"
-" Showdown"
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-" Showtime"
-" Shrine"
-" Shroud"
-" Shu"
-" Shut"
-" Shutdown"
-" Shutterstock"
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-" Si"
-" Sib"
-" Siber"
-" Siberia"
-" Siberian"
-" Sic"
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-" Sicherheits"
-" Sicily"
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-" Sid"
-" Sidd"
-" Side"
-" Sidebar"
-" Sidney"
-" Sie"
-" Sied"
-" Sieg"
-" Siege"
-" Siehe"
-" Siem"
-" Sierra"
-" Sig"
-" Sight"
-" Sigma"
-" Sign"
-" Signal"
-" Signature"
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-" Significant"
-" Signs"
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-" Sikh"
-" Sil"
-" Silence"
-" Silent"
-" Silicon"
-" Silk"
-" Silva"
-" Silver"
-" Sim"
-" SimFlow"
-" Similar"
-" Similarly"
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-" Simon"
-" Simone"
-" Simp"
-" Simpl"
-" Simple"
-" SimpleDateFormat"
-" Simply"
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-" Simpsons"
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-" Simulation"
-" Simulator"
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-" Since"
-" Sinclair"
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-" Sing"
-" Singap"
-" Singapore"
-" Singer"
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-" Singleton"
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-" Sint"
-" Sioux"
-" Sir"
-" Siren"
-" Siri"
-" Sirius"
-" Sister"
-" Sisters"
-" Sit"
-" Site"
-" Sites"
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-" Sitting"
-" Situ"
-" Situation"
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-" Six"
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-" Sixth"
-" Size"
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-" SizedBox"
-" Sk"
-" Ske"
-" Sketch"
-" Ski"
-" Skies"
-" Skill"
-" Skills"
-" Skin"
-" Skinner"
-" Skip"
-" Skull"
-" Sky"
-" Skydragon"
-" Skype"
-" Skyrim"
-" Skywalker"
-" Sl"
-" Slack"
-" Slam"
-" Slash"
-" Slate"
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-" Slaughter"
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-" Slayer"
-" Sle"
-" Sleep"
-" Sleeping"
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-" Slightly"
-" Slim"
-" Slime"
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-" Slo"
-" Sloan"
-" Slot"
-" Slov"
-" Slovakia"
-" Sloven"
-" Slovenia"
-" Slow"
-" Slowly"
-" Slug"
-" Sly"
-" Sm"
-" Small"
-" Smart"
-" Smartstocks"
-" Smash"
-" Smile"
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-" Smithsonian"
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-" Smoking"
-" Smooth"
-" Smy"
-" Sn"
-" Snake"
-" Snap"
-" Snapchat"
-" Snapdragon"
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-" Snapshot"
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-" Sneak"
-" Sniper"
-" Sno"
-" Snow"
-" Snowden"
-" Snyder"
-" So"
-" Sob"
-" Soc"
-" Soccer"
-" Sochi"
-" Soci"
-" Social"
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-" Society"
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-" Socrates"
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-" Soft"
-" Software"
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-" Soldier"
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-" Solid"
-" SolidGoldMagikarp"
-" Solitaire"
-" Solo"
-" Solomon"
-" Solr"
-" Solution"
-" Solutions"
-" Solve"
-" Solver"
-" Som"
-" Somali"
-" Somalia"
-" Some"
-" Somebody"
-" Somehow"
-" Someone"
-" Somers"
-" Somerset"
-" Something"
-" Sometimes"
-" Somew"
-" Somewhere"
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-" Son"
-" Song"
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-" Soon"
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-" Sophia"
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-" Sor"
-" Sora"
-" Sorce"
-" Sorcerer"
-" Sorceress"
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-" Sorry"
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-" Sorted"
-" Sou"
-" Soul"
-" Souls"
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-" South"
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-" Southwest"
-" Sov"
-" Sovere"
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-" Sox"
-" Soy"
-" Sozial"
-" Sp"
-" Spa"
-" Spac"
-" Space"
-" SpaceX"
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-" Span"
-" Spani"
-" Spanish"
-" Spark"
-" Sparkle"
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-" Sparrow"
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-" Spart"
-" Spartan"
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-" Spatial"
-" Spawn"
-" Spe"
-" Speak"
-" Speaker"
-" Speaking"
-" Spear"
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-" Spec"
-" Special"
-" Specialist"
-" Species"
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-" Specifically"
-" Specification"
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-" Specify"
-" Spect"
-" Spectre"
-" Spectrum"
-" Speech"
-" Speed"
-" Speedway"
-" Spell"
-" Spells"
-" Spencer"
-" Spend"
-" Spending"
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-" Sphere"
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-" Spicer"
-" Spider"
-" Spiegel"
-" Spiel"
-" Spielberg"
-" Spieler"
-" Spike"
-" Spin"
-" Spir"
-" Spiral"
-" Spirit"
-" Spirits"
-" Spiritual"
-" Spit"
-" Spl"
-" Splash"
-" Split"
-" Spo"
-" Spock"
-" Spoiler"
-" Spokane"
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-" Spons"
-" Sponsor"
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-" Sport"
-" Sporting"
-" Sports"
-" Spot"
-" Spotify"
-" Spotlight"
-" Spr"
-" Sprache"
-" Spray"
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-" Springer"
-" Springfield"
-" Springs"
-" Sprint"
-" Sprite"
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-" Spy"
-" Sql"
-" Squ"
-" Squad"
-" Squadron"
-" Square"
-" Squid"
-" Squirrel"
-" Sr"
-" Src"
-" Sri"
-" Ssh"
-" St"
-" StObject"
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-" Staat"
-" Staaten"
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-" Stamina"
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-" Standing"
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-" Stanton"
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-" StarCraft"
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-" Startup"
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-" StatelessWidget"
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-" Station"
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-" Statistics"
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-" StatusCode"
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-" Step"
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-" Stephanie"
-" Stephen"
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-" Stephenson"
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-" Ster"
-" Sterling"
-" Stern"
-" Stev"
-" Steve"
-" Steven"
-" Stevens"
-" Stevenson"
-" Stew"
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-" Still"
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-" Sto"
-" Stock"
-" Stockholm"
-" Stoke"
-" Stone"
-" Stones"
-" Stop"
-" Storage"
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-" Story"
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-" Str"
-" Stra"
-" Straight"
-" Strait"
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-" Stranger"
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-" Strat"
-" Strateg"
-" Strategic"
-" Strategies"
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-" Straw"
-" Strawberry"
-" Stre"
-" Stream"
-" Streaming"
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-" Streets"
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-" Stri"
-" Strict"
-" Strike"
-" Strikes"
-" String"
-" StringBuffer"
-" StringBuilder"
-" StringComparison"
-" StringConcatenation"
-" StringIO"
-" StringUtils"
-" Strings"
-" Strip"
-" Stripe"
-" Stro"
-" Strong"
-" Strongh"
-" Stronghold"
-" Stru"
-" Struct"
-" Structural"
-" Structure"
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-" Stuart"
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-" Studios"
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-" Sturgeon"
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-" Stuttgart"
-" Sty"
-" Style"
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-" Styled"
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-" Su"
-" Suarez"
-" Sub"
-" SubL"
-" SubLObject"
-" Subaru"
-" Subcommittee"
-" Subject"
-" Subjects"
-" Submission"
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-" Subscribe"
-" Subscriber"
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-" Subst"
-" Substance"
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-" Subtle"
-" Subtract"
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-" Suc"
-" Success"
-" Successful"
-" Successfully"
-" Such"
-" Sud"
-" Sudan"
-" Suddenly"
-" Sue"
-" Suff"
-" Suffolk"
-" Sug"
-" Sugar"
-" Suggest"
-" Suicide"
-" Suisse"
-" Suit"
-" Suite"
-" Suk"
-" Sul"
-" Sullivan"
-" Sultan"
-" Sum"
-" Summary"
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-" Summit"
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-" Summoner"
-" Sun"
-" Sund"
-" Sunday"
-" Sundays"
-" Sunder"
-" Sunderland"
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-" Sunni"
-" Sunny"
-" Sunrise"
-" Suns"
-" Sunset"
-" Sunshine"
-" Sup"
-" Super"
-" Superintendent"
-" Superior"
-" Superman"
-" Supervisor"
-" Supp"
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-" Supplemental"
-" Supplementary"
-" Supplier"
-" Supply"
-" Support"
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-" Supporters"
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-" Suppose"
-" Supreme"
-" Sur"
-" Sure"
-" Surely"
-" Surf"
-" Surface"
-" Surge"
-" Surgery"
-" Surprise"
-" Surprisingly"
-" Surrey"
-" Surv"
-" Surve"
-" Surveillance"
-" Survey"
-" Surviv"
-" Survival"
-" Survive"
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-" Sus"
-" Susan"
-" Susp"
-" Sussex"
-" Sustainable"
-" Sut"
-" Sutherland"
-" Sutton"
-" Suz"
-" Suzanne"
-" Suzuki"
-" Sv"
-" Sven"
-" Svens"
-" Sver"
-" Sverige"
-" Svg"
-" Sw"
-" Swagger"
-" Swamp"
-" Swan"
-" Swansea"
-" Swanson"
-" Swap"
-" Swarm"
-" Swe"
-" Swed"
-" Sweden"
-" Swedish"
-" Sweeney"
-" Sweep"
-" Sweet"
-" Swift"
-" Swim"
-" Swing"
-" Swiss"
-" Switch"
-" Switzerland"
-" Sword"
-" Swords"
-" Swordsman"
-" Sy"
-" Syd"
-" Sydney"
-" Syl"
-" Sylv"
-" Sylvia"
-" Sym"
-" Symb"
-" Symbol"
-" Symfony"
-" Symphony"
-" Symptoms"
-" Syn"
-" Sync"
-" Synchron"
-" Synd"
-" Syndicate"
-" Syndrome"
-" Synt"
-" Syntax"
-" Syracuse"
-" Syri"
-" Syria"
-" Syrian"
-" Syrians"
-" Sys"
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-" TA"
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-" TABLE"
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-" TODAY"
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-" Therapy"
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-" Thirty"
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-" Welcome"
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-" Welfare"
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-" Wells"
-" Welsh"
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-" Außerdem"
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-" Bevölker"
-" Bevölkerung"
-" Bibliografía"
-" Bibliothèque"
-" Bé"
-" Bü"
-" Bür"
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-" Comité"
-" Congrès"
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-" Dí"
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-" León"
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-" Lé"
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-" Lö"
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-" María"
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-" Mé"
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-" Nä"
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-" Perú"
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-" Première"
-" Präsident"
-" Prés"
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-" René"
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-" até"
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-" déf"
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-" dél"
-" dém"
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-" européenne"
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-" même"
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-" númer"
-" número"
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-" også"
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-" política"
-" político"
-" poč"
-" poł"
-" położ"
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-" producción"
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-" pré"
-" préc"
-" précéd"
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-" présence"
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-" présente"
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-" pró"
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-" publié"
-" può"
-" på"
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-" północ"
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-" później"
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-" público"
-" pł"
-" př"
-" před"
-" přek"
-" při"
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-" región"
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-" représent"
-" rivière"
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-" rég"
-" région"
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-" répond"
-" rés"
-" réseau"
-" rész"
-" río"
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-" również"
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-" rôle"
-" röm"
-" rör"
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-" schließlich"
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-" según"
-" señ"
-" situé"
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-" siècle"
-" siège"
-" się"
-" sjö"
-" skład"
-" slä"
-" släktet"
-" società"
-" société"
-" společ"
-" später"
-" spé"
-" spécial"
-" století"
-" stör"
-" succès"
-" svě"
-" système"
-" szám"
-" száz"
-" szó"
-" são"
-" så"
-" sé"
-" série"
-" sí"
-" só"
-" són"
-" süd"
-" să"
-" są"
-" také"
-" także"
-" talál"
-" también"
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-" tenía"
-" też"
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-" totalité"
-" través"
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-" très"
-" três"
-" två"
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-" tätig"
-" té"
-" téc"
-" télé"
-" télévis"
-" tém"
-" tématu"
-" tér"
-" términ"
-" tête"
-" tít"
-" título"
-" tö"
-" több"
-" tör"
-" ursprüng"
-" utilisé"
-" után"
-" versión"
-" veröff"
-" veröffentlicht"
-" világ"
-" vá"
-" vál"
-" válto"
-" város"
-" vä"
-" vär"
-" væ"
-" vé"
-" vég"
-" vérit"
-" ví"
-" vý"
-" vě"
-" vš"
-" však"
-" wieś"
-" wię"
-" województ"
-" wrześ"
-" września"
-" współ"
-" während"
-" wür"
-" wł"
-" zač"
-" zewnętrz"
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-" został"
-" została"
-" zunächst"
-" zurück"
-" związ"
-" zá"
-" zák"
-" ¡"
-" £"
-" §"
-" «"
-" °"
-" ·"
-" »"
-" ¿"
-" À"
-" Á"
-" Ál"
-" Áng"
-" Ä"
-" Å"
-" Års"
-" Årsmed"
-" È"
-" É"
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-" Éditions"
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-" Ét"
-" États"
-" Í"
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-" ×"
-" Ú"
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-" à"
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-" új"
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-" último"
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-" Č"
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-" Š"
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-" že"
-" și"
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-" Є"
-" І"
-" Ін"
-" Історія"
-" Ј"
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-" Ад"
-" Ал"
-" Алек"
-" Александ"
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-" Б"
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-" Вла"
-" Влади"
-" Владимир"
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-" Вол"
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-" Кри"
-" Ку"
-" Кур"
-" Л"
-" Ла"
-" Ле"
-" Ли"
-" Литература"
-" Ло"
-" Лон"
-" Лу"
-" Ль"
-" Льв"
-" Лю"
-" Лі"
-" М"
-" Ма"
-" Май"
-" Мак"
-" Мар"
-" Ме"
-" Между"
-" Мекси"
-" Мексика"
-" Мексику"
-" Мексичка"
-" Ми"
-" Миха"
-" Михай"
-" Мо"
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-" Мор"
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-" Моск"
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-" Му"
-" Мі"
-" Н"
-" На"
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-" Население"
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-" Насеље"
-" Нау"
-" Националь"
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-" Ни"
-" Никола"
-" Николай"
-" Но"
-" Нов"
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-" Нор"
-" Нью"
-" Ні"
-" О"
-" Об"
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-" Одна"
-" Однако"
-" Он"
-" Ор"
-" Ос"
-" Основ"
-" От"
-" Оте"
-" Отече"
-" Официаль"
-" П"
-" Па"
-" Пав"
-" Пар"
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-" Пе"
-" Пер"
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-" Пет"
-" Пи"
-" По"
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-" Пор"
-" Посилання"
-" После"
-" Пра"
-" Пре"
-" Пред"
-" През"
-" Према"
-" При"
-" Про"
-" Пу"
-" Пів"
-" Під"
-" Після"
-" Р"
-" Ра"
-" Раз"
-" Рас"
-" Распо"
-" Расподела"
-" Ре"
-" Резу"
-" Резултати"
-" Рес"
-" Республи"
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-" Ри"
-" Ро"
-" Роб"
-" Роди"
-" Роз"
-" Рос"
-" Росси"
-" России"
-" Российской"
-" Ру"
-" Рус"
-" С"
-" СП"
-" СССР"
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-" Са"
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-" Савезне"
-" Сан"
-" Санкт"
-" Свя"
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-" Север"
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-" Си"
-" См"
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-" Совет"
-" Список"
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-" Спољашње"
-" Ср"
-" Сред"
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-" Ст"
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-" Становништво"
-" Сте"
-" Су"
-" Т"
-" Та"
-" Так"
-" Также"
-" Те"
-" Тер"
-" Ти"
-" То"
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-" Три"
-" Ту"
-" Тур"
-" У"
-" Уи"
-" Укра"
-" Украи"
-" Украины"
-" Украї"
-" України"
-" Ф"
-" Фа"
-" Фе"
-" Федера"
-" Федерации"
-" Фи"
-" Фо"
-" Фран"
-" Х"
-" Ха"
-" Хар"
-" Хо"
-" Хро"
-" Хронологи"
-" Хронологија"
-" Ху"
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-" Це"
-" Цент"
-" Ч"
-" Ча"
-" Че"
-" Чем"
-" Чер"
-" Чи"
-" Ш"
-" Ша"
-" Ши"
-" Щ"
-" Э"
-" Это"
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-" Я"
-" а"
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-" все"
-" всего"
-" всех"
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-" всі"
-" вто"
-" второй"
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-" віці"
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-" га"
-" гг"
-" где"
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-" генерал"
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-" гла"
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-" го"
-" гово"
-" год"
-" года"
-" годах"
-" годи"
-" година"
-" године"
-" годов"
-" году"
-" годы"
-" голо"
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-" гор"
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-" город"
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-" городе"
-" государ"
-" гра"
-" град"
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-" губер"
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-" да"
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-"交际"
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-"京沪"
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-"亲切"
-"亲历"
-"亲友"
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-"亲和"
-"亲和力"
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-"停电"
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-"允"
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-"博爱"
-"博物"
-"博物院"
-"博物馆"
-"博览"
-"博览会"
-"卜"
-"卞"
-"卟"
-"占"
-"占卜"
-"占地"
-"占地面积"
-"占据"
-"占有"
-"占有率"
-"占用"
-"占领"
-"卡"
-"卡尔"
-"卡拉"
-"卡斯"
-"卡片"
-"卡牌"
-"卡特"
-"卡组"
-"卡罗"
-"卡路里"
-"卡车"
-"卡通"
-"卡顿"
-"卢"
-"卢卡"
-"卣"
-"卤"
-"卦"
-"卧"
-"卧室"
-"卧床"
-"卧槽"
-"卧铺"
-"卧龙"
-"卫"
-"卫健"
-"卫国"
-"卫士"
-"卫星"
-"卫浴"
-"卫生"
-"卫生局"
-"卫生组织"
-"卫生部"
-"卫生间"
-"卫生院"
-"卫衣"
-"卫视"
-"卮"
-"卯"
-"印"
-"印制"
-"印刷"
-"印发"
-"印尼"
-"印度"
-"印度人"
-"印度尼西亚"
-"印章"
-"印第安"
-"印花"
-"印记"
-"印证"
-"印象"
-"危"
-"危及"
-"危害"
-"危急"
-"危机"
-"危重"
-"危险"
-"危险性"
-"即"
-"即位"
-"即使"
-"即便"
-"即兴"
-"即刻"
-"即可"
-"即将"
-"即日起"
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-"却是"
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-"卵"
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-"卷"
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-"卻"
-"卿"
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-"厂商"
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-"厄"
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-"厅长"
-"历"
-"历代"
-"历任"
-"历史"
-"历史学"
-"历史性"
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-"历年"
-"历时"
-"历来"
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-"历经"
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-"厉"
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-"压"
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-"厌恶"
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-"厍"
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-"厘"
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-"厚"
-"厚厚"
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-"厚重"
-"厚非"
-"厝"
-"原"
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-"发病"
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-"发音"
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-"发黑"
-"叔"
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-"受欢迎"
-"受灾"
-"受理"
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-"受精"
-"受聘"
-"受苦"
-"受访"
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-"受贿"
-"受过"
-"受邀"
-"受阻"
-"受限"
-"受骗"
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-"变动"
-"变化"
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-"变小"
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-"变异"
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-"变得"
-"变态"
-"变性"
-"变成"
-"变换"
-"变更"
-"变焦"
-"变现"
-"变电"
-"变白"
-"变相"
-"变美"
-"变色"
-"变质"
-"变迁"
-"变速"
-"变速器"
-"变速箱"
-"变量"
-"变革"
-"变频"
-"变黑"
-"叙"
-"叙事"
-"叙利亚"
-"叙述"
-"叛"
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-"叛逆"
-"叟"
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-"口号"
-"口吃"
-"口吻"
-"口味"
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-"口岸"
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-"口水"
-"口渴"
-"口碑"
-"口红"
-"口罩"
-"口腔"
-"口腔溃疡"
-"口臭"
-"口舌"
-"口袋"
-"口诀"
-"口译"
-"口语"
-"古"
-"古人"
-"古今"
-"古代"
-"古兰"
-"古典"
-"古城"
-"古墓"
-"古巴"
-"古希腊"
-"古建筑"
-"古怪"
-"古文"
-"古时"
-"古朴"
-"古树"
-"古玩"
-"古琴"
-"古筝"
-"古籍"
-"古老"
-"古董"
-"古装"
-"古诗"
-"古迹"
-"古道"
-"古镇"
-"古风"
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-"句"
-"句型"
-"句子"
-"句话"
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-"另一只"
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-"召回"
-"召开"
-"召集"
-"叭"
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-"可不可"
-"可不可以"
-"可不是"
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-"可想而知"
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-"可靠性"
-"台"
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-"台北市"
-"台南"
-"台山"
-"台州"
-"台式机"
-"台湾"
-"台湾省"
-"台灣"
-"台灯"
-"台独"
-"台球"
-"台词"
-"台阶"
-"台面"
-"台风"
-"叱"
-"史"
-"史上"
-"史书"
-"史以"
-"史学"
-"史密"
-"史料"
-"史蒂"
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-"史诗"
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-"叵"
-"叶"
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-"各区"
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-"吆"
-"合"
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-"吉"
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-"吊"
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-"吊销"
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-"吋"
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-"同道"
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-"名词"
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-"名额"
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-"吏"
-"吐"
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-"吓"
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-"吕"
-"吕布"
-"吖"
-"吗"
-"君"
-"君主"
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-"吝"
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-"吞"
-"吞吐"
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-"吟"
-"吠"
-"吡"
-"吣"
-"否"
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-"含有"
-"含水"
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-"含糖"
-"含蓄"
-"含量"
-"含金"
-"含金量"
-"听"
-"听不懂"
-"听从"
-"听众"
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-"听力"
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-"听听"
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-"听歌"
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-"听话"
-"听说"
-"听课"
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-"吮"
-"启"
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-"启蒙"
-"启超"
-"启迪"
-"吱"
-"吲"
-"吴"
-"吴亦凡"
-"吴国"
-"吴江"
-"吴越"
-"吵"
-"吵吵"
-"吵架"
-"吵闹"
-"吸"
-"吸入"
-"吸取"
-"吸尘"
-"吸引"
-"吸引力"
-"吸收"
-"吸毒"
-"吸气"
-"吸水"
-"吸烟"
-"吸纳"
-"吸脂"
-"吸血"
-"吸血鬼"
-"吸附"
-"吸食"
-"吹"
-"吹牛"
-"吹风"
-"吹风机"
-"吻"
-"吻合"
-"吼"
-"吽"
-"吾"
-"呀"
-"呃"
-"呆"
-"呆呆"
-"呈"
-"呈现"
-"呈现出"
-"告"
-"告别"
-"告白"
-"告知"
-"告终"
-"告诉"
-"告诫"
-"呋"
-"呎"
-"呐"
-"呐喊"
-"呒"
-"呓"
-"呔"
-"呕"
-"呕吐"
-"呖"
-"呗"
-"员"
-"员外"
-"员工"
-"呛"
-"呜"
-"呜呜"
-"呢"
-"呣"
-"呤"
-"呦"
-"周"
-"周公"
-"周刊"
-"周到"
-"周围"
-"周密"
-"周岁"
-"周年"
-"周恩"
-"周恩来"
-"周旋"
-"周日"
-"周易"
-"周期"
-"周期性"
-"周末"
-"周炎"
-"周瑜"
-"周知"
-"周礼"
-"周线"
-"周身"
-"周转"
-"周边"
-"周遭"
-"呱"
-"呲"
-"味"
-"味儿"
-"味甘"
-"味精"
-"味蕾"
-"味觉"
-"味道"
-"呵"
-"呵呵"
-"呵护"
-"呶"
-"呷"
-"呸"
-"呻"
-"呻吟"
-"呼"
-"呼叫"
-"呼吁"
-"呼吸"
-"呼吸困难"
-"呼吸系统"
-"呼吸道"
-"呼呼"
-"呼和浩特"
-"呼唤"
-"呼啸"
-"呼喊"
-"呼噜"
-"呼声"
-"呼应"
-"呼气"
-"命"
-"命中"
-"命中率"
-"命令"
-"命名"
-"命局"
-"命理"
-"命运"
-"命门"
-"命题"
-"咀"
-"咀嚼"
-"咂"
-"咄"
-"咆"
-"咆哮"
-"咋"
-"和"
-"和事"
-"和尚"
-"和平"
-"和弦"
-"和田"
-"和田玉"
-"和睦"
-"和蔼"
-"和解"
-"和谐"
-"和谐社"
-"和谐社会"
-"咎"
-"咏"
-"咐"
-"咒"
-"咒语"
-"咔"
-"咕"
-"咕噜"
-"咖"
-"咖啡"
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-"咖啡馆"
-"咖喱"
-"咙"
-"咚"
-"咛"
-"咝"
-"咣"
-"咤"
-"咦"
-"咧"
-"咧咧"
-"咨"
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-"咨询服务"
-"咨询电话"
-"咩"
-"咪"
-"咫"
-"咫尺"
-"咬"
-"咬牙"
-"咯"
-"咱"
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-"咲"
-"咳"
-"咳嗽"
-"咳崩"
-"咴"
-"咸"
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-"咸鱼"
-"咻"
-"咽"
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-"咽炎"
-"咿"
-"哀"
-"哀乐"
-"品"
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-"品种"
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-"品质"
-"哂"
-"哄"
-"哆"
-"哇"
-"哈"
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-"哈佛大学"
-"哈利"
-"哈哈"
-"哈哈哈"
-"哈哈大笑"
-"哈密"
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-"哈尔滨"
-"哈尔滨市"
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-"哈里"
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-"哌"
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-"哎"
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-"哏"
-"哐"
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-"哒"
-"哓"
-"哔"
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-"哕"
-"哗"
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-"哗啦"
-"哙"
-"哚"
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-"哟"
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-"哨"
-"哩"
-"哪"
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-"哪些"
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-"哪有"
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-"哭"
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-"哭泣"
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-"哮"
-"哮喘"
-"哲"
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-"哺"
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-"哼哼"
-"哽"
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-"唑"
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-"唯"
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-"唯个"
-"唯心主义"
-"唯有"
-"唯物"
-"唯物主义"
-"唯独"
-"唯美"
-"唰"
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-"唳"
-"唵"
-"唷"
-"唼"
-"唾"
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-"唿"
-"啁"
-"啃"
-"啄"
-"商"
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-"商业银行"
-"商人"
-"商会"
-"商务"
-"商务印书"
-"商务部"
-"商品"
-"商品房"
-"商圈"
-"商场"
-"商城"
-"商学院"
-"商家"
-"商店"
-"商户"
-"商报"
-"商机"
-"商标"
-"商用"
-"商界"
-"商行"
-"商贸"
-"商量"
-"商银行"
-"商铺"
-"啉"
-"啊"
-"啊啊"
-"問"
-"問題"
-"啐"
-"啕"
-"啖"
-"啜"
-"啡"
-"啤"
-"啤酒"
-"啥"
-"啦"
-"啧"
-"啧啧"
-"啪"
-"啬"
-"啭"
-"啮"
-"啰"
-"啰嗦"
-"啵"
-"啶"
-"啷"
-"啸"
-"啻"
-"啼"
-"啾"
-"喀"
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-"喁"
-"喂"
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-"喂奶"
-"喂食"
-"喃"
-"善"
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-"善待"
-"善恶"
-"善意"
-"善根"
-"善用"
-"善良"
-"喆"
-"喇"
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-"喈"
-"喉"
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-"喊"
-"喋"
-"喏"
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-"喘"
-"喙"
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-"喜事"
-"喜剧"
-"喜好"
-"喜庆"
-"喜怒"
-"喜怒哀乐"
-"喜悦"
-"喜欢"
-"喜歡"
-"喜爱"
-"喜马拉雅"
-"喝"
-"喝咖啡"
-"喝多"
-"喝完"
-"喝彩"
-"喝水"
-"喝汤"
-"喝点"
-"喝茶"
-"喝酒"
-"喝醉"
-"喟"
-"喧"
-"喧嚣"
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-"單"
-"喱"
-"喳"
-"喵"
-"喷"
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-"喷嚏"
-"喷射"
-"喷气"
-"喷水"
-"喷泉"
-"喷洒"
-"喷涂"
-"喷雾"
-"喹"
-"喻"
-"喽"
-"喾"
-"嗄"
-"嗅"
-"嗅觉"
-"嗉"
-"嗌"
-"嗍"
-"嗎"
-"嗐"
-"嗑"
-"嗒"
-"嗓"
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-"嗓音"
-"嗔"
-"嗖"
-"嗜"
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-"嗝"
-"嗞"
-"嗟"
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-"嗤"
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-"嗦"
-"嗨"
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-"嘁"
-"嘈"
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-"嘉"
-"嘉兴"
-"嘉奖"
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-"嘉年华"
-"嘉庆"
-"嘉靖"
-"嘌"
-"嘎"
-"嘏"
-"嘘"
-"嘚"
-"嘛"
-"嘞"
-"嘟"
-"嘟嘟"
-"嘡"
-"嘣"
-"嘤"
-"嘧"
-"嘬"
-"嘭"
-"嘱"
-"嘱咐"
-"嘲"
-"嘲笑"
-"嘲讽"
-"嘴"
-"嘴上"
-"嘴唇"
-"嘴巴"
-"嘴炮"
-"嘴角"
-"嘴边"
-"嘴里"
-"嘶"
-"嘹"
-"嘻"
-"嘻嘻"
-"嘿"
-"嘿嘿"
-"噌"
-"噍"
-"噎"
-"噔"
-"噗"
-"噘"
-"噙"
-"噜"
-"噢"
-"噤"
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-"器件"
-"器具"
-"器官"
-"器材"
-"器械"
-"器物"
-"器皿"
-"噩"
-"噩噩"
-"噩梦"
-"噪"
-"噪声"
-"噪音"
-"噫"
-"噬"
-"噱"
-"噶"
-"噻"
-"噼"
-"嚄"
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-"嚼"
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-"囊肿"
-"囔"
-"囗"
-"囚"
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-"四射"
-"四川"
-"四川省"
-"四库"
-"四方"
-"四时"
-"四海"
-"四肢"
-"四通"
-"四驱"
-"回"
-"回乡"
-"回事"
-"回到"
-"回升"
-"回去"
-"回合"
-"回味"
-"回国"
-"回复"
-"回头"
-"回家"
-"回应"
-"回归"
-"回忆"
-"回忆起"
-"回想"
-"回报"
-"回报率"
-"回撤"
-"回收"
-"回旋"
-"回族"
-"回暖"
-"回来"
-"回民"
-"回流"
-"回眸"
-"回答"
-"回老"
-"回老家"
-"回落"
-"回访"
-"回调"
-"回购"
-"回路"
-"回转"
-"回过"
-"回避"
-"回顾"
-"回馈"
-"回首"
-"囟"
-"因"
-"因为"
-"因人"
-"因人而异"
-"因其"
-"因地"
-"因地制宜"
-"因子"
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-"因為"
-"因特网"
-"因素"
-"因缘"
-"因而"
-"囡"
-"团"
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-"团团"
-"团圆"
-"团委"
-"团战"
-"团支"
-"团结"
-"团聚"
-"团购"
-"团长"
-"团队"
-"囤"
-"囧"
-"囫"
-"园"
-"园内"
-"园区"
-"园林"
-"园艺"
-"困"
-"困境"
-"困惑"
-"困扰"
-"困难"
-"囱"
-"围"
-"围城"
-"围墙"
-"围巾"
-"围攻"
-"围棋"
-"围着"
-"围绕"
-"围观"
-"囵"
-"囹"
-"固"
-"固体"
-"固化"
-"固定"
-"固定资产"
-"固态"
-"固执"
-"固有"
-"固然"
-"固醇"
-"国"
-"国产"
-"国人"
-"国企"
-"国会"
-"国债"
-"国共"
-"国内"
-"国内外"
-"国军"
-"国力"
-"国务"
-"国务院"
-"国土"
-"国土资源"
-"国外"
-"国学"
-"国安"
-"国宝"
-"国家"
-"国家专利"
-"国家有关"
-"国家机关"
-"国家标准"
-"国家税务"
-"国家税务总局"
-"国家级"
-"国家统计局"
-"国家自然科学基金"
-"国家队"
-"国庆"
-"国庆节"
-"国库"
-"国度"
-"国情"
-"国旗"
-"国有"
-"国有企业"
-"国有资产"
-"国服"
-"国标"
-"国歌"
-"国民"
-"国民党"
-"国民政府"
-"国民经济"
-"国民革命军"
-"国泰"
-"国王"
-"国画"
-"国税"
-"国立"
-"国籍"
-"国美"
-"国荣"
-"国药"
-"国营"
-"国藩"
-"国语"
-"国货"
-"国贸"
-"国资"
-"国资委"
-"国足"
-"国道"
-"国门"
-"国防"
-"国防部"
-"国际"
-"国际交流"
-"国际化"
-"国际标准"
-"国际贸易"
-"国际金融"
-"图"
-"图上"
-"图中"
-"图为"
-"图书"
-"图书馆"
-"图像"
-"图册"
-"图层"
-"图形"
-"图文"
-"图标"
-"图案"
-"图片"
-"图画"
-"图纸"
-"图腾"
-"图表"
-"图解"
-"囿"
-"圃"
-"圄"
-"圆"
-"圆圆"
-"圆弧"
-"圆形"
-"圆柱"
-"圆桌"
-"圆润"
-"圆满"
-"圆筒"
-"圆锥"
-"圈"
-"圈内"
-"圈子"
-"圈里"
-"圉"
-"圊"
-"國"
-"園"
-"圖"
-"團"
-"圜"
-"土"
-"土司"
-"土地"
-"土地革命"
-"土壤"
-"土家族"
-"土建"
-"土星"
-"土木"
-"土木工程"
-"土耳其"
-"土著"
-"土豆"
-"土豪"
-"圣"
-"圣人"
-"圣地"
-"圣殿"
-"圣母"
-"圣经"
-"圣诞"
-"圣诞节"
-"圣贤"
-"在"
-"在一起"
-"在世"
-"在世界上"
-"在乎"
-"在于"
-"在位"
-"在内"
-"在历史上"
-"在场"
-"在外"
-"在实践"
-"在实践中"
-"在家"
-"在座"
-"在建"
-"在意"
-"在我"
-"在我看来"
-"在所"
-"在手"
-"在校"
-"在校学生"
-"在此"
-"在此之"
-"在此之前"
-"在此期间"
-"在生活"
-"在生活中"
-"在线"
-"在职"
-"在读"
-"圩"
-"圪"
-"圬"
-"圭"
-"圮"
-"圯"
-"地"
-"地上"
-"地下"
-"地下室"
-"地下水"
-"地中海"
-"地为"
-"地主"
-"地久"
-"地亚"
-"地产"
-"地价"
-"地位"
-"地利"
-"地势"
-"地区"
-"地去"
-"地名"
-"地向"
-"地图"
-"地址"
-"地块"
-"地坪"
-"地域"
-"地基"
-"地壳"
-"地处"
-"地委"
-"地层"
-"地带"
-"地形"
-"地想"
-"地摊"
-"地支"
-"地方"
-"地方税"
-"地板"
-"地标"
-"地步"
-"地段"
-"地毯"
-"地点"
-"地狱"
-"地球"
-"地理"
-"地理位置"
-"地理信息"
-"地理学"
-"地瓜"
-"地用"
-"地盘"
-"地砖"
-"地税"
-"地笑"
-"地级"
-"地缘"
-"地藏"
-"地表"
-"地被"
-"地说"
-"地貌"
-"地质"
-"地质学"
-"地道"
-"地里"
-"地铁"
-"地铁站"
-"地问"
-"地震"
-"地面"
-"地黄"
-"圳"
-"圹"
-"场"
-"场上"
-"场内"
-"场合"
-"场地"
-"场均"
-"场外"
-"场所"
-"场景"
-"场面"
-"场馆"
-"圻"
-"圾"
-"址"
-"坂"
-"均"
-"均价"
-"均匀"
-"均线"
-"均衡"
-"坊"
-"坌"
-"坍"
-"坎"
-"坎坷"
-"坏"
-"坏习惯"
-"坏事"
-"坏人"
-"坏死"
-"坐"
-"坐下"
-"坐在"
-"坐姿"
-"坐标"
-"坐落"
-"坐落于"
-"坐车"
-"坑"
-"块"
-"块状"
-"块钱"
-"坚"
-"坚信"
-"坚决"
-"坚固"
-"坚守"
-"坚定"
-"坚定不移"
-"坚实"
-"坚强"
-"坚持"
-"坚持下去"
-"坚持不懈"
-"坚挺"
-"坚果"
-"坚硬"
-"坚韧"
-"坛"
-"坝"
-"坞"
-"坟"
-"坟墓"
-"坠"
-"坠落"
-"坡"
-"坤"
-"坦"
-"坦克"
-"坦然"
-"坦白"
-"坦言"
-"坦诚"
-"坨"
-"坩"
-"坪"
-"坫"
-"坭"
-"坯"
-"坳"
-"坷"
-"坻"
-"坼"
-"垂"
-"垂体"
-"垂直"
-"垂钓"
-"垃"
-"垃圾"
-"垃圾桶"
-"垄"
-"垄断"
-"垅"
-"垆"
-"型"
-"型号"
-"型材"
-"垌"
-"垒"
-"垓"
-"垛"
-"垠"
-"垡"
-"垢"
-"垣"
-"垤"
-"垦"
-"垧"
-"垩"
-"垫"
-"垫底"
-"垭"
-"垮"
-"垱"
-"垸"
-"埂"
-"埃"
-"埃及"
-"埃尔"
-"埃斯"
-"埋"
-"埋头"
-"埋怨"
-"埋没"
-"埋葬"
-"埋藏"
-"城"
-"城东"
-"城中"
-"城乡"
-"城乡居民"
-"城关"
-"城内"
-"城北"
-"城区"
-"城南"
-"城县"
-"城堡"
-"城墙"
-"城市"
-"城市化"
-"城市群"
-"城建"
-"城管"
-"城西"
-"城郊"
-"城里"
-"城镇"
-"城镇化"
-"城门"
-"城际"
-"城隍"
-"埒"
-"埔"
-"埕"
-"埘"
-"埙"
-"埚"
-"埝"
-"域"
-"域名"
-"埠"
-"埤"
-"埭"
-"埯"
-"埴"
-"埵"
-"埸"
-"培"
-"培养"
-"培植"
-"培育"
-"培训"
-"培训中心"
-"培训班"
-"基"
-"基于"
-"基准"
-"基友"
-"基因"
-"基因组"
-"基地"
-"基尼"
-"基层"
-"基建"
-"基数"
-"基本"
-"基本上"
-"基本功"
-"基本知识"
-"基本面"
-"基点"
-"基督"
-"基督徒"
-"基督教"
-"基石"
-"基础"
-"基础教育"
-"基础知识"
-"基础设施"
-"基站"
-"基调"
-"基质"
-"基部"
-"基金"
-"基金会"
-"埽"
-"堀"
-"堂"
-"堂堂"
-"堂皇"
-"堆"
-"堆放"
-"堆积"
-"堇"
-"堉"
-"堋"
-"堍"
-"堑"
-"堕"
-"堕胎"
-"堕落"
-"堙"
-"堞"
-"堡"
-"堡垒"
-"堤"
-"堪"
-"堪比"
-"堪称"
-"堰"
-"報"
-"場"
-"堵"
-"堵住"
-"堵塞"
-"堵车"
-"塄"
-"塆"
-"塌"
-"塍"
-"塑"
-"塑性"
-"塑料"
-"塑料袋"
-"塑胶"
-"塑造"
-"塔"
-"塔尔"
-"塘"
-"塞"
-"塞尔"
-"塞尔维亚"
-"塞罗"
-"塞进"
-"填"
-"填充"
-"填写"
-"填报"
-"填料"
-"填满"
-"填空"
-"填补"
-"填词"
-"塬"
-"塾"
-"墀"
-"墁"
-"境"
-"境内"
-"境地"
-"境外"
-"境界"
-"墅"
-"墉"
-"墒"
-"墓"
-"墓地"
-"墓志"
-"墓葬"
-"墙"
-"墙上"
-"墙体"
-"墙壁"
-"墙角"
-"墙面"
-"增"
-"增产"
-"增值"
-"增值税"
-"增减"
-"增加"
-"增加值"
-"增压"
-"增发"
-"增多"
-"增大"
-"增幅"
-"增强"
-"增持"
-"增收"
-"增效"
-"增殖"
-"增添"
-"增生"
-"增益"
-"增肌"
-"增至"
-"增补"
-"增设"
-"增资"
-"增进"
-"增速"
-"增量"
-"增长"
-"增长率"
-"增长速度"
-"增高"
-"墟"
-"墨"
-"墨尔本"
-"墨水"
-"墨西哥"
-"墨迹"
-"墨镜"
-"墩"
-"墼"
-"壁"
-"壁垒"
-"壁挂"
-"壁画"
-"壁纸"
-"壅"
-"壑"
-"壕"
-"壤"
-"士"
-"士兵"
-"士官"
-"士气"
-"士顿"
-"壬"
-"壮"
-"壮丽"
-"壮大"
-"壮志"
-"壮族"
-"壮烈"
-"壮观"
-"壮阔"
-"壮阳"
-"声"
-"声乐"
-"声名"
-"声响"
-"声器"
-"声声"
-"声学"
-"声明"
-"声望"
-"声波"
-"声称"
-"声色"
-"声誉"
-"声音"
-"売"
-"壳"
-"壶"
-"壹"
-"处"
-"处世"
-"处事"
-"处于"
-"处以"
-"处分"
-"处在"
-"处境"
-"处处"
-"处女"
-"处方"
-"处死"
-"处理"
-"处理器"
-"处级"
-"处罚"
-"处置"
-"处长"
-"备"
-"备份"
-"备受"
-"备忘"
-"备战"
-"备案"
-"备注"
-"备用"
-"备考"
-"备课"
-"変"
-"复"
-"复习"
-"复产"
-"复仇"
-"复位"
-"复兴"
-"复出"
-"复制"
-"复印"
-"复印件"
-"复原"
-"复发"
-"复古"
-"复合"
-"复合材料"
-"复复"
-"复审"
-"复工"
-"复旦"
-"复旦大学"
-"复杂"
-"复杂性"
-"复查"
-"复核"
-"复活"
-"复苏"
-"复议"
-"复试"
-"复读"
-"夏"
-"夏令"
-"夏令营"
-"夏侯"
-"夏天"
-"夏威夷"
-"夏季"
-"夏日"
-"夏普"
-"夏洛"
-"夏洛特"
-"夏秋"
-"夔"
-"夕"
-"夕阳"
-"外"
-"外乎"
-"外事"
-"外交"
-"外交部"
-"外人"
-"外企"
-"外伤"
-"外侧"
-"外公"
-"外出"
-"外力"
-"外加"
-"外包"
-"外卖"
-"外号"
-"外向"
-"外商"
-"外商投资"
-"外围"
-"外国"
-"外国人"
-"外国语"
-"外地"
-"外墙"
-"外壳"
-"外套"
-"外婆"
-"外媒"
-"外层"
-"外币"
-"外延"
-"外形"
-"外挂"
-"外接"
-"外援"
-"外文"
-"外星"
-"外星人"
-"外景"
-"外来"
-"外汇"
-"外汇储备"
-"外甥"
-"外用"
-"外界"
-"外皮"
-"外省"
-"外科"
-"外籍"
-"外线"
-"外经贸"
-"外置"
-"外行"
-"外衣"
-"外表"
-"外观"
-"外观设计"
-"外语"
-"外语教学"
-"外貌"
-"外贸"
-"外资"
-"外边"
-"外部"
-"外链"
-"外阴"
-"外面"
-"夙"
-"多"
-"多一些"
-"多一点"
-"多个"
-"多为"
-"多久"
-"多么"
-"多于"
-"多云"
-"多些"
-"多人"
-"多以"
-"多伦"
-"多伦多"
-"多位"
-"多余"
-"多元"
-"多元化"
-"多公里"
-"多出"
-"多功能"
-"多加"
-"多半"
-"多单"
-"多发"
-"多变"
-"多名"
-"多喝水"
-"多地"
-"多坏"
-"多处"
-"多多"
-"多大"
-"多天"
-"多头"
-"多好"
-"多姿"
-"多媒体"
-"多家"
-"多少"
-"多层"
-"多层次"
-"多岁"
-"多平方"
-"多年"
-"多彩"
-"多得"
-"多情"
-"多想"
-"多才"
-"多数"
-"多方"
-"多方面"
-"多月"
-"多有"
-"多条"
-"多样"
-"多样化"
-"多样性"
-"多次"
-"多款"
-"多点"
-"多用"
-"多种"
-"多种多样"
-"多种形式"
-"多空"
-"多篇"
-"多米"
-"多糖"
-"多维"
-"多芬"
-"多见"
-"多见于"
-"多谢"
-"多达"
-"多远"
-"多部"
-"多重"
-"多钱"
-"多长"
-"多长时间"
-"多难"
-"多项"
-"多食"
-"夜"
-"夜夜"
-"夜市"
-"夜幕"
-"夜店"
-"夜总"
-"夜晚"
-"夜景"
-"夜深"
-"夜班"
-"夜空"
-"夜色"
-"夜里"
-"夜间"
-"够"
-"夠"
-"夢"
-"夤"
-"大"
-"大不了"
-"大专"
-"大业"
-"大中专"
-"大中华"
-"大中型"
-"大为"
-"大乘"
-"大事"
-"大于"
-"大人"
-"大件"
-"大众"
-"大伙"
-"大会"
-"大体"
-"大佛"
-"大作"
-"大佬"
-"大使"
-"大使馆"
-"大侠"
-"大便"
-"大全"
-"大关"
-"大兴"
-"大写"
-"大军"
-"大减"
-"大到"
-"大力"
-"大力发展"
-"大力支持"
-"大势"
-"大匙"
-"大千"
-"大半"
-"大卫"
-"大厅"
-"大厦"
-"大发"
-"大叔"
-"大叫"
-"大可"
-"大吃"
-"大吉"
-"大同"
-"大名"
-"大吵"
-"大哥"
-"大哭"
-"大唐"
-"大喊"
-"大喜"
-"大国"
-"大圣"
-"大地"
-"大块"
-"大坝"
-"大型"
-"大城市"
-"大堂"
-"大堆"
-"大增"
-"大声"
-"大多"
-"大多数"
-"大大"
-"大大增加"
-"大大小小"
-"大大提高"
-"大大的"
-"大大降低"
-"大夫"
-"大头"
-"大奖"
-"大好"
-"大妈"
-"大姐"
-"大姨"
-"大字"
-"大学"
-"大学毕业"
-"大学生"
-"大宗"
-"大宝"
-"大家"
-"大家庭"
-"大将"
-"大将军"
-"大小"
-"大局"
-"大屏"
-"大屏幕"
-"大展"
-"大山"
-"大峡谷"
-"大巴"
-"大师"
-"大帝"
-"大幅"
-"大幅度"
-"大庆"
-"大度"
-"大开"
-"大德"
-"大怒"
-"大悟"
-"大悲"
-"大意"
-"大戏"
-"大成"
-"大战"
-"大户"
-"大手"
-"大打"
-"大打折扣"
-"大批"
-"大把"
-"大拇指"
-"大招"
-"大提琴"
-"大方"
-"大明"
-"大有"
-"大本"
-"大本营"
-"大杯"
-"大林"
-"大枣"
-"大树"
-"大桥"
-"大棚"
-"大楼"
-"大概"
-"大殿"
-"大气"
-"大气污染"
-"大汉"
-"大汗"
-"大江"
-"大河"
-"大法"
-"大洋"
-"大海"
-"大涨"
-"大清"
-"大湾"
-"大潮"
-"大火"
-"大灯"
-"大炮"
-"大热"
-"大熊猫"
-"大爱"
-"大爷"
-"大片"
-"大牌"
-"大牛"
-"大王"
-"大理"
-"大理石"
-"大病"
-"大白"
-"大白菜"
-"大盘"
-"大碗"
-"大神"
-"大笑"
-"大米"
-"大红"
-"大约"
-"大纲"
-"大肆"
-"大肠"
-"大胆"
-"大胜"
-"大脑"
-"大腿"
-"大臣"
-"大自然"
-"大致"
-"大葱"
-"大蒜"
-"大街"
-"大街小巷"
-"大衣"
-"大西洋"
-"大观"
-"大规模"
-"大豆"
-"大象"
-"大财"
-"大赏"
-"大赛"
-"大跌"
-"大路"
-"大辞"
-"大运"
-"大连"
-"大连市"
-"大连理工"
-"大选"
-"大通"
-"大道"
-"大部"
-"大部分"
-"大都"
-"大都市"
-"大酒店"
-"大量"
-"大钱"
-"大长"
-"大门"
-"大闸"
-"大闹"
-"大队"
-"大阪"
-"大陆"
-"大院"
-"大雨"
-"大雪"
-"大面"
-"大面积"
-"大革命"
-"大额"
-"大风"
-"大餐"
-"大骂"
-"大鱼"
-"大鹏"
-"大麦"
-"大麻"
-"大黄"
-"大龄"
-"天"
-"天上"
-"天下"
-"天下第一"
-"天主"
-"天主教"
-"天亮"
-"天人"
-"天价"
-"天体"
-"天使"
-"天候"
-"天内"
-"天台"
-"天后"
-"天启"
-"天呐"
-"天命"
-"天国"
-"天地"
-"天堂"
-"天大"
-"天天"
-"天子"
-"天安门"
-"天宝"
-"天宫"
-"天山"
-"天干"
-"天平"
-"天府"
-"天性"
-"天成"
-"天才"
-"天数"
-"天文"
-"天文学"
-"天时"
-"天桥"
-"天气"
-"天气预报"
-"天水"
-"天池"
-"天河"
-"天津"
-"天津市"
-"天涯"
-"天灾"
-"天然"
-"天然气"
-"天猫"
-"天王"
-"天生"
-"天皇"
-"天盖"
-"天真"
-"天神"
-"天秤"
-"天空"
-"天窗"
-"天竺"
-"天籁"
-"天线"
-"天花"
-"天花板"
-"天蝎"
-"天赋"
-"天边"
-"天道"
-"天长"
-"天门"
-"天际"
-"天马"
-"天鹅"
-"天黑"
-"天龙"
-"太"
-"太久"
-"太低"
-"太冷"
-"太原"
-"太原市"
-"太后"
-"太和"
-"太多"
-"太大"
-"太太"
-"太好"
-"太好了"
-"太子"
-"太守"
-"太宗"
-"太小"
-"太少"
-"太岁"
-"太差"
-"太平"
-"太平天国"
-"太平洋"
-"太强"
-"太快"
-"太晚"
-"太极"
-"太极拳"
-"太深"
-"太湖"
-"太白"
-"太监"
-"太祖"
-"太空"
-"太累"
-"太行"
-"太贵"
-"太过"
-"太远"
-"太重"
-"太阳"
-"太阳系"
-"太阳能"
-"太阴"
-"太难"
-"太高"
-"夫"
-"夫人"
-"夫妇"
-"夫妻"
-"夫妻俩"
-"夫子"
-"夬"
-"夭"
-"央"
-"央企"
-"央行"
-"央视"
-"夯"
-"夯实"
-"失"
-"失业"
-"失业率"
-"失传"
-"失信"
-"失利"
-"失去"
-"失常"
-"失忆"
-"失恋"
-"失意"
-"失控"
-"失效"
-"失明"
-"失望"
-"失真"
-"失眠"
-"失落"
-"失衡"
-"失误"
-"失调"
-"失败"
-"失踪"
-"头"
-"头上"
-"头像"
-"头发"
-"头号"
-"头孢"
-"头戴"
-"头昏"
-"头晕"
-"头条"
-"头来"
-"头疼"
-"头痛"
-"头皮"
-"头盔"
-"头禅"
-"头等"
-"头脑"
-"头衔"
-"头部"
-"头顶"
-"夷"
-"夸"
-"夸大"
-"夸奖"
-"夸张"
-"夹"
-"夹克"
-"夹杂"
-"夺"
-"夺冠"
-"夺取"
-"夺回"
-"夺得"
-"夺目"
-"夼"
-"奁"
-"奂"
-"奄"
-"奇"
-"奇侠"
-"奇奇"
-"奇妙"
-"奇幻"
-"奇异"
-"奇怪"
-"奇想"
-"奇才"
-"奇特"
-"奇瑞"
-"奇石"
-"奇艺"
-"奇葩"
-"奇观"
-"奇迹"
-"奇遇"
-"奈"
-"奈何"
-"奉"
-"奉命"
-"奉献"
-"奉行"
-"奋"
-"奋力"
-"奋勇"
-"奋发"
-"奋战"
-"奋斗"
-"奋进"
-"奎"
-"奏"
-"契"
-"契丹"
-"契合"
-"契机"
-"契约"
-"奔"
-"奔放"
-"奔波"
-"奔腾"
-"奔走"
-"奔赴"
-"奔跑"
-"奔驰"
-"奕"
-"奖"
-"奖励"
-"奖品"
-"奖学"
-"奖学金"
-"奖惩"
-"奖杯"
-"奖牌"
-"奖赛"
-"奖金"
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-"套"
-"套利"
-"套房"
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-"套装"
-"套路"
-"套餐"
-"奘"
-"奚"
-"奠"
-"奠基"
-"奠定"
-"奢"
-"奢侈"
-"奢侈品"
-"奢华"
-"奢望"
-"奥"
-"奥会"
-"奥利"
-"奥地利"
-"奥尔"
-"奥尼"
-"奥巴"
-"奥巴马"
-"奥数"
-"奥斯"
-"奥斯卡"
-"奥斯曼"
-"奥林匹克"
-"奥林匹克运动"
-"奥特"
-"奥特曼"
-"奥秘"
-"奥运"
-"奥运会"
-"奥迪"
-"奭"
-"女"
-"女主"
-"女主角"
-"女人"
-"女人味"
-"女儿"
-"女友"
-"女同"
-"女士"
-"女娲"
-"女婿"
-"女子"
-"女孩"
-"女孩儿"
-"女孩子"
-"女巫"
-"女性"
-"女性朋友"
-"女排"
-"女方"
-"女明星"
-"女星"
-"女朋友"
-"女权"
-"女权主义"
-"女演员"
-"女王"
-"女生"
-"女真"
-"女神"
-"女票"
-"女童"
-"女装"
-"女足"
-"女郎"
-"奴"
-"奴隶"
-"奶"
-"奶奶"
-"奶油"
-"奶牛"
-"奶瓶"
-"奶粉"
-"奶茶"
-"奶酪"
-"奸"
-"她"
-"她们"
-"她家"
-"好"
-"好不"
-"好不好"
-"好不容易"
-"好久"
-"好书"
-"好了"
-"好事"
-"好些"
-"好人"
-"好似"
-"好像"
-"好几"
-"好几个"
-"好几年"
-"好友"
-"好吃"
-"好听"
-"好喝"
-"好坏"
-"好处"
-"好多"
-"好奇"
-"好奇心"
-"好好"
-"好好学"
-"好好学习"
-"好学"
-"好孩子"
-"好心"
-"好感"
-"好日子"
-"好歹"
-"好比"
-"好汉"
-"好消息"
-"好点"
-"好玩"
-"好用"
-"好看"
-"好笑"
-"好莱"
-"好莱坞"
-"好评"
-"好象"
-"好转"
-"好运"
-"妁"
-"如"
-"如下"
-"如今"
-"如何"
-"如其"
-"如初"
-"如同"
-"如图"
-"如实"
-"如意"
-"如愿"
-"如是"
-"如有"
-"如期"
-"如来"
-"如来佛"
-"如果"
-"如果说"
-"如梦"
-"如此"
-"如此一来"
-"如水"
-"如火"
-"如火如荼"
-"如玉"
-"如生"
-"如画"
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-"如若"
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-"尔斯"
-"尔曼"
-"尔特"
-"尔盖"
-"尔蒙"
-"尔雅"
-"尔顿"
-"尕"
-"尖"
-"尖叫"
-"尖头"
-"尖端"
-"尖锐"
-"尖锐湿疣"
-"尘"
-"尘世"
-"尘土"
-"尘埃"
-"尚"
-"尚书"
-"尚可"
-"尚存"
-"尚无"
-"尚有"
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-"尤"
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-"就会"
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-"就够"
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-"就是说"
-"就此"
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-"就职"
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-"就读"
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-"尸"
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-"尼"
-"尼亚"
-"尼克"
-"尼古"
-"尼古拉"
-"尼奥"
-"尼尔"
-"尼康"
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-"尼龙"
-"尽"
-"尽全"
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-"尽力"
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-"尽头"
-"尽心"
-"尽快"
-"尽情"
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-"尽显"
-"尽有"
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-"尽管如此"
-"尽职"
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-"尽量减少"
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-"尽量避免"
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-"尾声"
-"尾巴"
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-"尾盘"
-"尾部"
-"尿"
-"尿液"
-"尿素"
-"尿道"
-"尿酸"
-"尿频"
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-"山石"
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-"山脚"
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-"岂"
-"岈"
-"岌"
-"岐"
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-"岔"
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-"巡"
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-"巢"
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-"工业化"
-"工业区"
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-"工业园区"
-"工业大学"
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-"工人"
-"工件"
-"工会"
-"工伤"
-"工伤保险"
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-"工作人员"
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-"工信"
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-"工具"
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-"工农业"
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-"工学"
-"工序"
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-"工程"
-"工程学"
-"工程学院"
-"工程师"
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-"工程技术"
-"工程施工"
-"工程质量"
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-"工艺"
-"工艺品"
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-"工行"
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-"工贸"
-"工资"
-"工部"
-"左"
-"左上"
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-"巩"
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-"巫"
-"差"
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-"己"
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-"巳"
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-"巴塞罗那"
-"巴士"
-"巴尔"
-"巴巴"
-"巴拉"
-"巴拿马"
-"巴掌"
-"巴斯"
-"巴比伦"
-"巴洛克"
-"巴特"
-"巴菲"
-"巴萨"
-"巴西"
-"巴马"
-"巴黎"
-"巷"
-"巷子"
-"巽"
-"巾"
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-"巿"
-"币"
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-"市场营销"
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-"市委书记"
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-"市容"
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-"市民"
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-"布"
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-"常规"
-"常规赛"
-"常识"
-"常说"
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-"常驻"
-"帻"
-"帼"
-"帽"
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-"幂"
-"幄"
-"幅"
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-"幌"
-"幔"
-"幕"
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-"幞"
-"幡"
-"幢"
-"幫"
-"干"
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-"干什么"
-"干净"
-"干嘛"
-"干干"
-"干干净净"
-"干扰"
-"干支"
-"干旱"
-"干洗"
-"干活"
-"干涉"
-"干涩"
-"干燥"
-"干线"
-"干练"
-"干细胞"
-"干脆"
-"干警"
-"干货"
-"干辣椒"
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-"平地"
-"平均"
-"平均分"
-"平均工资"
-"平均气温"
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-"平均线"
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-"平定"
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-"平常心"
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-"平庸"
-"平息"
-"平整"
-"平方"
-"平方公"
-"平方公里"
-"平方米"
-"平日"
-"平时"
-"平权"
-"平板"
-"平民"
-"平淡"
-"平滑"
-"平生"
-"平稳"
-"平等"
-"平米"
-"平行"
-"平衡"
-"平装"
-"平阳"
-"平静"
-"平面"
-"平顶山"
-"年"
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-"年代"
-"年份"
-"年会"
-"年内"
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-"年均"
-"年夜"
-"年头"
-"年少"
-"年幼"
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-"年度"
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-"年月"
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-"年生"
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-"年级"
-"年纪"
-"年终"
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-"年轻"
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-"年青"
-"年龄"
-"年龄段"
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-"并不一定"
-"并不多"
-"并不大"
-"并不知道"
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-"并购"
-"并重"
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-"幸好"
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-"幸福感"
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-"幺"
-"幻"
-"幻影"
-"幻想"
-"幻灯"
-"幻觉"
-"幼"
-"幼儿"
-"幼儿园"
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-"幼稚"
-"幼虫"
-"幽"
-"幽灵"
-"幽默"
-"幾"
-"广"
-"广东"
-"广东省"
-"广为"
-"广义"
-"广受"
-"广告"
-"广场"
-"广大"
-"广大群众"
-"广州"
-"广州市"
-"广度"
-"广播"
-"广播电"
-"广播电视"
-"广汽"
-"广泛"
-"广泛应用"
-"广深"
-"广电"
-"广西"
-"广西壮族自治区"
-"广角"
-"广阔"
-"庄"
-"庄严"
-"庄园"
-"庄子"
-"庄家"
-"庄村"
-"庄稼"
-"庄重"
-"庄镇"
-"庆"
-"庆典"
-"庆幸"
-"庆祝"
-"庇"
-"庇护"
-"床"
-"床上"
-"床位"
-"床单"
-"床垫"
-"床头"
-"床边"
-"庋"
-"序"
-"序列"
-"序号"
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-"庐"
-"庐山"
-"庑"
-"库"
-"库克"
-"库存"
-"库容"
-"库尔"
-"库房"
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-"应为"
-"应付"
-"应俱"
-"应力"
-"应变"
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-"应届"
-"应当"
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-"应手"
-"应援"
-"应收"
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-"应聘"
-"应聘者"
-"应试"
-"应该"
-"应运"
-"应运而生"
-"应邀"
-"应酬"
-"底"
-"底下"
-"底子"
-"底层"
-"底座"
-"底气"
-"底盘"
-"底线"
-"底蕴"
-"底部"
-"庖"
-"店"
-"店主"
-"店内"
-"店员"
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-"店长"
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-"庙"
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-"庚"
-"府"
-"庞"
-"庞大"
-"废"
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-"废弃物"
-"废旧"
-"废气"
-"废水"
-"废物"
-"废话"
-"废除"
-"庠"
-"庥"
-"度"
-"度假"
-"度假区"
-"度假村"
-"度日"
-"度过"
-"度量"
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-"座标"
-"座椅"
-"座落"
-"座谈"
-"座谈会"
-"庭"
-"庭审"
-"庭院"
-"庵"
-"庶"
-"康"
-"康乐"
-"康复"
-"康德"
-"康熙"
-"庸"
-"庸俗"
-"庹"
-"庾"
-"廉"
-"廉价"
-"廉政"
-"廉洁"
-"廊"
-"廊坊"
-"廓"
-"廖"
-"廛"
-"廣"
-"廨"
-"廪"
-"延"
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-"延年"
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-"延期"
-"延续"
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-"延误"
-"延边"
-"延迟"
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-"廷"
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-"建军"
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-"建华"
-"建国"
-"建安"
-"建成"
-"建房"
-"建有"
-"建材"
-"建构"
-"建档"
-"建档立卡"
-"建模"
-"建立"
-"建筑"
-"建筑业"
-"建筑学"
-"建筑工程"
-"建筑师"
-"建筑材料"
-"建筑物"
-"建筑设计"
-"建筑面积"
-"建议"
-"建设"
-"建设工程"
-"建设银行"
-"建设项目"
-"建造"
-"廿"
-"开"
-"开业"
-"开个"
-"开会"
-"开元"
-"开光"
-"开关"
-"开具"
-"开出"
-"开刀"
-"开创"
-"开办"
-"开发"
-"开发区"
-"开发商"
-"开发者"
-"开口"
-"开启"
-"开国"
-"开场"
-"开复"
-"开头"
-"开始"
-"开学"
-"开封"
-"开局"
-"开展"
-"开工"
-"开幕"
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-"开店"
-"开庭"
-"开开心"
-"开张"
-"开心"
-"开悟"
-"开战"
-"开户"
-"开房"
-"开拓"
-"开拓者"
-"开挂"
-"开挖"
-"开播"
-"开支"
-"开放"
-"开放式"
-"开明"
-"开朗"
-"开机"
-"开来"
-"开水"
-"开源"
-"开玩笑"
-"开盘"
-"开眼"
-"开着"
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-"开窗"
-"开端"
-"开篇"
-"开胃"
-"开花"
-"开裂"
-"开设"
-"开车"
-"开辟"
-"开通"
-"开采"
-"开销"
-"开门"
-"开阔"
-"开除"
-"开黑"
-"弁"
-"异"
-"异乡"
-"异味"
-"异国"
-"异地"
-"异域"
-"异常"
-"异形"
-"异性"
-"异样"
-"异步"
-"异步电"
-"异物"
-"异类"
-"异议"
-"弃"
-"弄"
-"弄清"
-"弄清楚"
-"弈"
-"弊"
-"弊端"
-"弋"
-"式"
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-"弑"
-"弓"
-"弓箭"
-"引"
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-"引人注目"
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-"引力"
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-"引导"
-"引战"
-"引擎"
-"引来"
-"引水"
-"引流"
-"引爆"
-"引玉"
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-"引申"
-"引诱"
-"引资"
-"引起"
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-"引领"
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-"弗兰克"
-"弗拉"
-"弗洛伊"
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-"弛"
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-"张"
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-"弥漫"
-"弥补"
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-"弦乐"
-"弧"
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-"弧形"
-"弩"
-"弭"
-"弯"
-"弯弯"
-"弯曲"
-"弯腰"
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-"弱"
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-"添加"
-"添加剂"
-"添花"
-"淼"
-"清"
-"清仓"
-"清代"
-"清净"
-"清凉"
-"清北"
-"清华"
-"清华大学"
-"清单"
-"清扫"
-"清新"
-"清明"
-"清明节"
-"清晨"
-"清晰"
-"清晰度"
-"清朝"
-"清末"
-"清楚"
-"清气"
-"清水"
-"清汤"
-"清河"
-"清泉"
-"清洁"
-"清洗"
-"清淡"
-"清清"
-"清清楚楚"
-"清澈"
-"清热"
-"清热解"
-"清热解毒"
-"清爽"
-"清理"
-"清白"
-"清真"
-"清秀"
-"清算"
-"清纯"
-"清脆"
-"清蒸"
-"清远"
-"清醒"
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-"清除"
-"清静"
-"清风"
-"清香"
-"清高"
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-"渊源"
-"渌"
-"渍"
-"渎"
-"渐"
-"渐变"
-"渐渐"
-"渐进"
-"渑"
-"渔"
-"渔业"
-"渔民"
-"渔船"
-"渗"
-"渗入"
-"渗出"
-"渗透"
-"渗透到"
-"渚"
-"渝"
-"渠"
-"渠道"
-"渡"
-"渡口"
-"渡江"
-"渡河"
-"渡边"
-"渡过"
-"渣"
-"渤"
-"渤海"
-"渥"
-"温"
-"温和"
-"温哥"
-"温婉"
-"温室"
-"温州"
-"温差"
-"温带"
-"温度"
-"温开水"
-"温情"
-"温控"
-"温暖"
-"温柔"
-"温水"
-"温泉"
-"温润"
-"温热"
-"温饱"
-"温馨"
-"渫"
-"渭"
-"港"
-"港元"
-"港口"
-"港台"
-"港币"
-"港湾"
-"港澳"
-"港澳台"
-"港股"
-"渲"
-"渲染"
-"渴"
-"渴望"
-"游"
-"游乐"
-"游人"
-"游侠"
-"游击"
-"游击队"
-"游刃"
-"游客"
-"游戏"
-"游戏规则"
-"游泳"
-"游泳池"
-"游牧"
-"游玩"
-"游离"
-"游艇"
-"游荡"
-"游行"
-"游览"
-"游记"
-"游走"
-"渺"
-"渺小"
-"渺茫"
-"湃"
-"湄"
-"湄公"
-"湉"
-"湍"
-"湎"
-"湔"
-"湖"
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-"湖人队"
-"湖北"
-"湖北省"
-"湖区"
-"湖南"
-"湖南卫视"
-"湖南省"
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-"湖水"
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-"湖边"
-"湖面"
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-"湘潭"
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-"湛"
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-"湮"
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-"漭"
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-"漱"
-"漱口"
-"漳"
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-"漶"
-"漾"
-"潇"
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-"潋"
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-"潭"
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-"潺"
-"潼"
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-"澳"
-"澳元"
-"澳大利亚"
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-"澹"
-"激"
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-"瀣"
-"瀬"
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-"灌水"
-"灌注"
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-"灌篮"
-"灌装"
-"灌输"
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-"灞"
-"灣"
-"火"
-"火候"
-"火力"
-"火山"
-"火影"
-"火星"
-"火柴"
-"火火"
-"火灾"
-"火炬"
-"火烧"
-"火热"
-"火焰"
-"火爆"
-"火箭"
-"火箭队"
-"火腿"
-"火花"
-"火药"
-"火车"
-"火车票"
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-"火锅"
-"火龙"
-"灬"
-"灭"
-"灭亡"
-"灭火"
-"灭绝"
-"灭菌"
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-"灵气"
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-"灵魂"
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-"灾"
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-"灾害"
-"灾难"
-"灿"
-"灿烂"
-"炀"
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-"炒锅"
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-"炜"
-"炝"
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-"炫耀"
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-"炮"
-"炮儿"
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-"炮友"
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-"炮火"
-"炯"
-"炱"
-"炳"
-"炷"
-"炸"
-"炸弹"
-"炸药"
-"炸鸡"
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-"点位"
-"点儿"
-"点击"
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-"点半"
-"点名"
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-"点子"
-"点心"
-"点数"
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-"点火"
-"点点"
-"点点头"
-"点点滴滴"
-"点燃"
-"点球"
-"点睛"
-"点缀"
-"点评"
-"点赞"
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-"炼"
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-"烈日"
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-"烘干"
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-"烟"
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-"烟火"
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-"烟草"
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-"烟雨"
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-"热处理"
-"热巴"
-"热带"
-"热度"
-"热心"
-"热忱"
-"热情"
-"热播"
-"热敷"
-"热气"
-"热水"
-"热水器"
-"热潮"
-"热火"
-"热点"
-"热烈"
-"热爱"
-"热电"
-"热线"
-"热能"
-"热血"
-"热衷"
-"热衷于"
-"热议"
-"热身"
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-"热销"
-"热门"
-"热闹"
-"烯"
-"烷"
-"烹"
-"烹调"
-"烹饪"
-"烽"
-"烽火"
-"焉"
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-"焊接"
-"焐"
-"焓"
-"焕"
-"焕发"
-"焕然"
-"焖"
-"焗"
-"焘"
-"焙"
-"焚"
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-"焰"
-"焱"
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-"煌"
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-"煤气"
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-"煦"
-"照"
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-"煮粥"
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-"煸"
-"煸炒"
-"煺"
-"煽"
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-"熄灭"
-"熄灯"
-"熊"
-"熊市"
-"熊猫"
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-"熙"
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-"熥"
-"熨"
-"熬"
-"熬夜"
-"熬过"
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-"熹"
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-"燃油"
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-"燔"
-"燕"
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-"燥"
-"燧"
-"燮"
-"燹"
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-"爆棚"
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-"爆炸"
-"爆照"
-"爆破"
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-"爆笑"
-"爆米"
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-"爷爷奶奶"
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-"犯错误"
-"犰"
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-"状元"
-"状况"
-"状态"
-"犷"
-"犸"
-"犹"
-"犹太"
-"犹太人"
-"犹如"
-"犹豫"
-"狀"
-"狁"
-"狂"
-"狂妄"
-"狂欢"
-"狂热"
-"狂风"
-"狄"
-"狈"
-"狍"
-"狎"
-"狐"
-"狐狸"
-"狐臭"
-"狒"
-"狗"
-"狗屁"
-"狗屎"
-"狗狗"
-"狗肉"
-"狗血"
-"狙"
-"狙击"
-"狞"
-"狠"
-"狠心"
-"狠抓"
-"狠狠"
-"狡"
-"狡猾"
-"狨"
-"狩"
-"狩猎"
-"独"
-"独一无二"
-"独具"
-"独创"
-"独到"
-"独占"
-"独厚"
-"独唱"
-"独处"
-"独奏"
-"独孤"
-"独家"
-"独居"
-"独往"
-"独有"
-"独特"
-"独生"
-"独生子女"
-"独秀"
-"独立"
-"独立思考"
-"独立性"
-"独自"
-"独著"
-"独行"
-"独裁"
-"独角"
-"独角兽"
-"独资"
-"狭"
-"狭义"
-"狭小"
-"狭窄"
-"狭长"
-"狭隘"
-"狮"
-"狮子"
-"狯"
-"狰"
-"狱"
-"狲"
-"狳"
-"狷"
-"狸"
-"狺"
-"狼"
-"狼狈"
-"猁"
-"猃"
-"猎"
-"猎人"
-"猎头"
-"猎物"
-"猕"
-"猕猴"
-"猕猴桃"
-"猖"
-"猗"
-"猛"
-"猛烈"
-"猛然"
-"猛进"
-"猛龙"
-"猜"
-"猜想"
-"猜测"
-"猜猜"
-"猝"
-"猝死"
-"猞"
-"猡"
-"猢"
-"猥"
-"猥亵"
-"猥琐"
-"猩"
-"猩猩"
-"猪"
-"猪八戒"
-"猪油"
-"猪肉"
-"猪肝"
-"猪脚"
-"猪蹄"
-"猫"
-"猫咪"
-"猬"
-"献"
-"献上"
-"献出"
-"献给"
-"献血"
-"献身"
-"猱"
-"猴"
-"猴子"
-"猷"
-"猹"
-"猾"
-"猿"
-"獐"
-"獒"
-"獗"
-"獠"
-"獨"
-"獬"
-"獭"
-"獴"
-"獾"
-"玄"
-"玄关"
-"玄学"
-"玄宗"
-"玄幻"
-"玄武"
-"率"
-"率为"
-"率先"
-"率达"
-"率领"
-"玉"
-"玉兰"
-"玉器"
-"玉林"
-"玉树"
-"玉皇"
-"玉石"
-"玉米"
-"玉龙"
-"王"
-"王八"
-"王国"
-"王妃"
-"王子"
-"王宝强"
-"王室"
-"王家"
-"王府"
-"王思聪"
-"王朝"
-"王母"
-"王氏"
-"王爷"
-"王牌"
-"王维"
-"王羲"
-"王老师"
-"王者"
-"王莽"
-"王道"
-"玎"
-"玑"
-"玕"
-"玖"
-"玙"
-"玛"
-"玛丽"
-"玛瑙"
-"玛雅"
-"玟"
-"玠"
-"玡"
-"玢"
-"玥"
-"玦"
-"玩"
-"玩乐"
-"玩儿"
-"玩具"
-"玩家"
-"玩弄"
-"玩得"
-"玩意"
-"玩法"
-"玩游戏"
-"玩玩"
-"玩笑"
-"玩耍"
-"玩过"
-"玫"
-"玫瑰"
-"玫瑰花"
-"玭"
-"玮"
-"环"
-"环保"
-"环卫"
-"环境"
-"环境优美"
-"环境保护"
-"环境卫生"
-"环境污染"
-"环形"
-"环比"
-"环氧"
-"环游"
-"环状"
-"环球"
-"环绕"
-"环节"
-"环路"
-"现"
-"现为"
-"现今"
-"现代"
-"现代人"
-"现代农业"
-"现代化"
-"现代医学"
-"现任"
-"现在"
-"现场"
-"现如今"
-"现存"
-"现实"
-"现实主义"
-"现实生活"
-"现将"
-"现已"
-"现役"
-"现成"
-"现时"
-"现有"
-"现款"
-"现状"
-"现行"
-"现象"
-"现货"
-"现身"
-"现金"
-"现金流"
-"现阶段"
-"玲"
-"玲珑"
-"玳"
-"玷"
-"玺"
-"玻"
-"玻璃"
-"玻璃钢"
-"珀"
-"珂"
-"珅"
-"珈"
-"珉"
-"珊"
-"珊瑚"
-"珍"
-"珍品"
-"珍妮"
-"珍宝"
-"珍惜"
-"珍爱"
-"珍珠"
-"珍稀"
-"珍藏"
-"珍贵"
-"珏"
-"珐"
-"珑"
-"珙"
-"珞"
-"珠"
-"珠三角"
-"珠子"
-"珠宝"
-"珠江"
-"珠海"
-"珣"
-"珥"
-"珧"
-"珩"
-"珪"
-"班"
-"班上"
-"班主任"
-"班子"
-"班族"
-"班级"
-"班组"
-"班车"
-"班里"
-"班长"
-"珰"
-"珲"
-"現"
-"現在"
-"球"
-"球员"
-"球场"
-"球形"
-"球星"
-"球菌"
-"球衣"
-"球迷"
-"球队"
-"琅"
-"理"
-"理事"
-"理事会"
-"理事长"
-"理会"
-"理化"
-"理发"
-"理取"
-"理学"
-"理工"
-"理工大学"
-"理工学院"
-"理应"
-"理念"
-"理性"
-"理想"
-"理想主义"
-"理所"
-"理所应"
-"理所当然"
-"理智"
-"理查德"
-"理气"
-"理清"
-"理由"
-"理疗"
-"理科"
-"理解"
-"理论"
-"理论知识"
-"理财"
-"理财产品"
-"理赔"
-"琇"
-"琉"
-"琉球"
-"琉璃"
-"琊"
-"琏"
-"琐"
-"琐事"
-"琐碎"
-"琚"
-"琛"
-"琢"
-"琢磨"
-"琤"
-"琥"
-"琥珀"
-"琦"
-"琨"
-"琪"
-"琫"
-"琬"
-"琮"
-"琯"
-"琰"
-"琳"
-"琳琅"
-"琴"
-"琵"
-"琵琶"
-"琶"
-"琼"
-"琼斯"
-"琼脂"
-"瑁"
-"瑄"
-"瑕"
-"瑕疵"
-"瑗"
-"瑙"
-"瑚"
-"瑛"
-"瑜"
-"瑜伽"
-"瑞"
-"瑞典"
-"瑞士"
-"瑞安"
-"瑞拉"
-"瑟"
-"瑭"
-"瑰"
-"瑶"
-"瑶族"
-"瑾"
-"璀"
-"璀璨"
-"璁"
-"璃"
-"璇"
-"璈"
-"璋"
-"璎"
-"璐"
-"璘"
-"璜"
-"璞"
-"璟"
-"璠"
-"璧"
-"璨"
-"璩"
-"璪"
-"環"
-"璺"
-"瓒"
-"瓘"
-"瓜"
-"瓜子"
-"瓜果"
-"瓠"
-"瓢"
-"瓣"
-"瓤"
-"瓦"
-"瓦斯"
-"瓦解"
-"瓮"
-"瓯"
-"瓴"
-"瓶"
-"瓶子"
-"瓶盖"
-"瓶装"
-"瓶颈"
-"瓷"
-"瓷器"
-"瓷砖"
-"瓿"
-"甄"
-"甍"
-"甏"
-"甑"
-"甘"
-"甘心"
-"甘愿"
-"甘油"
-"甘甜"
-"甘肃"
-"甘肃省"
-"甘草"
-"甘蔗"
-"甘露"
-"甙"
-"甚"
-"甚者"
-"甚至"
-"甚至于"
-"甜"
-"甜味"
-"甜品"
-"甜点"
-"甜甜"
-"甜甜的"
-"甜美"
-"甜蜜"
-"甜食"
-"生"
-"生下"
-"生于"
-"生产"
-"生产力"
-"生产厂"
-"生产厂家"
-"生产商"
-"生产工艺"
-"生产总值"
-"生产线"
-"生产者"
-"生产能力"
-"生人"
-"生出"
-"生前"
-"生动"
-"生化"
-"生发"
-"生命"
-"生命力"
-"生命危险"
-"生命周期"
-"生命安全"
-"生命线"
-"生姜"
-"生子"
-"生存"
-"生平"
-"生态"
-"生态学"
-"生态环境"
-"生态系统"
-"生怕"
-"生性"
-"生意"
-"生成"
-"生抽"
-"生效"
-"生日"
-"生日快乐"
-"生日礼"
-"生机"
-"生来"
-"生根"
-"生死"
-"生殖"
-"生殖器"
-"生气"
-"生水"
-"生津"
-"生活"
-"生活习惯"
-"生活品质"
-"生活态度"
-"生活用品"
-"生活费"
-"生涯"
-"生源"
-"生灵"
-"生物"
-"生物化学"
-"生物学"
-"生猪"
-"生理"
-"生理学"
-"生生"
-"生病"
-"生硬"
-"生素"
-"生肖"
-"生育"
-"生菜"
-"生计"
-"生锈"
-"生长"
-"生长发育"
-"生鲜"
-"產"
-"甥"
-"用"
-"用于"
-"用人"
-"用人单位"
-"用以"
-"用作"
-"用具"
-"用到"
-"用力"
-"用功"
-"用品"
-"用地"
-"用处"
-"用尽"
-"用工"
-"用心"
-"用意"
-"用户"
-"用户数"
-"用房"
-"用手"
-"用料"
-"用来"
-"用水"
-"用法"
-"用狗"
-"用电"
-"用笔"
-"用药"
-"用词"
-"用语"
-"用车"
-"用过"
-"用途"
-"用量"
-"用钱"
-"用餐"
-"甩"
-"甫"
-"甬"
-"甭"
-"田"
-"田中"
-"田园"
-"田地"
-"田径"
-"田野"
-"田间"
-"由"
-"由于"
-"由此"
-"由此可"
-"由此可见"
-"由衷"
-"甲"
-"甲亢"
-"甲午"
-"甲基"
-"甲壳"
-"甲子"
-"甲方"
-"甲烷"
-"甲状腺"
-"甲等"
-"甲级"
-"甲醇"
-"甲醛"
-"甲骨"
-"甲骨文"
-"申"
-"申办"
-"申报"
-"申明"
-"申花"
-"申论"
-"申诉"
-"申请"
-"申请人"
-"申请者"
-"申请表"
-"申购"
-"申领"
-"电"
-"电位"
-"电信"
-"电力"
-"电动"
-"电动机"
-"电动汽车"
-"电动车"
-"电厂"
-"电压"
-"电台"
-"电商"
-"电器"
-"电图"
-"电场"
-"电子"
-"电子书"
-"电子产品"
-"电子信息"
-"电子商务"
-"电子客票"
-"电子游戏"
-"电子科技"
-"电子邮件"
-"电容"
-"电工"
-"电影"
-"电影票"
-"电影节"
-"电影院"
-"电报"
-"电控"
-"电教"
-"电机"
-"电极"
-"电梯"
-"电气"
-"电气工程"
-"电池"
-"电流"
-"电源"
-"电热"
-"电热水"
-"电瓶"
-"电磁"
-"电离"
-"电站"
-"电竞"
-"电算"
-"电线"
-"电缆"
-"电网"
-"电能"
-"电脑"
-"电荷"
-"电视"
-"电视剧"
-"电视台"
-"电视机"
-"电视节目"
-"电解"
-"电讯"
-"电话"
-"电话号码"
-"电路"
-"电车"
-"电量"
-"电镀"
-"电阻"
-"电饭"
-"男"
-"男主"
-"男主角"
-"男人"
-"男儿"
-"男友"
-"男同"
-"男士"
-"男女"
-"男子"
-"男孩"
-"男孩子"
-"男性"
-"男方"
-"男朋友"
-"男演员"
-"男生"
-"男盆友"
-"男神"
-"男票"
-"男科"
-"男篮"
-"男装"
-"甸"
-"町"
-"画"
-"画作"
-"画像"
-"画册"
-"画出"
-"画卷"
-"画图"
-"画家"
-"画廊"
-"画报"
-"画法"
-"画画"
-"画笔"
-"画质"
-"画面"
-"画风"
-"甾"
-"畀"
-"畅"
-"畅快"
-"畅游"
-"畅通"
-"畅销"
-"畈"
-"畋"
-"界"
-"界定"
-"界线"
-"界限"
-"界面"
-"畎"
-"畏"
-"畏惧"
-"畔"
-"留"
-"留下"
-"留下来"
-"留住"
-"留出"
-"留在"
-"留存"
-"留学"
-"留学生"
-"留守"
-"留心"
-"留念"
-"留恋"
-"留情"
-"留意"
-"留有"
-"留校"
-"留着"
-"留给"
-"留言"
-"畚"
-"畛"
-"畜"
-"畜牧"
-"畜牧业"
-"畜禽"
-"畢"
-"略"
-"略带"
-"略微"
-"略显"
-"略有"
-"畦"
-"番"
-"番外"
-"番禺"
-"番茄"
-"畫"
-"異"
-"畲"
-"畴"
-"當"
-"畸"
-"畸形"
-"畹"
-"畿"
-"疃"
-"疆"
-"疏"
-"疏导"
-"疏忽"
-"疏散"
-"疏松"
-"疏远"
-"疏通"
-"疑"
-"疑似"
-"疑惑"
-"疑虑"
-"疑问"
-"疑难"
-"疒"
-"疔"
-"疖"
-"疗"
-"疗养"
-"疗效"
-"疗法"
-"疗程"
-"疙"
-"疙瘩"
-"疚"
-"疝"
-"疟"
-"疠"
-"疡"
-"疣"
-"疤"
-"疤痕"
-"疥"
-"疫"
-"疫情"
-"疫病"
-"疫苗"
-"疬"
-"疭"
-"疮"
-"疯"
-"疯子"
-"疯狂"
-"疱"
-"疱疹"
-"疲"
-"疲乏"
-"疲倦"
-"疲劳"
-"疲惫"
-"疲软"
-"疳"
-"疴"
-"疵"
-"疸"
-"疹"
-"疼"
-"疼爱"
-"疼痛"
-"疽"
-"疾"
-"疾患"
-"疾控"
-"疾病"
-"痂"
-"病"
-"病人"
-"病例"
-"病历"
-"病原"
-"病变"
-"病史"
-"病因"
-"病害"
-"病床"
-"病态"
-"病患"
-"病情"
-"病房"
-"病死"
-"病毒"
-"病灶"
-"病理"
-"病理学"
-"病症"
-"病痛"
-"病程"
-"病菌"
-"病虫"
-"病虫害"
-"症"
-"症状"
-"痈"
-"痉"
-"痉挛"
-"痊"
-"痊愈"
-"痍"
-"痒"
-"痔"
-"痔疮"
-"痕"
-"痕迹"
-"痘"
-"痘痘"
-"痛"
-"痛哭"
-"痛心"
-"痛快"
-"痛点"
-"痛经"
-"痛苦"
-"痛风"
-"痞"
-"痢"
-"痣"
-"痤"
-"痤疮"
-"痦"
-"痧"
-"痨"
-"痪"
-"痫"
-"痰"
-"痱"
-"痴"
-"痴呆"
-"痴情"
-"痴迷"
-"痹"
-"痼"
-"痿"
-"瘀"
-"瘁"
-"瘃"
-"瘆"
-"瘊"
-"瘌"
-"瘐"
-"瘘"
-"瘙"
-"瘙痒"
-"瘛"
-"瘟"
-"瘟疫"
-"瘠"
-"瘢"
-"瘤"
-"瘦"
-"瘦下"
-"瘦弱"
-"瘦肉"
-"瘦身"
-"瘩"
-"瘪"
-"瘫"
-"瘫痪"
-"瘰"
-"瘳"
-"瘴"
-"瘵"
-"瘸"
-"瘼"
-"瘾"
-"瘿"
-"癃"
-"癌"
-"癌症"
-"癌细胞"
-"癍"
-"癔"
-"癖"
-"癜"
-"癞"
-"癣"
-"癫"
-"癫痫"
-"癫痫病"
-"癯"
-"癸"
-"発"
-"登"
-"登上"
-"登台"
-"登场"
-"登山"
-"登录"
-"登机"
-"登记"
-"登陆"
-"登高"
-"發"
-"白"
-"白了"
-"白云"
-"白人"
-"白俄罗斯"
-"白兔"
-"白内障"
-"白发"
-"白天"
-"白头"
-"白宫"
-"白富"
-"白居"
-"白居易"
-"白山"
-"白带"
-"白开水"
-"白斑"
-"白族"
-"白日"
-"白术"
-"白沙"
-"白玉"
-"白痴"
-"白癜"
-"白癜风"
-"白白"
-"白皙"
-"白眼"
-"白石"
-"白砂"
-"白糖"
-"白纸"
-"白细胞"
-"白羊"
-"白色"
-"白芍"
-"白花"
-"白莲"
-"白菜"
-"白萝卜"
-"白虎"
-"白血"
-"白血病"
-"白衣"
-"白酒"
-"白醋"
-"白金"
-"白银"
-"白雪"
-"白领"
-"白马"
-"白骨"
-"百事"
-"百八十"
-"百合"
-"百姓"
-"百家"
-"百岁"
-"百川"
-"百度"
-"百强"
-"百态"
-"百搭"
-"百病"
-"百科"
-"百科全书"
-"百般"
-"百花"
-"百草"
-"百计"
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-"的话"
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-"皆是"
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-"税务机关"
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-"税款"
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-"税率"
-"税费"
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-"稔"
-"稗"
-"稚"
-"稚嫩"
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-"穆斯"
-"穆斯林"
-"穆罕默"
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-"穴位"
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-"穷人"
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-"穷游"
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-"空军"
-"空前"
-"空单"
-"空地"
-"空头"
-"空心"
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-"空气污染"
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-"空腹"
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-"空袭"
-"空调"
-"空运"
-"空闲"
-"空间"
-"空降"
-"空隙"
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-"穿刺"
-"穿孔"
-"穿戴"
-"穿插"
-"穿梭"
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-"穿衣服"
-"穿越"
-"穿过"
-"穿透"
-"突"
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-"突厥"
-"突发"
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-"突变"
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-"突如其来"
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-"突破口"
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-"管辖"
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-"精油"
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-"精灵"
-"精炼"
-"精益"
-"精益求精"
-"精盐"
-"精确"
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-"精神分裂"
-"精神文明"
-"精神状态"
-"精神疾病"
-"精神病"
-"精简"
-"精算"
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-"精细化"
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-"糊涂"
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-"糌"
-"糍"
-"糕"
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-"糖尿病"
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-"糖类"
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-"糗"
-"糙"
-"糜"
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-"糯"
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-"经络"
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-"经营者"
-"经营范围"
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-"经验"
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-"结伴"
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-"结合"
-"结婚"
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-"结局"
-"结成"
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-"结核"
-"结盟"
-"结石"
-"结算"
-"结缘"
-"结肠"
-"结膜"
-"结节"
-"结论"
-"结识"
-"结语"
-"绔"
-"绕"
-"绕行"
-"绕过"
-"绗"
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-"绘制"
-"绘图"
-"绘本"
-"绘画"
-"给"
-"给予"
-"给人以"
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-"给出"
-"给力"
-"给我"
-"绚"
-"绚丽"
-"绚烂"
-"绛"
-"络"
-"绝"
-"绝不"
-"绝不会"
-"绝不能"
-"绝世"
-"绝伦"
-"绝佳"
-"绝地"
-"绝大多数"
-"绝大部分"
-"绝妙"
-"绝对"
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-"绝招"
-"绝无"
-"绝望"
-"绝缘"
-"绝育"
-"绝非"
-"绞"
-"绞痛"
-"统"
-"统一"
-"统帅"
-"统战"
-"统治"
-"统治者"
-"统称"
-"统筹"
-"统统"
-"统考"
-"统计"
-"统计学"
-"统计局"
-"统计数据"
-"统领"
-"绠"
-"绡"
-"绢"
-"绣"
-"绣花"
-"绥"
-"绦"
-"继"
-"继任"
-"继发"
-"继承"
-"继承人"
-"继电"
-"继续"
-"继而"
-"绨"
-"绩"
-"绩效"
-"绩点"
-"绪"
-"绫"
-"续"
-"续约"
-"续续"
-"续航"
-"绮"
-"绯"
-"绯闻"
-"绰"
-"绱"
-"绲"
-"绳"
-"绳子"
-"维"
-"维也纳"
-"维亚"
-"维他命"
-"维修"
-"维克"
-"维吾尔"
-"维基"
-"维基百"
-"维多"
-"维多利亚"
-"维奇"
-"维尔"
-"维度"
-"维护"
-"维拉"
-"维持"
-"维斯"
-"维新"
-"维权"
-"维特"
-"维生素"
-"维系"
-"绵"
-"绵延"
-"绵绵"
-"绵阳"
-"绶"
-"绷"
-"绸"
-"绺"
-"绻"
-"综"
-"综上"
-"综上所"
-"综上所述"
-"综合"
-"综合体"
-"综合征"
-"综合性"
-"综合治理"
-"综合症"
-"综艺"
-"综艺节目"
-"绽"
-"绽放"
-"绾"
-"绿"
-"绿化"
-"绿叶"
-"绿地"
-"绿树"
-"绿水"
-"绿洲"
-"绿灯"
-"绿绿"
-"绿色"
-"绿茶"
-"绿豆"
-"缀"
-"缁"
-"缂"
-"缃"
-"缄"
-"缅"
-"缅怀"
-"缅甸"
-"缆"
-"缇"
-"缈"
-"缉"
-"缌"
-"缎"
-"缑"
-"缒"
-"缓"
-"缓冲"
-"缓和"
-"缓存"
-"缓慢"
-"缓缓"
-"缓解"
-"缔"
-"缔约"
-"缔造"
-"缕"
-"编"
-"编为"
-"编入"
-"编写"
-"编制"
-"编剧"
-"编号"
-"编委"
-"编审"
-"编导"
-"编排"
-"编撰"
-"编曲"
-"编码"
-"编程"
-"编纂"
-"编织"
-"编者"
-"编著"
-"编译"
-"编辑"
-"编造"
-"缗"
-"缘"
-"缘分"
-"缘故"
-"缘由"
-"缘起"
-"缙"
-"缚"
-"缛"
-"缜"
-"缝"
-"缝合"
-"缝纫"
-"缝隙"
-"缟"
-"缠"
-"缠绕"
-"缠绵"
-"缡"
-"缢"
-"缣"
-"缤"
-"缤纷"
-"缥"
-"缦"
-"缧"
-"缨"
-"缩"
-"缩写"
-"缩减"
-"缩小"
-"缩影"
-"缩放"
-"缩水"
-"缩短"
-"缩量"
-"缪"
-"缫"
-"缬"
-"缭"
-"缭乱"
-"缭绕"
-"缮"
-"缯"
-"缰"
-"缱"
-"缲"
-"缳"
-"缴"
-"缴存"
-"缴纳"
-"缴费"
-"缵"
-"缶"
-"缸"
-"缺"
-"缺乏"
-"缺口"
-"缺失"
-"缺少"
-"缺席"
-"缺氧"
-"缺水"
-"缺点"
-"缺爱"
-"缺血"
-"缺陷"
-"罂"
-"罄"
-"罅"
-"罐"
-"罐头"
-"网"
-"网上"
-"网卡"
-"网友"
-"网吧"
-"网址"
-"网店"
-"网易"
-"网格"
-"网民"
-"网游"
-"网点"
-"网状"
-"网球"
-"网盘"
-"网站"
-"网红"
-"网约车"
-"网络"
-"网络安全"
-"网络小说"
-"网络平台"
-"网络游戏"
-"网络管理"
-"网络营销"
-"网络通信"
-"网购"
-"网贷"
-"网路"
-"网速"
-"网银"
-"网页"
-"罔"
-"罕"
-"罕见"
-"罗"
-"罗伯"
-"罗伯特"
-"罗兰"
-"罗列"
-"罗宾"
-"罗拉"
-"罗斯"
-"罗曼"
-"罗杰"
-"罗汉"
-"罗湖"
-"罗辑"
-"罗马"
-"罗马尼亚"
-"罘"
-"罚"
-"罚款"
-"罚球"
-"罟"
-"罡"
-"罢"
-"罢不"
-"罢了"
-"罢免"
-"罢工"
-"罨"
-"罩"
-"罪"
-"罪名"
-"罪恶"
-"罪犯"
-"罪行"
-"置"
-"置业"
-"置于"
-"置信"
-"置换"
-"置疑"
-"置身"
-"置身于"
-"罱"
-"署"
-"署名"
-"罴"
-"罹"
-"罽"
-"罾"
-"羁"
-"羁绊"
-"羅"
-"羊"
-"羊毛"
-"羊绒"
-"羊羊"
-"羊肉"
-"羌"
-"美"
-"美丽"
-"美人"
-"美元"
-"美军"
-"美利坚"
-"美剧"
-"美化"
-"美协"
-"美发"
-"美味"
-"美团"
-"美国"
-"美国政府"
-"美国纽约"
-"美女"
-"美好"
-"美好生活"
-"美妆"
-"美妙"
-"美学"
-"美容"
-"美容美发"
-"美容院"
-"美工"
-"美帝"
-"美式"
-"美德"
-"美感"
-"美方"
-"美景"
-"美术"
-"美术学院"
-"美术家"
-"美术馆"
-"美洲"
-"美满"
-"美甲"
-"美男"
-"美白"
-"美的"
-"美美"
-"美联储"
-"美股"
-"美观"
-"美誉"
-"美貌"
-"美酒"
-"美金"
-"美院"
-"美颜"
-"美食"
-"羑"
-"羔"
-"羚"
-"羞"
-"羞愧"
-"羞涩"
-"羞耻"
-"羞辱"
-"羟"
-"羡"
-"羡慕"
-"群"
-"群众"
-"群体"
-"群发"
-"群山"
-"群岛"
-"群星"
-"群落"
-"群里"
-"群雄"
-"羧"
-"義"
-"羯"
-"羰"
-"羲"
-"羸"
-"羹"
-"羼"
-"羽"
-"羽毛"
-"羽毛球"
-"羽状"
-"羽绒"
-"羽绒服"
-"羿"
-"翁"
-"翅"
-"翅膀"
-"翊"
-"翌"
-"翎"
-"習"
-"翔"
-"翕"
-"翘"
-"翚"
-"翟"
-"翠"
-"翠绿"
-"翡"
-"翡翠"
-"翥"
-"翦"
-"翩"
-"翩翩"
-"翮"
-"翰"
-"翰林"
-"翱"
-"翳"
-"翻"
-"翻倍"
-"翻出"
-"翻唱"
-"翻墙"
-"翻天"
-"翻开"
-"翻新"
-"翻滚"
-"翻炒"
-"翻看"
-"翻翻"
-"翻脸"
-"翻译"
-"翻身"
-"翻转"
-"翻过"
-"翻阅"
-"翼"
-"翼翼"
-"耀"
-"耀眼"
-"老"
-"老一辈"
-"老中医"
-"老乡"
-"老人"
-"老人家"
-"老伴"
-"老先"
-"老公"
-"老兵"
-"老化"
-"老去"
-"老友"
-"老君"
-"老和尚"
-"老外"
-"老大"
-"老天"
-"老太"
-"老太太"
-"老夫"
-"老头"
-"老奶奶"
-"老妈"
-"老娘"
-"老婆"
-"老子"
-"老字号"
-"老实"
-"老客户"
-"老家"
-"老将"
-"老少"
-"老师"
-"老干部"
-"老年"
-"老年人"
-"老总"
-"老抽"
-"老挝"
-"老旧"
-"老是"
-"老朋友"
-"老板"
-"老板娘"
-"老爷"
-"老爸"
-"老牌"
-"老王"
-"老百姓"
-"老罗"
-"老老"
-"老老实实"
-"老者"
-"老虎"
-"老街"
-"老鹰"
-"老鼠"
-"老龄"
-"老龄化"
-"考"
-"考上"
-"考了"
-"考入"
-"考前"
-"考勤"
-"考取"
-"考古"
-"考古学"
-"考场"
-"考完"
-"考官"
-"考察"
-"考得"
-"考查"
-"考核"
-"考点"
-"考生"
-"考研"
-"考究"
-"考网"
-"考虑"
-"考证"
-"考评"
-"考试"
-"考试成绩"
-"考过"
-"考量"
-"考题"
-"考验"
-"耄"
-"者"
-"耆"
-"耋"
-"而"
-"而上"
-"而下"
-"而不"
-"而且"
-"而为"
-"而今"
-"而入"
-"而出"
-"而后"
-"而复"
-"而外"
-"而定"
-"而已"
-"而异"
-"而成"
-"而是"
-"而来"
-"而然"
-"而生"
-"而知"
-"而立"
-"而至"
-"而行"
-"而言"
-"而言之"
-"而论"
-"而过"
-"耍"
-"耐"
-"耐久"
-"耐克"
-"耐力"
-"耐劳"
-"耐压"
-"耐受"
-"耐寒"
-"耐心"
-"耐火"
-"耐热"
-"耐用"
-"耐磨"
-"耐腐蚀"
-"耐药"
-"耒"
-"耕"
-"耕作"
-"耕地"
-"耕耘"
-"耖"
-"耗"
-"耗尽"
-"耗时"
-"耗材"
-"耗电"
-"耗费"
-"耘"
-"耙"
-"耜"
-"耠"
-"耢"
-"耥"
-"耦"
-"耦合"
-"耧"
-"耨"
-"耩"
-"耪"
-"耱"
-"耳"
-"耳塞"
-"耳朵"
-"耳机"
-"耳熟"
-"耳环"
-"耳目"
-"耳聋"
-"耳边"
-"耳鸣"
-"耳鼻"
-"耳鼻喉"
-"耶"
-"耶夫"
-"耶律"
-"耶稣"
-"耶鲁"
-"耷"
-"耸"
-"耻"
-"耻辱"
-"耽"
-"耽美"
-"耽误"
-"耿"
-"聂"
-"聃"
-"聆"
-"聆听"
-"聊"
-"聊城"
-"聊天"
-"聊斋"
-"聊聊"
-"聊起"
-"聋"
-"职"
-"职业"
-"职业技能"
-"职业生涯"
-"职业道德"
-"职位"
-"职务"
-"职员"
-"职场"
-"职工"
-"职权"
-"职称"
-"职能"
-"职能部门"
-"职责"
-"职高"
-"聒"
-"联"
-"联保"
-"联储"
-"联军"
-"联动"
-"联合"
-"联合会"
-"联合国"
-"联合国教科文组织"
-"联名"
-"联席"
-"联想"
-"联手"
-"联播"
-"联机"
-"联欢"
-"联盟"
-"联系"
-"联系人"
-"联系方"
-"联系方式"
-"联系电话"
-"联结"
-"联络"
-"联网"
-"联考"
-"联谊"
-"联谊会"
-"联赛"
-"联通"
-"联邦"
-"聖"
-"聘"
-"聘任"
-"聘用"
-"聘请"
-"聚"
-"聚乙烯"
-"聚会"
-"聚合"
-"聚在"
-"聚在一起"
-"聚居"
-"聚氨"
-"聚氨酯"
-"聚焦"
-"聚酯"
-"聚集"
-"聚餐"
-"聞"
-"聩"
-"聪"
-"聪慧"
-"聪明"
-"聪明人"
-"聯"
-"聱"
-"聲"
-"聽"
-"聿"
-"肃"
-"肄"
-"肆"
-"肆意"
-"肆无忌惮"
-"肇"
-"肇事"
-"肇庆"
-"肉"
-"肉丝"
-"肉体"
-"肉桂"
-"肉片"
-"肉眼"
-"肉类"
-"肉质"
-"肉食"
-"肉馅"
-"肋"
-"肋骨"
-"肌"
-"肌瘤"
-"肌肉"
-"肌肤"
-"肓"
-"肖"
-"肖像"
-"肘"
-"肚"
-"肚子"
-"肚皮"
-"肚脐"
-"肛"
-"肛肠"
-"肛门"
-"肝"
-"肝功能"
-"肝气"
-"肝火"
-"肝炎"
-"肝病"
-"肝癌"
-"肝硬"
-"肝硬化"
-"肝经"
-"肝肾"
-"肝胆"
-"肝脏"
-"肟"
-"肠"
-"肠胃"
-"肠道"
-"股"
-"股东"
-"股东大会"
-"股价"
-"股份"
-"股份制"
-"股市"
-"股指"
-"股本"
-"股权"
-"股民"
-"股票"
-"股票市场"
-"股骨"
-"股骨头"
-"肢"
-"肢体"
-"肤"
-"肤浅"
-"肤色"
-"肤质"
-"肥"
-"肥厚"
-"肥大"
-"肥料"
-"肥沃"
-"肥皂"
-"肥肉"
-"肥胖"
-"肩"
-"肩上"
-"肩膀"
-"肩负"
-"肩部"
-"肪"
-"肫"
-"肮"
-"肯"
-"肯定"
-"肯尼迪"
-"肯德"
-"肯德基"
-"肱"
-"育"
-"育人"
-"育儿"
-"育才"
-"育种"
-"肴"
-"肺"
-"肺气"
-"肺炎"
-"肺癌"
-"肺结核"
-"肺腑"
-"肺部"
-"肼"
-"肽"
-"肾"
-"肾上"
-"肾上腺"
-"肾功能"
-"肾小"
-"肾气"
-"肾炎"
-"肾病"
-"肾脏"
-"肾虚"
-"肿"
-"肿块"
-"肿大"
-"肿痛"
-"肿瘤"
-"肿胀"
-"胀"
-"胀痛"
-"胁"
-"胂"
-"胃"
-"胃口"
-"胃溃疡"
-"胃炎"
-"胃病"
-"胃痛"
-"胃癌"
-"胃肠"
-"胃肠道"
-"胃部"
-"胃酸"
-"胄"
-"胆"
-"胆囊"
-"胆固醇"
-"胆子"
-"胆小"
-"胆怯"
-"胆汁"
-"胆碱"
-"胆结石"
-"背"
-"背上"
-"背书"
-"背光"
-"背包"
-"背叛"
-"背后"
-"背影"
-"背心"
-"背景"
-"背着"
-"背离"
-"背诵"
-"背负"
-"背部"
-"背锅"
-"背靠"
-"背面"
-"背驰"
-"胍"
-"胎"
-"胎儿"
-"胎盘"
-"胖"
-"胖子"
-"胖胖"
-"胗"
-"胙"
-"胚"
-"胚胎"
-"胛"
-"胜"
-"胜任"
-"胜出"
-"胜利"
-"胜地"
-"胜率"
-"胜负"
-"胜过"
-"胝"
-"胞"
-"胞胎"
-"胡"
-"胡乱"
-"胡同"
-"胡子"
-"胡椒"
-"胡椒粉"
-"胡歌"
-"胡萝卜"
-"胡萝卜素"
-"胡说"
-"胡适"
-"胡锦"
-"胡锦涛"
-"胤"
-"胥"
-"胧"
-"胨"
-"胪"
-"胫"
-"胬"
-"胭"
-"胯"
-"胰"
-"胰岛"
-"胰岛素"
-"胰腺"
-"胱"
-"胳"
-"胳膊"
-"胴"
-"胶"
-"胶东"
-"胶体"
-"胶原"
-"胶原蛋白"
-"胶囊"
-"胶带"
-"胶水"
-"胶片"
-"胶版"
-"胶粘"
-"胶质"
-"胸"
-"胸前"
-"胸口"
-"胸大"
-"胸怀"
-"胸腔"
-"胸部"
-"胸闷"
-"胺"
-"胼"
-"能"
-"能为"
-"能力"
-"能动"
-"能否"
-"能够"
-"能干"
-"能手"
-"能源"
-"能耐"
-"能耗"
-"能量"
-"脂"
-"脂肪"
-"脂肪肝"
-"脂肪酸"
-"脆"
-"脆弱"
-"脉"
-"脉冲"
-"脉搏"
-"脉络"
-"脊"
-"脊柱"
-"脊椎"
-"脊髓"
-"脍"
-"脍炙"
-"脏"
-"脏器"
-"脏腑"
-"脐"
-"脑"
-"脑中"
-"脑力"
-"脑子"
-"脑子里"
-"脑残"
-"脑洞"
-"脑海"
-"脑海中"
-"脑筋"
-"脑细胞"
-"脑膜"
-"脑血管"
-"脑补"
-"脑袋"
-"脑部"
-"脒"
-"脓"
-"脔"
-"脖"
-"脖子"
-"脘"
-"脚"
-"脚下"
-"脚印"
-"脚尖"
-"脚底"
-"脚本"
-"脚步"
-"脚气"
-"脚趾"
-"脚跟"
-"脚踏"
-"脚踏实地"
-"脚踝"
-"脬"
-"脯"
-"脱"
-"脱俗"
-"脱发"
-"脱口"
-"脱欧"
-"脱毛"
-"脱水"
-"脱硫"
-"脱离"
-"脱胎"
-"脱落"
-"脱衣"
-"脱贫"
-"脱颖"
-"脱颖而出"
-"脲"
-"脸"
-"脸上"
-"脸型"
-"脸庞"
-"脸皮"
-"脸红"
-"脸色"
-"脸蛋"
-"脸部"
-"脸颊"
-"脾"
-"脾气"
-"脾胃"
-"脾虚"
-"腆"
-"腈"
-"腊"
-"腊八"
-"腊月"
-"腊肉"
-"腋"
-"腋下"
-"腋臭"
-"腌"
-"腌制"
-"腐"
-"腐女"
-"腐朽"
-"腐烂"
-"腐蚀"
-"腐蚀性"
-"腐败"
-"腑"
-"腓"
-"腔"
-"腕"
-"腚"
-"腠"
-"腥"
-"腥味"
-"腦"
-"腧"
-"腩"
-"腭"
-"腮"
-"腰"
-"腰带"
-"腰椎"
-"腰椎间盘"
-"腰痛"
-"腰线"
-"腰部"
-"腰酸"
-"腱"
-"腴"
-"腹"
-"腹中"
-"腹泻"
-"腹痛"
-"腹肌"
-"腹胀"
-"腹腔"
-"腹膜"
-"腹部"
-"腺"
-"腻"
-"腼"
-"腾"
-"腾腾"
-"腾讯"
-"腾飞"
-"腿"
-"腿部"
-"膀"
-"膀胱"
-"膂"
-"膈"
-"膊"
-"膏"
-"膑"
-"膘"
-"膙"
-"膛"
-"膜"
-"膜炎"
-"膝"
-"膝关"
-"膝关节"
-"膝盖"
-"膦"
-"膨"
-"膨胀"
-"膳"
-"膳食"
-"膺"
-"膻"
-"臀"
-"臀部"
-"臂"
-"臃"
-"臆"
-"臉"
-"臊"
-"臌"
-"臜"
-"臣"
-"臧"
-"自"
-"自个"
-"自主"
-"自习"
-"自从"
-"自以为"
-"自以为是"
-"自传"
-"自体"
-"自作"
-"自保"
-"自信"
-"自信心"
-"自创"
-"自制"
-"自动"
-"自动化"
-"自助"
-"自助餐"
-"自卑"
-"自卫"
-"自发"
-"自古"
-"自古以来"
-"自在"
-"自大"
-"自如"
-"自娱"
-"自学"
-"自定义"
-"自家"
-"自尊"
-"自尊心"
-"自己"
-"自已"
-"自带"
-"自干"
-"自幼"
-"自弃"
-"自强"
-"自律"
-"自得"
-"自性"
-"自恋"
-"自愈"
-"自愿"
-"自慰"
-"自成"
-"自我"
-"自我介绍"
-"自拍"
-"自拔"
-"自控"
-"自救"
-"自暴"
-"自有"
-"自杀"
-"自来"
-"自来水"
-"自查"
-"自欺"
-"自此"
-"自治"
-"自治区"
-"自治县"
-"自治州"
-"自测"
-"自然"
-"自然人"
-"自然保护区"
-"自然村"
-"自然灾害"
-"自然环境"
-"自然界"
-"自然科学"
-"自然而然"
-"自然资源"
-"自理"
-"自由"
-"自由基"
-"自由职业"
-"自由自在"
-"自由贸易"
-"自知"
-"自私"
-"自称"
-"自立"
-"自答"
-"自考"
-"自营"
-"自行"
-"自行车"
-"自觉"
-"自认"
-"自诩"
-"自语"
-"自豪"
-"自负"
-"自责"
-"自贸"
-"自贸区"
-"自费"
-"自足"
-"自身"
-"自转"
-"自达"
-"自选"
-"自重"
-"自闭"
-"自闭症"
-"自问"
-"自驾"
-"自驾游"
-"臬"
-"臭"
-"臭味"
-"臭氧"
-"至"
-"至上"
-"至于"
-"至今"
-"至关"
-"至关重要"
-"至尊"
-"至少"
-"至极"
-"至此"
-"至终"
-"致"
-"致使"
-"致力"
-"致力于"
-"致命"
-"致富"
-"致敬"
-"致歉"
-"致死"
-"致电"
-"致病"
-"致癌"
-"致谢"
-"致辞"
-"致远"
-"臺"
-"臻"
-"臼"
-"臾"
-"舀"
-"舂"
-"舄"
-"舅"
-"舅舅"
-"舆"
-"舆论"
-"與"
-"興"
-"舉"
-"舌"
-"舌头"
-"舌尖"
-"舌苔"
-"舍"
-"舍不得"
-"舍利"
-"舍弃"
-"舍得"
-"舐"
-"舒"
-"舒展"
-"舒心"
-"舒服"
-"舒畅"
-"舒缓"
-"舒适"
-"舒适度"
-"舒适性"
-"舔"
-"舛"
-"舜"
-"舞"
-"舞台"
-"舞弊"
-"舞曲"
-"舞蹈"
-"舟"
-"舟山"
-"舢"
-"航"
-"航天"
-"航母"
-"航海"
-"航班"
-"航空"
-"航空公司"
-"航空航天"
-"航线"
-"航行"
-"航运"
-"航道"
-"舫"
-"般"
-"般的"
-"般若"
-"舰"
-"舰艇"
-"舰队"
-"舱"
-"舴"
-"舵"
-"舶"
-"舷"
-"舸"
-"船"
-"船上"
-"船只"
-"船舶"
-"船长"
-"舻"
-"艄"
-"艇"
-"艋"
-"艘"
-"艟"
-"艨"
-"艮"
-"良"
-"良多"
-"良好"
-"良心"
-"良性"
-"良机"
-"良知"
-"良种"
-"良药"
-"艰"
-"艰巨"
-"艰苦"
-"艰辛"
-"艰难"
-"色"
-"色彩"
-"色情"
-"色拉"
-"色斑"
-"色泽"
-"色狼"
-"色系"
-"色素"
-"色色"
-"色调"
-"色谱"
-"艳"
-"艳丽"
-"艹"
-"艺"
-"艺人"
-"艺兴"
-"艺术"
-"艺术品"
-"艺术家"
-"艺术界"
-"艺术类"
-"艺术节"
-"艺考"
-"艺谋"
-"艽"
-"艾"
-"艾伦"
-"艾尔"
-"艾斯"
-"艾滋"
-"艾滋病"
-"艾灸"
-"艾玛"
-"艾米"
-"艿"
-"节"
-"节俭"
-"节假"
-"节假日"
-"节制"
-"节后"
-"节奏"
-"节度"
-"节拍"
-"节日"
-"节气"
-"节水"
-"节点"
-"节目"
-"节省"
-"节约"
-"节能"
-"节节"
-"节课"
-"节选"
-"节食"
-"芄"
-"芈"
-"芊"
-"芋"
-"芋头"
-"芍"
-"芎"
-"芑"
-"芒"
-"芒果"
-"芗"
-"芙"
-"芙蓉"
-"芜"
-"芜湖"
-"芝"
-"芝加哥"
-"芝士"
-"芝麻"
-"芟"
-"芡"
-"芥"
-"芦"
-"芦笋"
-"芦苇"
-"芦荟"
-"芨"
-"芩"
-"芪"
-"芫"
-"芬"
-"芬兰"
-"芬芳"
-"芭"
-"芭比"
-"芭蕾"
-"芭蕾舞"
-"芮"
-"芯"
-"芯片"
-"花"
-"花儿"
-"花八"
-"花卉"
-"花园"
-"花岗"
-"花岗岩"
-"花开"
-"花心"
-"花期"
-"花木"
-"花朵"
-"花果"
-"花柱"
-"花样"
-"花椒"
-"花海"
-"花瓣"
-"花瓶"
-"花生"
-"花生油"
-"花生米"
-"花盆"
-"花粉"
-"花纹"
-"花肉"
-"花色"
-"花花"
-"花草"
-"花药"
-"花萼"
-"花费"
-"花都"
-"花钱"
-"花香"
-"花鸟"
-"花鸟画"
-"芳"
-"芳草"
-"芳香"
-"芷"
-"芸"
-"芹"
-"芹菜"
-"芼"
-"芽"
-"芾"
-"苁"
-"苄"
-"苇"
-"苈"
-"苋"
-"苌"
-"苍"
-"苍白"
-"苍蝇"
-"苎"
-"苏"
-"苏东坡"
-"苏丹"
-"苏区"
-"苏南"
-"苏宁"
-"苏州"
-"苏州市"
-"苏打"
-"苏格兰"
-"苏联"
-"苏菲"
-"苏轼"
-"苏醒"
-"苏黎世"
-"苑"
-"苒"
-"苓"
-"苔"
-"苕"
-"苗"
-"苗族"
-"苗木"
-"苗条"
-"苘"
-"苛"
-"苛刻"
-"苜"
-"苞"
-"苟"
-"苟且"
-"苡"
-"苣"
-"苤"
-"若"
-"若干"
-"若惊"
-"若有"
-"若能"
-"苦"
-"苦心"
-"苦恼"
-"苦涩"
-"苦瓜"
-"苦练"
-"苦苦"
-"苦难"
-"苫"
-"苯"
-"英"
-"英伦"
-"英俊"
-"英勇"
-"英国"
-"英国人"
-"英国伦敦"
-"英国皇家"
-"英寸"
-"英尺"
-"英才"
-"英文"
-"英文名"
-"英格兰"
-"英汉"
-"英特尔"
-"英美"
-"英语"
-"英超"
-"英里"
-"英镑"
-"英雄"
-"苴"
-"苷"
-"苹"
-"苹果"
-"苹果公司"
-"苻"
-"茀"
-"茁"
-"茁壮"
-"茂"
-"茂名"
-"茂密"
-"茂盛"
-"范"
-"范例"
-"范儿"
-"范围"
-"范式"
-"范文"
-"范畴"
-"茄"
-"茄子"
-"茅"
-"茅台"
-"茅台酒"
-"茆"
-"茉"
-"茉莉"
-"茌"
-"茎"
-"茏"
-"茑"
-"茓"
-"茔"
-"茕"
-"茗"
-"茚"
-"茜"
-"茧"
-"茨"
-"茫"
-"茫然"
-"茫茫"
-"茬"
-"茭"
-"茯"
-"茯苓"
-"茱"
-"茴"
-"茴香"
-"茵"
-"茶"
-"茶几"
-"茶匙"
-"茶叶"
-"茶园"
-"茶树"
-"茶水"
-"茶艺"
-"茸"
-"茹"
-"茼"
-"荀"
-"荃"
-"荆"
-"荆州"
-"荆棘"
-"荆门"
-"荇"
-"草"
-"草丛"
-"草书"
-"草原"
-"草地"
-"草坪"
-"草木"
-"草本"
-"草根"
-"草案"
-"草泥"
-"草稿"
-"草药"
-"草莓"
-"荏"
-"荐"
-"荑"
-"荒"
-"荒凉"
-"荒唐"
-"荒地"
-"荒山"
-"荒废"
-"荒漠"
-"荒诞"
-"荒谬"
-"荔"
-"荔枝"
-"荚"
-"荜"
-"荞"
-"荞麦"
-"荟"
-"荟萃"
-"荠"
-"荡"
-"荡漾"
-"荡荡"
-"荣"
-"荣华"
-"荣威"
-"荣幸"
-"荣耀"
-"荣获"
-"荣誉"
-"荣誉称号"
-"荤"
-"荥"
-"荦"
-"荧"
-"荧光"
-"荨"
-"荨麻疹"
-"荩"
-"荪"
-"荫"
-"荬"
-"荮"
-"药"
-"药业"
-"药丸"
-"药剂"
-"药品"
-"药学"
-"药师"
-"药店"
-"药性"
-"药房"
-"药效"
-"药方"
-"药材"
-"药水"
-"药物"
-"药理"
-"药用"
-"荷"
-"荷兰"
-"荷叶"
-"荷尔"
-"荷尔蒙"
-"荷花"
-"荸"
-"荻"
-"荼"
-"荽"
-"莅"
-"莆"
-"莆田"
-"莉"
-"莉亚"
-"莉莉"
-"莎"
-"莎士"
-"莒"
-"莓"
-"莘"
-"莛"
-"莜"
-"莞"
-"莠"
-"莨"
-"莩"
-"莪"
-"莫"
-"莫及"
-"莫名"
-"莫名其妙"
-"莫大"
-"莫扎"
-"莫斯科"
-"莫测"
-"莫过于"
-"莱"
-"莱坞"
-"莱德"
-"莱斯"
-"莱昂"
-"莱特"
-"莱茵"
-"莲"
-"莲子"
-"莲花"
-"莲藕"
-"莳"
-"莴"
-"获"
-"获利"
-"获取"
-"获奖"
-"获得"
-"获得成功"
-"获得者"
-"获悉"
-"获批"
-"获益"
-"获省"
-"获胜"
-"莸"
-"莹"
-"莺"
-"莼"
-"莽"
-"菀"
-"菁"
-"菅"
-"菇"
-"菊"
-"菊花"
-"菌"
-"菌丝"
-"菏"
-"菏泽"
-"菔"
-"菖"
-"菘"
-"菜"
-"菜单"
-"菜品"
-"菜市场"
-"菜肴"
-"菜谱"
-"菜鸟"
-"菟"
-"菠"
-"菠菜"
-"菠萝"
-"菡"
-"菩"
-"菩提"
-"菩萨"
-"菪"
-"華"
-"菰"
-"菱"
-"菱形"
-"菲"
-"菲利普"
-"菲尔"
-"菲律宾"
-"菹"
-"菽"
-"萁"
-"萃"
-"萃取"
-"萄"
-"萋"
-"萌"
-"萌芽"
-"萌萌"
-"萍"
-"萎"
-"萎缩"
-"萏"
-"萑"
-"萘"
-"萜"
-"萝"
-"萝卜"
-"萝莉"
-"萤"
-"营"
-"营业"
-"营业执照"
-"营业税"
-"营业部"
-"营业额"
-"营养"
-"营养不良"
-"营养价值"
-"营养学"
-"营养成分"
-"营养物质"
-"营养素"
-"营利"
-"营口"
-"营商"
-"营地"
-"营收"
-"营救"
-"营运"
-"营造"
-"营销"
-"萦"
-"萧"
-"萧山"
-"萧条"
-"萨"
-"萨克"
-"萨尔"
-"萨拉"
-"萨摩"
-"萨斯"
-"萨满"
-"萬"
-"萱"
-"萸"
-"萼"
-"落"
-"落下"
-"落入"
-"落到"
-"落到实处"
-"落叶"
-"落后"
-"落地"
-"落实"
-"落寞"
-"落差"
-"落幕"
-"落成"
-"落户"
-"落日"
-"落水"
-"落泪"
-"落脚"
-"落花"
-"落落"
-"落魄"
-"葆"
-"葉"
-"葑"
-"著"
-"著作"
-"著作权"
-"著名"
-"著述"
-"葚"
-"葛"
-"葛根"
-"葡"
-"葡萄"
-"葡萄干"
-"葡萄牙"
-"葡萄糖"
-"葡萄酒"
-"董"
-"董事"
-"董事会"
-"董事长"
-"董卓"
-"葩"
-"葫"
-"葫芦"
-"葬"
-"葭"
-"葱"
-"葱姜"
-"葱白"
-"葱花"
-"葳"
-"葵"
-"葵花"
-"葶"
-"葸"
-"葺"
-"蒂"
-"蒂斯"
-"蒋"
-"蒋介"
-"蒋介石"
-"蒌"
-"蒗"
-"蒙"
-"蒙古"
-"蒙古人"
-"蒙古族"
-"蒙德"
-"蒙特"
-"蒙蒙"
-"蒜"
-"蒜末"
-"蒟"
-"蒡"
-"蒯"
-"蒲"
-"蒲公英"
-"蒴"
-"蒸"
-"蒸发"
-"蒸气"
-"蒸汽"
-"蒸熟"
-"蒸馏"
-"蒹"
-"蒺"
-"蒽"
-"蒿"
-"蓁"
-"蓄"
-"蓄势"
-"蓄水"
-"蓄电"
-"蓄电池"
-"蓄积"
-"蓉"
-"蓊"
-"蓍"
-"蓐"
-"蓑"
-"蓓"
-"蓖"
-"蓝"
-"蓝光"
-"蓝图"
-"蓝天"
-"蓝牙"
-"蓝筹"
-"蓝筹股"
-"蓝色"
-"蓝莓"
-"蓟"
-"蓠"
-"蓥"
-"蓦"
-"蓬"
-"蓬勃"
-"蓬勃发展"
-"蓬松"
-"蓬莱"
-"蓼"
-"蓿"
-"蔑"
-"蔓"
-"蔓延"
-"蔗"
-"蔚"
-"蔚来"
-"蔟"
-"蔡"
-"蔫"
-"蔬"
-"蔬果"
-"蔬菜"
-"蔷"
-"蔷薇"
-"蔸"
-"蔺"
-"蔻"
-"蔼"
-"蔽"
-"蕃"
-"蕈"
-"蕉"
-"蕊"
-"蕖"
-"蕙"
-"蕞"
-"蕤"
-"蕨"
-"蕲"
-"蕴"
-"蕴含"
-"蕴藏"
-"蕺"
-"蕻"
-"蕾"
-"蕾丝"
-"薄"
-"薄弱"
-"薄片"
-"薄膜"
-"薄荷"
-"薄薄"
-"薄薄的"
-"薅"
-"薇"
-"薏"
-"薏米"
-"薛"
-"薛之谦"
-"薜"
-"薤"
-"薨"
-"薪"
-"薪水"
-"薪资"
-"薪酬"
-"薪金"
-"薪阶"
-"薮"
-"薯"
-"薰"
-"薰衣"
-"薰衣草"
-"薷"
-"薹"
-"藁"
-"藉"
-"藏"
-"藏书"
-"藏传"
-"藏品"
-"藏族"
-"藐"
-"藓"
-"藕"
-"藜"
-"藝"
-"藠"
-"藤"
-"藤原"
-"藩"
-"藻"
-"藻类"
-"藿"
-"蘅"
-"蘑"
-"蘑菇"
-"蘖"
-"蘧"
-"蘩"
-"蘸"
-"虎"
-"虎牙"
-"虏"
-"虐"
-"虐待"
-"虐狗"
-"虑"
-"虔"
-"虔诚"
-"處"
-"虚"
-"虚伪"
-"虚假"
-"虚实"
-"虚寒"
-"虚幻"
-"虚弱"
-"虚心"
-"虚拟"
-"虚拟现实"
-"虚无"
-"虚构"
-"虚空"
-"虚荣"
-"虞"
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-"褶"
-"褶皱"
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-"襄阳"
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-"襦"
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-"西侧"
-"西兰花"
-"西北"
-"西北部"
-"西区"
-"西医"
-"西南"
-"西双版纳"
-"西周"
-"西哥"
-"西域"
-"西夏"
-"西宁"
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-"西安交通"
-"西安市"
-"西山"
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-"西汉"
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-"西湖"
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-"西瓜"
-"西甲"
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-"西蒙"
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-"西路"
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-"西门子"
-"西雅图"
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-"语文"
-"语文课"
-"语气"
-"语法"
-"语种"
-"语言"
-"语言学"
-"语言文字"
-"语言表达"
-"语调"
-"语音"
-"诮"
-"误"
-"误以"
-"误以为"
-"误会"
-"误区"
-"误导"
-"误差"
-"误解"
-"误认"
-"误诊"
-"诰"
-"诱"
-"诱人"
-"诱发"
-"诱因"
-"诱导"
-"诱惑"
-"诲"
-"诳"
-"说"
-"说不"
-"说不上"
-"说不出"
-"说不定"
-"说不清"
-"说了"
-"说了算"
-"说些"
-"说出"
-"说到"
-"说到底"
-"说卡"
-"说句"
-"说唱"
-"说好"
-"说完"
-"说实话"
-"说明"
-"说明书"
-"说是"
-"说服"
-"说服力"
-"说来"
-"说法"
-"说白"
-"说白了"
-"说话"
-"说谎"
-"说起"
-"说道"
-"诵"
-"诵读"
-"诶"
-"请"
-"请假"
-"请勿"
-"请客"
-"请教"
-"请来"
-"请求"
-"请示"
-"请问"
-"诸"
-"诸位"
-"诸佛"
-"诸侯"
-"诸君"
-"诸多"
-"诸如"
-"诸如此"
-"诸法"
-"诸神"
-"诸葛"
-"诸葛亮"
-"诹"
-"诺"
-"诺基"
-"诺基亚"
-"诺夫"
-"诺曼"
-"诺言"
-"诺贝尔"
-"诺贝尔奖"
-"读"
-"读书"
-"读写"
-"读出"
-"读博"
-"读卡"
-"读取"
-"读完"
-"读懂"
-"读物"
-"读者"
-"读读"
-"读过"
-"读音"
-"诼"
-"诽"
-"诽谤"
-"课"
-"课上"
-"课件"
-"课余"
-"课后"
-"课堂"
-"课堂教学"
-"课外"
-"课文"
-"课时"
-"课本"
-"课标"
-"课程"
-"课间"
-"课题"
-"诿"
-"谀"
-"谁"
-"谁家"
-"谁知"
-"谁谁"
-"谂"
-"调"
-"调侃"
-"调入"
-"调养"
-"调制"
-"调剂"
-"调动"
-"调匀"
-"调味"
-"调味品"
-"调味料"
-"调和"
-"调度"
-"调戏"
-"调换"
-"调控"
-"调整"
-"调料"
-"调查"
-"调理"
-"调用"
-"调皮"
-"调研"
-"调色"
-"调节"
-"调解"
-"调试"
-"调速"
-"调配"
-"调频"
-"谄"
-"谅"
-"谅解"
-"谆"
-"谇"
-"谈"
-"谈不上"
-"谈判"
-"谈到"
-"谈及"
-"谈吐"
-"谈心"
-"谈恋"
-"谈恋爱"
-"谈论"
-"谈话"
-"谈谈"
-"谈起"
-"谈过"
-"谊"
-"谋"
-"谋划"
-"谋取"
-"谋杀"
-"谋求"
-"谋生"
-"谋略"
-"谌"
-"谍"
-"谎"
-"谎言"
-"谏"
-"谐"
-"谐振"
-"谐波"
-"谑"
-"谒"
-"谓"
-"谓之"
-"谓语"
-"谔"
-"谕"
-"谖"
-"谗"
-"谙"
-"谚"
-"谛"
-"谜"
-"谝"
-"谟"
-"谠"
-"谡"
-"谢"
-"谢绝"
-"谢谢"
-"谣"
-"谣言"
-"谤"
-"谥"
-"谦"
-"谦虚"
-"谦逊"
-"谧"
-"谨"
-"谨慎"
-"谨防"
-"谩"
-"谩骂"
-"谪"
-"谬"
-"谭"
-"谮"
-"谯"
-"谰"
-"谱"
-"谱写"
-"谲"
-"谳"
-"谴"
-"谴责"
-"谵"
-"谶"
-"谷"
-"谷子"
-"谷歌"
-"谷物"
-"豁"
-"豁然"
-"豁达"
-"豆"
-"豆制品"
-"豆子"
-"豆沙"
-"豆浆"
-"豆瓣"
-"豆类"
-"豆腐"
-"豆芽"
-"豆角"
-"豆豆"
-"豇"
-"豉"
-"豌"
-"豌豆"
-"豕"
-"豚"
-"象"
-"象形"
-"象征"
-"象棋"
-"象牙"
-"豢"
-"豨"
-"豪"
-"豪华"
-"豪宅"
-"豪情"
-"豪放"
-"豪杰"
-"豪车"
-"豪迈"
-"豪门"
-"豫"
-"豳"
-"豸"
-"豹"
-"豺"
-"貂"
-"貅"
-"貉"
-"貊"
-"貌"
-"貌似"
-"貌美"
-"貔"
-"貘"
-"貝"
-"負"
-"貴"
-"買"
-"費"
-"資"
-"質"
-"贝"
-"贝克"
-"贝壳"
-"贝尔"
-"贝拉"
-"贝斯"
-"贞"
-"贞观"
-"负"
-"负债"
-"负担"
-"负有"
-"负离"
-"负罪"
-"负荷"
-"负责"
-"负责人"
-"负责同志"
-"负载"
-"负重"
-"负面"
-"负面影响"
-"贡"
-"贡献"
-"财"
-"财产"
-"财会"
-"财力"
-"财务"
-"财务管理"
-"财团"
-"财富"
-"财报"
-"财政"
-"财政局"
-"财政收入"
-"财政部"
-"财星"
-"财源"
-"财物"
-"财神"
-"财税"
-"财经"
-"财经大学"
-"财运"
-"责"
-"责令"
-"责任"
-"责任人"
-"责任制"
-"责任心"
-"责任感"
-"责备"
-"责怪"
-"贤"
-"败"
-"败坏"
-"账"
-"账单"
-"账号"
-"账户"
-"账款"
-"账面"
-"货"
-"货品"
-"货币"
-"货币政策"
-"货架"
-"货款"
-"货源"
-"货物"
-"货车"
-"货运"
-"质"
-"质保"
-"质地"
-"质感"
-"质押"
-"质朴"
-"质检"
-"质疑"
-"质量"
-"质量保证"
-"质问"
-"贩"
-"贩卖"
-"贪"
-"贪图"
-"贪婪"
-"贪官"
-"贪心"
-"贪污"
-"贫"
-"贫困"
-"贫困人口"
-"贫困地区"
-"贫困户"
-"贫困村"
-"贫富"
-"贫民"
-"贫穷"
-"贫苦"
-"贫血"
-"贬"
-"贬义"
-"贬低"
-"贬值"
-"购"
-"购买"
-"购房"
-"购房者"
-"购物"
-"购物中心"
-"购票"
-"购置"
-"购车"
-"贮"
-"贮存"
-"贮藏"
-"贯"
-"贯彻"
-"贯彻执行"
-"贯彻落实"
-"贯穿"
-"贯通"
-"贰"
-"贱"
-"贲"
-"贳"
-"贴"
-"贴合"
-"贴图"
-"贴士"
-"贴心"
-"贴着"
-"贴纸"
-"贴身"
-"贴近"
-"贵"
-"贵人"
-"贵在"
-"贵妃"
-"贵宾"
-"贵州"
-"贵州省"
-"贵族"
-"贵重"
-"贵金属"
-"贵阳"
-"贶"
-"贷"
-"贷款"
-"贸"
-"贸市场"
-"贸易"
-"费"
-"费力"
-"费劲"
-"费城"
-"费尔"
-"费率"
-"费用"
-"贺"
-"贻"
-"贼"
-"贽"
-"贾"
-"贿"
-"贿赂"
-"赁"
-"赂"
-"赃"
-"资"
-"资产"
-"资产重组"
-"资产阶级"
-"资企业"
-"资助"
-"资料"
-"资本"
-"资本主义"
-"资格"
-"资格证"
-"资格证书"
-"资治通"
-"资深"
-"资源"
-"资源优势"
-"资源共享"
-"资源管理"
-"资源配置"
-"资管"
-"资讯"
-"资质"
-"资费"
-"资金"
-"赅"
-"赇"
-"赈"
-"赉"
-"赊"
-"赋"
-"赋予"
-"赋能"
-"赌"
-"赌博"
-"赌场"
-"赍"
-"赎"
-"赎回"
-"赏"
-"赏析"
-"赏花"
-"赏识"
-"赐"
-"赐予"
-"赓"
-"赔"
-"赔付"
-"赔偿"
-"赔钱"
-"赕"
-"赖"
-"赖以"
-"赘"
-"赘肉"
-"赙"
-"赚"
-"赚取"
-"赚点"
-"赚钱"
-"赛"
-"赛事"
-"赛前"
-"赛区"
-"赛后"
-"赛场"
-"赛季"
-"赛车"
-"赛道"
-"赛马"
-"赜"
-"赝"
-"赞"
-"赞助"
-"赞叹"
-"赞同"
-"赞成"
-"赞扬"
-"赞美"
-"赞誉"
-"赞赏"
-"赟"
-"赠"
-"赠与"
-"赠送"
-"赡"
-"赡养"
-"赢"
-"赢利"
-"赢家"
-"赢得"
-"赣"
-"赣州"
-"赤"
-"赤壁"
-"赤字"
-"赤峰"
-"赤裸"
-"赤道"
-"赦"
-"赧"
-"赫"
-"赫尔"
-"赫赫"
-"赭"
-"走"
-"走上"
-"走低"
-"走入"
-"走出"
-"走到"
-"走动"
-"走势"
-"走向"
-"走廊"
-"走开"
-"走弯"
-"走强"
-"走得"
-"走心"
-"走来"
-"走神"
-"走私"
-"走红"
-"走访"
-"走走"
-"走路"
-"走过"
-"走近"
-"走进"
-"走遍"
-"走高"
-"赳"
-"赴"
-"赴美"
-"赵"
-"赵云"
-"赵国"
-"赵薇"
-"赶"
-"赶上"
-"赶不上"
-"赶到"
-"赶回"
-"赶往"
-"赶快"
-"赶来"
-"赶紧"
-"赶走"
-"赶赴"
-"起"
-"起义"
-"起伏"
-"起初"
-"起到"
-"起动"
-"起因"
-"起头"
-"起始"
-"起家"
-"起居"
-"起床"
-"起来"
-"起步"
-"起源"
-"起源于"
-"起火"
-"起点"
-"起着"
-"起码"
-"起舞"
-"起草"
-"起诉"
-"起跑"
-"起跑线"
-"起身"
-"起重"
-"起重机"
-"起锅"
-"起飞"
-"趁"
-"趁早"
-"趁机"
-"趁热"
-"趁着"
-"趄"
-"超"
-"超乎"
-"超人"
-"超低"
-"超值"
-"超凡"
-"超出"
-"超前"
-"超声"
-"超声波"
-"超大"
-"超导"
-"超市"
-"超强"
-"超标"
-"超级"
-"超群"
-"超薄"
-"超越"
-"超跌"
-"超车"
-"超载"
-"超过"
-"超额"
-"超高"
-"越"
-"越冬"
-"越剧"
-"越南"
-"越发"
-"越多"
-"越多越"
-"越大"
-"越好"
-"越小"
-"越是"
-"越来"
-"越来越"
-"越来越少"
-"越狱"
-"越过"
-"越远"
-"越野"
-"越野车"
-"越长"
-"越高"
-"趋"
-"趋于"
-"趋势"
-"趋向"
-"趑"
-"趔"
-"趟"
-"趣"
-"趣事"
-"趣味"
-"趣味性"
-"趱"
-"足"
-"足三里"
-"足以"
-"足协"
-"足够"
-"足球"
-"足足"
-"足轻重"
-"足迹"
-"足部"
-"趴"
-"趴在"
-"趵"
-"趸"
-"趺"
-"趾"
-"趿"
-"跃"
-"跃进"
-"跄"
-"跆"
-"跆拳道"
-"跋"
-"跌"
-"跌倒"
-"跌停"
-"跌势"
-"跌宕"
-"跌幅"
-"跌破"
-"跌至"
-"跌落"
-"跎"
-"跏"
-"跐"
-"跑"
-"跑步"
-"跑跑"
-"跑车"
-"跑道"
-"跖"
-"跗"
-"跚"
-"跛"
-"距"
-"距今"
-"距离"
-"跞"
-"跟"
-"跟上"
-"跟不上"
-"跟前"
-"跟着"
-"跟踪"
-"跟进"
-"跟随"
-"跟鞋"
-"跟风"
-"跣"
-"跤"
-"跨"
-"跨国"
-"跨境"
-"跨年"
-"跨度"
-"跨界"
-"跨越"
-"跪"
-"跬"
-"路"
-"路上"
-"路人"
-"路况"
-"路口"
-"路基"
-"路子"
-"路径"
-"路易"
-"路易斯"
-"路段"
-"路灯"
-"路由"
-"路由器"
-"路程"
-"路线"
-"路边"
-"路过"
-"路透"
-"路途"
-"路里"
-"路面"
-"跳"
-"跳下"
-"跳出"
-"跳动"
-"跳楼"
-"跳槽"
-"跳水"
-"跳空"
-"跳绳"
-"跳舞"
-"跳跃"
-"跳过"
-"践"
-"践行"
-"践踏"
-"跶"
-"跷"
-"跸"
-"跹"
-"跺"
-"跻"
-"跻身"
-"踅"
-"踉"
-"踊"
-"踊跃"
-"踌"
-"踏"
-"踏上"
-"踏入"
-"踏实"
-"踏板"
-"踏踏"
-"踏踏实实"
-"踒"
-"踔"
-"踝"
-"踞"
-"踟"
-"踢"
-"踢球"
-"踩"
-"踪"
-"踪影"
-"踪迹"
-"踬"
-"踮"
-"踯"
-"踱"
-"踵"
-"踹"
-"踺"
-"踽"
-"蹀"
-"蹁"
-"蹂"
-"蹄"
-"蹇"
-"蹈"
-"蹉"
-"蹊"
-"蹋"
-"蹑"
-"蹒"
-"蹙"
-"蹚"
-"蹦"
-"蹦出"
-"蹩"
-"蹬"
-"蹭"
-"蹰"
-"蹲"
-"蹲下"
-"蹴"
-"蹶"
-"蹼"
-"蹽"
-"蹾"
-"蹿"
-"躁"
-"躅"
-"躇"
-"躏"
-"躐"
-"躜"
-"躞"
-"身"
-"身上"
-"身世"
-"身临"
-"身为"
-"身亡"
-"身价"
-"身份"
-"身份证"
-"身体"
-"身体健康"
-"身体状况"
-"身体素质"
-"身后"
-"身在"
-"身处"
-"身子"
-"身形"
-"身影"
-"身心"
-"身心健康"
-"身手"
-"身旁"
-"身材"
-"身着"
-"身穿"
-"身躯"
-"身边"
-"身高"
-"躬"
-"躯"
-"躯体"
-"躯干"
-"躲"
-"躲过"
-"躲避"
-"躺"
-"車"
-"軍"
-"較"
-"輕"
-"轉"
-"车"
-"车上"
-"车主"
-"车企"
-"车位"
-"车内"
-"车厢"
-"车友"
-"车型"
-"车头"
-"车子"
-"车尾"
-"车展"
-"车床"
-"车库"
-"车灯"
-"车牌"
-"车票"
-"车祸"
-"车程"
-"车窗"
-"车站"
-"车身"
-"车轮"
-"车载"
-"车辆"
-"车速"
-"车道"
-"车里"
-"车门"
-"车间"
-"车队"
-"车顶"
-"轧"
-"轨"
-"轨迹"
-"轨道"
-"轨道交通"
-"轩"
-"轩辕"
-"轫"
-"转"
-"转世"
-"转业"
-"转为"
-"转会"
-"转入"
-"转到"
-"转动"
-"转化"
-"转化成"
-"转化率"
-"转发"
-"转变"
-"转向"
-"转型"
-"转基因"
-"转头"
-"转弯"
-"转战"
-"转折"
-"转折点"
-"转换"
-"转接"
-"转机"
-"转眼"
-"转移"
-"转而"
-"转自"
-"转让"
-"转账"
-"转身"
-"转转"
-"转载"
-"转过"
-"转运"
-"转速"
-"轭"
-"轮"
-"轮回"
-"轮子"
-"轮廓"
-"轮换"
-"轮机"
-"轮椅"
-"轮毂"
-"轮流"
-"轮胎"
-"软"
-"软件"
-"软件工程"
-"软体"
-"软化"
-"软弱"
-"软文"
-"软硬"
-"软组织"
-"软软"
-"软骨"
-"轰"
-"轰动"
-"轰炸"
-"轰炸机"
-"轰轰"
-"轰轰烈烈"
-"轱"
-"轲"
-"轳"
-"轴"
-"轴承"
-"轴线"
-"轴距"
-"轶"
-"轶事"
-"轸"
-"轻"
-"轻便"
-"轻型"
-"轻声"
-"轻工"
-"轻工业"
-"轻巧"
-"轻度"
-"轻微"
-"轻快"
-"轻易"
-"轻松"
-"轻柔"
-"轻盈"
-"轻而易举"
-"轻薄"
-"轻视"
-"轻轨"
-"轻轻"
-"轻轻地"
-"轻轻松松"
-"轻重"
-"轼"
-"载"
-"载体"
-"载入"
-"载荷"
-"轾"
-"轿"
-"轿车"
-"辂"
-"较"
-"较为"
-"较大"
-"较差"
-"较弱"
-"较强"
-"较真"
-"较量"
-"较长"
-"辄"
-"辅"
-"辅以"
-"辅佐"
-"辅助"
-"辅导"
-"辅导员"
-"辅导班"
-"辅料"
-"辅食"
-"辆"
-"辆车"
-"辇"
-"辈"
-"辈子"
-"辉"
-"辉煌"
-"辊"
-"辋"
-"辍"
-"辍学"
-"辎"
-"辏"
-"辐"
-"辐射"
-"辐鳍"
-"辑"
-"输"
-"输入"
-"输出"
-"输卵管"
-"输掉"
-"输液"
-"输电"
-"输给"
-"输血"
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-"金牌"
-"金牛"
-"金石"
-"金矿"
-"金秋"
-"金色"
-"金莲"
-"金融"
-"金融业"
-"金融危机"
-"金融市场"
-"金融服务"
-"金融机构"
-"金针"
-"金钟"
-"金钱"
-"金银"
-"金银花"
-"金陵"
-"金额"
-"金马"
-"金鱼"
-"金鸡"
-"金鹰"
-"金黄"
-"金黄色"
-"釜"
-"鈴"
-"鉄"
-"鉴"
-"鉴于"
-"鉴别"
-"鉴定"
-"鉴赏"
-"銀"
-"銮"
-"鋆"
-"鋈"
-"錢"
-"錯"
-"錾"
-"鍪"
-"鎏"
-"鏊"
-"鏖"
-"鐾"
-"鑫"
-"钇"
-"针"
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-"针形"
-"针灸"
-"针织"
-"钉"
-"钉子"
-"钊"
-"钋"
-"钌"
-"钍"
-"钎"
-"钏"
-"钒"
-"钓"
-"钓鱼"
-"钓鱼岛"
-"钕"
-"钗"
-"钙"
-"钙质"
-"钚"
-"钛"
-"钜"
-"钝"
-"钞"
-"钞票"
-"钟"
-"钟声"
-"钟情"
-"钟楼"
-"钟爱"
-"钟表"
-"钠"
-"钡"
-"钢"
-"钢丝"
-"钢厂"
-"钢材"
-"钢板"
-"钢琴"
-"钢笔"
-"钢筋"
-"钢管"
-"钢结构"
-"钢铁"
-"钣"
-"钤"
-"钥"
-"钥匙"
-"钦"
-"钧"
-"钨"
-"钩"
-"钫"
-"钮"
-"钯"
-"钰"
-"钱"
-"钱包"
-"钱币"
-"钱财"
-"钲"
-"钳"
-"钴"
-"钵"
-"钹"
-"钺"
-"钻"
-"钻井"
-"钻孔"
-"钻石"
-"钻研"
-"钻进"
-"钼"
-"钽"
-"钾"
-"钿"
-"铀"
-"铁"
-"铁板"
-"铁矿石"
-"铁血"
-"铁路"
-"铁路局"
-"铁道"
-"铁道部"
-"铂"
-"铃"
-"铃声"
-"铃木"
-"铄"
-"铅"
-"铅笔"
-"铆"
-"铈"
-"铉"
-"铊"
-"铋"
-"铌"
-"铍"
-"铎"
-"铐"
-"铑"
-"铒"
-"铗"
-"铙"
-"铛"
-"铜"
-"铜陵"
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-"铝合"
-"铝合金"
-"铟"
-"铠"
-"铡"
-"铢"
-"铣"
-"铤"
-"铧"
-"铨"
-"铩"
-"铪"
-"铫"
-"铬"
-"铭"
-"铭心"
-"铭文"
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-"铮"
-"铯"
-"铰"
-"铱"
-"铲"
-"铳"
-"铵"
-"银"
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-"银川"
-"银幕"
-"银杏"
-"银河"
-"银牌"
-"银耳"
-"银联"
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-"销售"
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-"锂电池"
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-"锈"
-"锉"
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-"锦赛"
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-"镀铬"
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-"镂空"
-"镇"
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-"镇守"
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-"长久"
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-"长出"
-"长发"
-"长城"
-"长处"
-"长大"
-"长子"
-"长安"
-"长官"
-"长宽"
-"长寿"
-"长年"
-"长度"
-"长廊"
-"长征"
-"长得"
-"长成"
-"长效"
-"长效机制"
-"长方形"
-"长时间"
-"长春"
-"长春市"
-"长期"
-"长期以来"
-"长款"
-"长此"
-"长毛"
-"长江"
-"长沙"
-"长沙市"
-"长河"
-"长治"
-"长生"
-"长痘"
-"长白山"
-"长相"
-"长短"
-"长篇"
-"长篇小说"
-"长约"
-"长线"
-"长老"
-"长者"
-"长虹"
-"长袖"
-"长裙"
-"长裤"
-"长足"
-"长跑"
-"长辈"
-"长达"
-"长远"
-"长途"
-"长长"
-"长长的"
-"长颈"
-"門"
-"開"
-"開始"
-"間"
-"関"
-"關"
-"门"
-"门下"
-"门人"
-"门前"
-"门口"
-"门外"
-"门将"
-"门市"
-"门店"
-"门户"
-"门户网"
-"门户网站"
-"门槛"
-"门票"
-"门禁"
-"门窗"
-"门类"
-"门诊"
-"门锁"
-"门面"
-"闩"
-"闪"
-"闪亮"
-"闪光"
-"闪光灯"
-"闪存"
-"闪烁"
-"闪现"
-"闪电"
-"闪耀"
-"闪闪"
-"闫"
-"闭"
-"闭上"
-"闭上眼睛"
-"闭关"
-"闭口"
-"闭合"
-"闭塞"
-"闭幕"
-"闭环"
-"闭路"
-"闭门"
-"问"
-"问下"
-"问世"
-"问候"
-"问到"
-"问卷"
-"问及"
-"问答"
-"问责"
-"问过"
-"问道"
-"问问"
-"问题"
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-"闯入"
-"闯关"
-"闯荡"
-"闰"
-"闱"
-"闲"
-"闲暇"
-"闲着"
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-"闲聊"
-"闳"
-"间"
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-"间断"
-"间歇"
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-"间谍"
-"间距"
-"间隔"
-"间隙"
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-"闷"
-"闷声"
-"闷热"
-"闸"
-"闹"
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-"闹钟"
-"闹闹"
-"闺"
-"闺女"
-"闺密"
-"闺蜜"
-"闻"
-"闻乐"
-"闻到"
-"闻名"
-"闼"
-"闽"
-"闽东"
-"闽南"
-"闾"
-"闿"
-"阀"
-"阀门"
-"阁"
-"阂"
-"阃"
-"阄"
-"阅"
-"阅兵"
-"阅卷"
-"阅历"
-"阅览"
-"阅读"
-"阆"
-"阇"
-"阈"
-"阈值"
-"阉"
-"阊"
-"阋"
-"阍"
-"阎"
-"阏"
-"阐"
-"阐明"
-"阐述"
-"阐释"
-"阑"
-"阑尾"
-"阒"
-"阔"
-"阔叶"
-"阕"
-"阖"
-"阗"
-"阙"
-"阚"
-"阜"
-"阜阳"
-"阝"
-"队"
-"队伍"
-"队列"
-"队友"
-"队员"
-"队长"
-"阡"
-"阪"
-"阮"
-"阱"
-"防"
-"防伪"
-"防卫"
-"防备"
-"防守"
-"防尘"
-"防弹"
-"防御"
-"防抖"
-"防护"
-"防控"
-"防晒"
-"防晒霜"
-"防止"
-"防水"
-"防汛"
-"防治"
-"防洪"
-"防滑"
-"防潮"
-"防火"
-"防爆"
-"防疫"
-"防病"
-"防癌"
-"防盗"
-"防空"
-"防线"
-"防腐"
-"防腐剂"
-"防范"
-"防锈"
-"防雷"
-"防震"
-"防风"
-"阳"
-"阳光"
-"阳区"
-"阳县"
-"阳台"
-"阳市"
-"阳性"
-"阳明"
-"阳春"
-"阳气"
-"阳痿"
-"阳线"
-"阳虚"
-"阴"
-"阴凉"
-"阴天"
-"阴影"
-"阴性"
-"阴暗"
-"阴极"
-"阴线"
-"阴茎"
-"阴虚"
-"阴谋"
-"阴道"
-"阴道炎"
-"阴阳"
-"阴险"
-"阴雨"
-"阴霾"
-"阵"
-"阵亡"
-"阵地"
-"阵容"
-"阵营"
-"阵阵"
-"阵雨"
-"阶"
-"阶层"
-"阶梯"
-"阶段"
-"阶段性"
-"阶级"
-"阻"
-"阻击"
-"阻力"
-"阻力位"
-"阻塞"
-"阻抗"
-"阻拦"
-"阻挡"
-"阻断"
-"阻止"
-"阻滞"
-"阻燃"
-"阻碍"
-"阿"
-"阿克"
-"阿卡"
-"阿姆斯特"
-"阿姨"
-"阿富汗"
-"阿尔"
-"阿尔卑斯"
-"阿布"
-"阿弥陀佛"
-"阿德"
-"阿拉"
-"阿拉伯"
-"阿拉斯加"
-"阿斯"
-"阿根廷"
-"阿森"
-"阿森纳"
-"阿波罗"
-"阿联"
-"阿胶"
-"阿迪"
-"阿里"
-"阿里巴巴"
-"阿难"
-"陀"
-"陀罗"
-"陀螺"
-"陂"
-"附"
-"附上"
-"附中"
-"附件"
-"附加"
-"附加值"
-"附图"
-"附子"
-"附属"
-"附带"
-"附录"
-"附着"
-"附赠"
-"附近"
-"际"
-"际关系"
-"陆"
-"陆军"
-"陆地"
-"陆游"
-"陆续"
-"陆陆"
-"陇"
-"陈"
-"陈代"
-"陈列"
-"陈奕"
-"陈旧"
-"陈氏"
-"陈皮"
-"陈述"
-"陉"
-"陋"
-"陌"
-"陌生"
-"陌生人"
-"降"
-"降临"
-"降价"
-"降低"
-"降低成本"
-"降准"
-"降到"
-"降压"
-"降噪"
-"降幅"
-"降息"
-"降水"
-"降水量"
-"降温"
-"降级"
-"降至"
-"降落"
-"降血"
-"降血压"
-"降解"
-"降雨"
-"限"
-"限于"
-"限价"
-"限制"
-"限定"
-"限度"
-"限时"
-"限期"
-"限行"
-"限购"
-"限速"
-"限量"
-"限额"
-"陔"
-"陕"
-"陕北"
-"陕西"
-"陕西省"
-"陛"
-"陟"
-"陡"
-"院"
-"院内"
-"院士"
-"院子"
-"院校"
-"院系"
-"院落"
-"院长"
-"除"
-"除了"
-"除去"
-"除夕"
-"除外"
-"除夜"
-"除尘"
-"除恶"
-"除掉"
-"除此"
-"除此之外"
-"除湿"
-"除皱"
-"除草"
-"除非"
-"陨"
-"陨石"
-"险"
-"险恶"
-"陪"
-"陪伴"
-"陪同"
-"陬"
-"陲"
-"陳"
-"陵"
-"陵园"
-"陶"
-"陶冶"
-"陶器"
-"陶渊"
-"陶瓷"
-"陶艺"
-"陶醉"
-"陷"
-"陷入"
-"陷害"
-"陷阱"
-"陸"
-"陽"
-"隅"
-"隆"
-"隆胸"
-"隆起"
-"隆重"
-"隆鼻"
-"隈"
-"隊"
-"隋"
-"隋唐"
-"隋朝"
-"隍"
-"随"
-"随之"
-"随之而"
-"随之而来"
-"随从"
-"随便"
-"随即"
-"随口"
-"随后"
-"随地"
-"随处"
-"随处可见"
-"随心"
-"随心所欲"
-"随性"
-"随意"
-"随手"
-"随时"
-"随时随地"
-"随机"
-"随着"
-"随笔"
-"随缘"
-"随访"
-"随身"
-"随身携带"
-"随风"
-"隐"
-"隐匿"
-"隐含"
-"隐居"
-"隐形"
-"隐形眼"
-"隐忍"
-"隐性"
-"隐患"
-"隐瞒"
-"隐私"
-"隐秘"
-"隐约"
-"隐蔽"
-"隐藏"
-"隐身"
-"隐隐"
-"隔"
-"隔壁"
-"隔夜"
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-"隔绝"
-"隔膜"
-"隔音"
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-"障"
-"障碍"
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-"隧道"
-"隨"
-"隰"
-"隳"
-"隶"
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-"隽"
-"难"
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-"难以"
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-"难免会"
-"难关"
-"难受"
-"难度"
-"难得"
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-"难怪"
-"难易"
-"难民"
-"难点"
-"难看"
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-"难题"
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-"雁"
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-"雄厚"
-"雄安"
-"雄心"
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-"雅思"
-"雅致"
-"雅虎"
-"集"
-"集中"
-"集会"
-"集体"
-"集体经济"
-"集合"
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-"集团公司"
-"集市"
-"集成"
-"集成电路"
-"集散"
-"集权"
-"集约"
-"集结"
-"集群"
-"集聚"
-"集装箱"
-"集训"
-"集贸"
-"集资"
-"集邮"
-"集锦"
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-"雉"
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-"雌激素"
-"雌雄"
-"雍"
-"雍正"
-"雎"
-"雏"
-"雒"
-"雕"
-"雕像"
-"雕刻"
-"雕塑"
-"雕琢"
-"雖"
-"雠"
-"離"
-"難"
-"雨"
-"雨伞"
-"雨天"
-"雨季"
-"雨林"
-"雨水"
-"雨量"
-"雨雪"
-"雩"
-"雪"
-"雪地"
-"雪山"
-"雪白"
-"雪花"
-"雪芹"
-"雯"
-"雱"
-"雲"
-"雳"
-"零件"
-"零售"
-"零售业"
-"零售商"
-"零散"
-"零星"
-"零点"
-"零碎"
-"零花"
-"零部"
-"零部件"
-"零食"
-"雷"
-"雷克萨"
-"雷克萨斯"
-"雷军"
-"雷斯"
-"雷电"
-"雷神"
-"雷诺"
-"雷达"
-"雷锋"
-"雷雨"
-"雷霆"
-"雹"
-"電"
-"雾"
-"需"
-"需不需"
-"需求"
-"需求量"
-"需要"
-"需谨慎"
-"霁"
-"霄"
-"霆"
-"震"
-"震动"
-"震惊"
-"震慑"
-"震撼"
-"震荡"
-"霈"
-"霉"
-"霉素"
-"霉菌"
-"霍"
-"霍华"
-"霍夫"
-"霍尔"
-"霍金"
-"霎"
-"霏"
-"霓"
-"霓虹"
-"霖"
-"霜"
-"霞"
-"霨"
-"霪"
-"霭"
-"霰"
-"露"
-"露出"
-"露天"
-"露水"
-"露面"
-"霸"
-"霸主"
-"霸占"
-"霸权"
-"霸气"
-"霸王"
-"霸道"
-"霹"
-"霹雳"
-"霾"
-"青"
-"青云"
-"青光"
-"青团"
-"青少年"
-"青山"
-"青岛"
-"青岛市"
-"青年"
-"青年人"
-"青旅"
-"青春"
-"青春期"
-"青春痘"
-"青梅"
-"青椒"
-"青海"
-"青海省"
-"青涩"
-"青睐"
-"青石"
-"青色"
-"青花"
-"青菜"
-"青藏"
-"青蛙"
-"青铜"
-"青铜器"
-"青青"
-"青鸟"
-"青龙"
-"靓"
-"靓丽"
-"靖"
-"静"
-"静下"
-"静下心"
-"静下心来"
-"静坐"
-"静安"
-"静心"
-"静态"
-"静止"
-"静物"
-"静电"
-"静脉"
-"静脉曲张"
-"静谧"
-"静静"
-"静静地"
-"静静的"
-"静音"
-"靛"
-"非"
-"非但"
-"非典"
-"非农"
-"非凡"
-"非常"
-"非常容易"
-"非常感谢"
-"非常明显"
-"非常简单"
-"非常适合"
-"非常重视"
-"非得"
-"非所"
-"非机动车"
-"非正"
-"非法"
-"非洲"
-"非线性"
-"非营利"
-"非要"
-"非诚"
-"非遗"
-"非金属"
-"靠"
-"靠山"
-"靠拢"
-"靠着"
-"靠近"
-"靡"
-"面"
-"面上"
-"面临"
-"面具"
-"面前"
-"面包"
-"面向"
-"面向对象"
-"面团"
-"面子"
-"面孔"
-"面容"
-"面对"
-"面对面"
-"面料"
-"面条"
-"面板"
-"面瘫"
-"面皮"
-"面目"
-"面相"
-"面积"
-"面粉"
-"面糊"
-"面纱"
-"面而来"
-"面膜"
-"面色"
-"面议"
-"面试"
-"面试官"
-"面貌"
-"面部"
-"面霜"
-"面食"
-"靥"
-"革"
-"革命"
-"革命烈"
-"革新"
-"革质"
-"靰"
-"靳"
-"靴"
-"靶"
-"靶向"
-"靸"
-"靺"
-"靼"
-"靿"
-"鞅"
-"鞋"
-"鞋子"
-"鞋底"
-"鞍"
-"鞍山"
-"鞑"
-"鞒"
-"鞘"
-"鞠"
-"鞠躬"
-"鞡"
-"鞣"
-"鞧"
-"鞨"
-"鞫"
-"鞬"
-"鞭"
-"鞭炮"
-"鞯"
-"鞲"
-"鞴"
-"韦"
-"韦伯"
-"韦博"
-"韦德"
-"韧"
-"韧带"
-"韧性"
-"韩"
-"韩信"
-"韩剧"
-"韩国"
-"韩寒"
-"韩庚"
-"韩式"
-"韩文"
-"韩版"
-"韩语"
-"韩非"
-"韪"
-"韫"
-"韬"
-"韭"
-"韭菜"
-"音"
-"音乐"
-"音乐会"
-"音乐学院"
-"音乐家"
-"音乐节"
-"音像"
-"音响"
-"音效"
-"音符"
-"音箱"
-"音色"
-"音节"
-"音译"
-"音调"
-"音质"
-"音量"
-"音频"
-"韵"
-"韵味"
-"韵律"
-"韶"
-"韶关"
-"響"
-"順"
-"領"
-"頫"
-"頭"
-"題"
-"願"
-"類"
-"顯"
-"页"
-"页岩"
-"页数"
-"页面"
-"顶"
-"顶上"
-"顶多"
-"顶尖"
-"顶层"
-"顶峰"
-"顶点"
-"顶礼"
-"顶端"
-"顶级"
-"顶部"
-"顷"
-"顸"
-"项"
-"项目"
-"项目管理"
-"项目经理"
-"项链"
-"顺"
-"顺丰"
-"顺义"
-"顺从"
-"顺便"
-"顺其自"
-"顺其自然"
-"顺利"
-"顺利完成"
-"顺势"
-"顺序"
-"顺应"
-"顺德"
-"顺手"
-"顺时针"
-"顺水"
-"顺畅"
-"顺眼"
-"顺着"
-"顺风"
-"须"
-"须知"
-"顼"
-"顽"
-"顽固"
-"顽强"
-"顾"
-"顾及"
-"顾名"
-"顾名思义"
-"顾客"
-"顾忌"
-"顾虑"
-"顾问"
-"顿"
-"顿悟"
-"顿时"
-"顿饭"
-"颀"
-"颁"
-"颁发"
-"颁奖"
-"颁奖典礼"
-"颁布"
-"颂"
-"颃"
-"预"
-"预习"
-"预付"
-"预估"
-"预先"
-"预判"
-"预后"
-"预告"
-"预售"
-"预备"
-"预定"
-"预应"
-"预想"
-"预报"
-"预料"
-"预期"
-"预案"
-"预测"
-"预热"
-"预留"
-"预知"
-"预示"
-"预科"
-"预算"
-"预约"
-"预见"
-"预览"
-"预言"
-"预警"
-"预计"
-"预订"
-"预设"
-"预选"
-"预防"
-"颅"
-"领"
-"领事"
-"领会"
-"领先"
-"领先地位"
-"领克"
-"领养"
-"领军"
-"领取"
-"领土"
-"领地"
-"领域"
-"领头"
-"领官"
-"领导"
-"领导人"
-"领导力"
-"领导小组"
-"领导班子"
-"领导者"
-"领带"
-"领悟"
-"领略"
-"领航"
-"领衔"
-"领袖"
-"领跑"
-"领队"
-"颇"
-"颇为"
-"颇具"
-"颇受"
-"颈"
-"颈椎"
-"颈椎病"
-"颈部"
-"颉"
-"颊"
-"颌"
-"颍"
-"颎"
-"颏"
-"颐"
-"频"
-"频发"
-"频率"
-"频繁"
-"频谱"
-"频道"
-"频频"
-"颓"
-"颓废"
-"颔"
-"颖"
-"颗"
-"颗粒"
-"题"
-"题为"
-"题主"
-"题主想"
-"题型"
-"题外"
-"题库"
-"题材"
-"题目"
-"题词"
-"颙"
-"颚"
-"颛"
-"颜"
-"颜值"
-"颜料"
-"颜色"
-"额"
-"额外"
-"额头"
-"额定"
-"额度"
-"颞"
-"颟"
-"颠"
-"颠倒"
-"颠簸"
-"颠覆"
-"颡"
-"颢"
-"颤"
-"颤抖"
-"颦"
-"颧"
-"風"
-"风"
-"风中"
-"风云"
-"风俗"
-"风光"
-"风力"
-"风华"
-"风口"
-"风向"
-"风吹"
-"风味"
-"风土"
-"风声"
-"风头"
-"风寒"
-"风尘"
-"风尚"
-"风度"
-"风情"
-"风扇"
-"风控"
-"风景"
-"风景区"
-"风景线"
-"风暴"
-"风机"
-"风格"
-"风气"
-"风水"
-"风波"
-"风流"
-"风湿"
-"风湿性"
-"风琴"
-"风电"
-"风筝"
-"风范"
-"风行"
-"风衣"
-"风貌"
-"风趣"
-"风速"
-"风采"
-"风险"
-"风险管理"
-"风雨"
-"风靡"
-"风顺"
-"飑"
-"飒"
-"飓"
-"飕"
-"飘"
-"飘过"
-"飘逸"
-"飘飘"
-"飘香"
-"飙"
-"飙升"
-"飚"
-"飛"
-"飞"
-"飞利浦"
-"飞天"
-"飞奔"
-"飞快"
-"飞扬"
-"飞机"
-"飞来"
-"飞猛"
-"飞翔"
-"飞舞"
-"飞船"
-"飞行"
-"飞行员"
-"飞起"
-"飞跃"
-"飞车"
-"飞轮"
-"飞过"
-"飞速"
-"飞速发展"
-"飞鸟"
-"飞龙"
-"食"
-"食品"
-"食品安全"
-"食品药品"
-"食堂"
-"食客"
-"食指"
-"食材"
-"食欲"
-"食欲不振"
-"食物"
-"食用"
-"食用油"
-"食用菌"
-"食疗"
-"食盐"
-"食神"
-"食管"
-"食肉"
-"食谱"
-"食道"
-"飧"
-"飨"
-"飯"
-"養"
-"餍"
-"餐"
-"餐具"
-"餐厅"
-"餐桌"
-"餐桌上"
-"餐饮"
-"餐馆"
-"館"
-"餮"
-"饔"
-"饕"
-"饥"
-"饥渴"
-"饥饿"
-"饧"
-"饨"
-"饩"
-"饪"
-"饫"
-"饬"
-"饭"
-"饭前"
-"饭后"
-"饭店"
-"饭桌"
-"饭碗"
-"饭菜"
-"饭馆"
-"饮"
-"饮品"
-"饮料"
-"饮水"
-"饮用"
-"饮用水"
-"饮茶"
-"饮酒"
-"饮食"
-"饮食习惯"
-"饯"
-"饰"
-"饰品"
-"饰演"
-"饱"
-"饱受"
-"饱含"
-"饱和"
-"饱满"
-"饲"
-"饲养"
-"饲料"
-"饴"
-"饵"
-"饶"
-"饷"
-"饸"
-"饹"
-"饺"
-"饺子"
-"饼"
-"饼干"
-"饽"
-"饿"
-"饿死"
-"馀"
-"馁"
-"馃"
-"馄"
-"馄饨"
-"馅"
-"馅料"
-"馅饼"
-"馆"
-"馆藏"
-"馇"
-"馈"
-"馈赠"
-"馊"
-"馋"
-"馍"
-"馏"
-"馐"
-"馑"
-"馒"
-"馒头"
-"馓"
-"馔"
-"馕"
-"首"
-"首个"
-"首乌"
-"首付"
-"首位"
-"首先"
-"首创"
-"首发"
-"首家"
-"首富"
-"首尔"
-"首屈"
-"首届"
-"首席"
-"首批"
-"首播"
-"首日"
-"首映"
-"首次"
-"首款"
-"首歌"
-"首相"
-"首答"
-"首要"
-"首轮"
-"首选"
-"首都"
-"首页"
-"首领"
-"首饰"
-"馗"
-"香"
-"香味"
-"香山"
-"香料"
-"香格里拉"
-"香槟"
-"香气"
-"香水"
-"香油"
-"香港"
-"香港中文"
-"香火"
-"香烟"
-"香甜"
-"香精"
-"香肠"
-"香草"
-"香菇"
-"香菜"
-"香葱"
-"香蕉"
-"香辣"
-"馥"
-"馨"
-"馬"
-"駅"
-"驗"
-"马"
-"马丁"
-"马上"
-"马云"
-"马克"
-"马克思"
-"马克思主义"
-"马列"
-"马刺"
-"马力"
-"马哈"
-"马尔"
-"马尔代夫"
-"马尾"
-"马屁"
-"马德里"
-"马拉"
-"马拉松"
-"马斯"
-"马来"
-"马来西亚"
-"马桶"
-"马特"
-"马甲"
-"马自达"
-"马蓉"
-"马虎"
-"马赛"
-"马赫"
-"马路"
-"马路上"
-"马蹄"
-"马车"
-"马达"
-"马里"
-"马铃薯"
-"马鞍山"
-"马龙"
-"驭"
-"驮"
-"驯"
-"驰"
-"驰名"
-"驰骋"
-"驱"
-"驱使"
-"驱动"
-"驱车"
-"驱逐"
-"驱逐舰"
-"驳"
-"驳回"
-"驴"
-"驶"
-"驷"
-"驸"
-"驹"
-"驻"
-"驻地"
-"驻扎"
-"驻村"
-"驻足"
-"驼"
-"驽"
-"驾"
-"驾乘"
-"驾校"
-"驾照"
-"驾车"
-"驾驭"
-"驾驶"
-"驾驶员"
-"驾驶证"
-"驿"
-"驿站"
-"骀"
-"骁"
-"骁龙"
-"骂"
-"骂人"
-"骄"
-"骄傲"
-"骅"
-"骆"
-"骆驼"
-"骇"
-"骈"
-"骊"
-"骋"
-"验"
-"验光"
-"验收"
-"验方"
-"验证"
-"骎"
-"骏"
-"骐"
-"骑"
-"骑兵"
-"骑士"
-"骑着"
-"骑行"
-"骑车"
-"骑马"
-"骒"
-"骓"
-"骖"
-"骗"
-"骗人"
-"骗取"
-"骗子"
-"骗局"
-"骘"
-"骚"
-"骚年"
-"骚扰"
-"骛"
-"骜"
-"骝"
-"骞"
-"骟"
-"骠"
-"骡"
-"骢"
-"骣"
-"骤"
-"骥"
-"骧"
-"骨"
-"骨头"
-"骨子里"
-"骨干"
-"骨干教师"
-"骨折"
-"骨架"
-"骨灰"
-"骨盆"
-"骨科"
-"骨肉"
-"骨质"
-"骨骼"
-"骨髓"
-"骰"
-"骶"
-"骷"
-"骷髅"
-"骸"
-"骺"
-"骼"
-"髀"
-"髁"
-"髂"
-"髅"
-"髋"
-"髌"
-"髑"
-"髓"
-"體"
-"高"
-"高下"
-"高中"
-"高中生"
-"高丽"
-"高举"
-"高于"
-"高产"
-"高人"
-"高价"
-"高估"
-"高位"
-"高低"
-"高傲"
-"高僧"
-"高光"
-"高兴"
-"高冷"
-"高出"
-"高分"
-"高分子"
-"高危"
-"高压"
-"高原"
-"高发"
-"高品质"
-"高地"
-"高处"
-"高大"
-"高宗"
-"高官"
-"高尔"
-"高尔夫"
-"高尔夫球"
-"高尚"
-"高层"
-"高层次"
-"高山"
-"高峰"
-"高峰期"
-"高峰论坛"
-"高度"
-"高度重视"
-"高开"
-"高强度"
-"高性价"
-"高性能"
-"高手"
-"高技术"
-"高效"
-"高效率"
-"高数"
-"高新"
-"高新区"
-"高新技术"
-"高昂"
-"高明"
-"高架"
-"高标准"
-"高校"
-"高档"
-"高桥"
-"高楼"
-"高歌"
-"高水平"
-"高汤"
-"高涨"
-"高深"
-"高清"
-"高温"
-"高潮"
-"高点"
-"高烧"
-"高热"
-"高盛"
-"高祖"
-"高票"
-"高科技"
-"高空"
-"高端"
-"高等"
-"高等学校"
-"高等教育"
-"高等院校"
-"高管"
-"高粱"
-"高素质"
-"高级"
-"高考"
-"高耸"
-"高职"
-"高能"
-"高薪"
-"高蛋白"
-"高血压"
-"高血脂"
-"高调"
-"高质量"
-"高贵"
-"高超"
-"高跟"
-"高跟鞋"
-"高达"
-"高远"
-"高通"
-"高速"
-"高速公路"
-"高铁"
-"高阶"
-"高雄"
-"高雅"
-"高音"
-"高频"
-"高颜值"
-"高额"
-"高风险"
-"高高"
-"高高的"
-"高龄"
-"髡"
-"髦"
-"髫"
-"髭"
-"髯"
-"髹"
-"髻"
-"鬃"
-"鬈"
-"鬏"
-"鬓"
-"鬟"
-"鬣"
-"鬲"
-"鬻"
-"鬼"
-"鬼吹"
-"鬼子"
-"鬼怪"
-"鬼神"
-"魁"
-"魂"
-"魃"
-"魄"
-"魅"
-"魅力"
-"魅族"
-"魆"
-"魇"
-"魈"
-"魉"
-"魍"
-"魏"
-"魏则"
-"魏国"
-"魏晋"
-"魑"
-"魔"
-"魔兽"
-"魔力"
-"魔女"
-"魔幻"
-"魔术"
-"魔法"
-"魔王"
-"魔界"
-"魔鬼"
-"鱼"
-"鱼儿"
-"鱼头"
-"鱼尾"
-"鱼尾纹"
-"鱼类"
-"鱼肉"
-"鱼雷"
-"鱼龙"
-"鱿"
-"鱿鱼"
-"鲀"
-"鲁"
-"鲁国"
-"鲁斯"
-"鲁班"
-"鲁能"
-"鲁迅"
-"鲂"
-"鲅"
-"鲆"
-"鲇"
-"鲈"
-"鲋"
-"鲍"
-"鲍鱼"
-"鲎"
-"鲐"
-"鲑"
-"鲔"
-"鲚"
-"鲛"
-"鲜"
-"鲜为"
-"鲜卑"
-"鲜奶"
-"鲜嫩"
-"鲜明"
-"鲜活"
-"鲜红"
-"鲜美"
-"鲜肉"
-"鲜艳"
-"鲜花"
-"鲜血"
-"鲞"
-"鲟"
-"鲠"
-"鲡"
-"鲢"
-"鲣"
-"鲤"
-"鲤鱼"
-"鲥"
-"鲧"
-"鲨"
-"鲨鱼"
-"鲩"
-"鲫"
-"鲫鱼"
-"鲮"
-"鲱"
-"鲲"
-"鲳"
-"鲴"
-"鲵"
-"鲶"
-"鲷"
-"鲸"
-"鲻"
-"鲼"
-"鲽"
-"鳀"
-"鳃"
-"鳄"
-"鳄鱼"
-"鳅"
-"鳇"
-"鳊"
-"鳌"
-"鳍"
-"鳎"
-"鳏"
-"鳐"
-"鳓"
-"鳔"
-"鳕"
-"鳖"
-"鳗"
-"鳙"
-"鳜"
-"鳝"
-"鳞"
-"鳞片"
-"鳟"
-"鳢"
-"鳥"
-"鸟"
-"鸟儿"
-"鸟巢"
-"鸟类"
-"鸠"
-"鸡"
-"鸡尾酒"
-"鸡毛"
-"鸡汤"
-"鸡皮"
-"鸡精"
-"鸡翅"
-"鸡肉"
-"鸡胸"
-"鸡腿"
-"鸡蛋"
-"鸡血"
-"鸢"
-"鸣"
-"鸣人"
-"鸥"
-"鸦"
-"鸦片"
-"鸨"
-"鸩"
-"鸪"
-"鸫"
-"鸬"
-"鸭"
-"鸭子"
-"鸮"
-"鸯"
-"鸱"
-"鸲"
-"鸳"
-"鸳鸯"
-"鸵"
-"鸶"
-"鸷"
-"鸸"
-"鸹"
-"鸻"
-"鸽"
-"鸽子"
-"鸾"
-"鸿"
-"鹁"
-"鹂"
-"鹃"
-"鹄"
-"鹅"
-"鹆"
-"鹇"
-"鹈"
-"鹉"
-"鹊"
-"鹋"
-"鹌"
-"鹏"
-"鹐"
-"鹑"
-"鹕"
-"鹗"
-"鹘"
-"鹚"
-"鹛"
-"鹜"
-"鹞"
-"鹤"
-"鹦"
-"鹦鹉"
-"鹧"
-"鹩"
-"鹪"
-"鹫"
-"鹬"
-"鹭"
-"鹮"
-"鹰"
-"鹳"
-"鹿"
-"鹿晗"
-"麂"
-"麇"
-"麈"
-"麋"
-"麒"
-"麒麟"
-"麓"
-"麝"
-"麟"
-"麦"
-"麦克"
-"麦克风"
-"麦当劳"
-"麦田"
-"麦芽"
-"麦迪"
-"麸"
-"麻"
-"麻将"
-"麻木"
-"麻油"
-"麻烦"
-"麻疹"
-"麻痹"
-"麻花"
-"麻辣"
-"麻醉"
-"麻雀"
-"麻麻"
-"麻黄"
-"麼"
-"麽"
-"麾"
-"黃"
-"黄"
-"黄冈"
-"黄土"
-"黄埔"
-"黄山"
-"黄帝"
-"黄昏"
-"黄梅"
-"黄河"
-"黄油"
-"黄浦"
-"黄海"
-"黄牛"
-"黄瓜"
-"黄疸"
-"黄石"
-"黄色"
-"黄芪"
-"黄花"
-"黄蓉"
-"黄蜂"
-"黄褐"
-"黄褐斑"
-"黄豆"
-"黄连"
-"黄酒"
-"黄酮"
-"黄金"
-"黄金周"
-"黄鹤"
-"黄龙"
-"黍"
-"黎"
-"黎明"
-"黏"
-"黏膜"
-"黑"
-"黑人"
-"黑名"
-"黑名单"
-"黑夜"
-"黑头"
-"黑客"
-"黑帮"
-"黑幕"
-"黑恶势力"
-"黑暗"
-"黑木耳"
-"黑板"
-"黑洞"
-"黑点"
-"黑白"
-"黑眼圈"
-"黑社会"
-"黑色"
-"黑色素"
-"黑芝麻"
-"黑衣"
-"黑褐"
-"黑豆"
-"黑马"
-"黑龙江"
-"黑龙江省"
-"黒"
-"黔"
-"默"
-"默化"
-"默契"
-"默认"
-"默默"
-"默默地"
-"默默无闻"
-"黛"
-"黛玉"
-"黜"
-"黝"
-"點"
-"黟"
-"黠"
-"黢"
-"黥"
-"黧"
-"黨"
-"黩"
-"黪"
-"黯"
-"黯然"
-"黻"
-"黼"
-"黾"
-"鼋"
-"鼍"
-"鼎"
-"鼎盛"
-"鼎鼎"
-"鼐"
-"鼓"
-"鼓动"
-"鼓励"
-"鼓吹"
-"鼓掌"
-"鼓楼"
-"鼓浪"
-"鼓舞"
-"鼓起"
-"鼙"
-"鼠"
-"鼠标"
-"鼢"
-"鼬"
-"鼯"
-"鼹"
-"鼻"
-"鼻咽"
-"鼻塞"
-"鼻子"
-"鼻孔"
-"鼻梁"
-"鼻涕"
-"鼻炎"
-"鼻翼"
-"鼻腔"
-"鼾"
-"齁"
-"齉"
-"齐"
-"齐全"
-"齐国"
-"齐心"
-"齐放"
-"齐白石"
-"齐聚"
-"齐鲁"
-"齐齐"
-"齐齐哈尔"
-"齑"
-"齿"
-"齿轮"
-"龀"
-"龃"
-"龄"
-"龅"
-"龇"
-"龈"
-"龉"
-"龊"
-"龋"
-"龌"
-"龍"
-"龙"
-"龙井"
-"龙凤"
-"龙华"
-"龙头"
-"龙头企业"
-"龙女"
-"龙山"
-"龙岗"
-"龙岩"
-"龙族"
-"龙江"
-"龙泉"
-"龙湖"
-"龙湾"
-"龙潭"
-"龙王"
-"龙珠"
-"龙眼"
-"龙舟"
-"龙虎"
-"龙虾"
-"龙门"
-"龙骨"
-"龚"
-"龛"
-"龟"
-"龠"
-"龸"
-"가"
-"강"
-"개"
-"경"
-"고"
-"공"
-"과"
-"교"
-"구"
-"국"
-"군"
-"그"
-"기"
-"김"
-"나"
-"남"
-"내"
-"년"
-"는"
-"니"
-"다"
-"단"
-"대"
-"도"
-"동"
-"드"
-"들"
-"라"
-"로"
-"를"
-"리"
-"마"
-"만"
-"면"
-"명"
-"모"
-"무"
-"문"
-"미"
-"민"
-"바"
-"박"
-"방"
-"백"
-"보"
-"부"
-"비"
-"사"
-"산"
-"상"
-"서"
-"선"
-"성"
-"세"
-"소"
-"수"
-"스"
-"시"
-"식"
-"신"
-"씨"
-"아"
-"안"
-"어"
-"에"
-"여"
-"역"
-"연"
-"영"
-"오"
-"왕"
-"요"
-"용"
-"우"
-"원"
-"월"
-"위"
-"유"
-"은"
-"을"
-"음"
-"의"
-"이"
-"인"
-"일"
-"자"
-"장"
-"재"
-"전"
-"정"
-"제"
-"조"
-"종"
-"주"
-"중"
-"지"
-"진"
-"차"
-"천"
-"타"
-"터"
-"트"
-"하"
-"학"
-"한"
-"합"
-"해"
-"현"
-"호"
-"화"
-"회"
-"fi"
-"️"
-"﹕"
-""
-"!"
-"("
-")"
-","
-"-"
-":"
-";"
-"?"
-"~"
-""
-"�"
-"��"
-"𠳐"
-"𥻗"
-"𬉼"
-""
-""
-""
-""
-""
-""
-""
diff --git a/9G-Train/cpm/dataset/list_dataset.py b/9G-Train/cpm/dataset/list_dataset.py
deleted file mode 100644
index b07f222..0000000
--- a/9G-Train/cpm/dataset/list_dataset.py
+++ /dev/null
@@ -1,57 +0,0 @@
-import itertools
-import os
-import random
-from typing import List
-
-import bmtrain as bmt
-import torch
-
-
-class ListDataset(torch.utils.data.Dataset):
- """
- 同时支持 map-style 和 iterable-style
- """
-
- def __init__(
- self, data_list: List, distributed: bool = False, shuffle: bool = True, infinite: bool = False
- ) -> None:
- super(ListDataset, self).__init__()
- if distributed:
- rank = bmt.rank()
- world_size = bmt.world_size()
- self.data_list = list(itertools.islice(data_list, rank, None, world_size))
- else:
- self.data_list = data_list
- self.shuffle = shuffle
- self.infinite = infinite
- self.idx = 0
-
- if shuffle:
- self._shuffle()
-
- def __iter__(self):
- return self
-
- def __next__(self):
- if self.idx >= len(self):
- if self.infinite:
- if self.shuffle:
- self._shuffle()
- self.idx = 0
- else:
- raise StopIteration
- data = self.data_list[self.idx]
- self.idx += 1
- return data
-
- def __getitem__(self, idx):
- return self.data_list[idx]
-
- def __len__(self):
- return len(self.data_list)
-
- def _shuffle(self):
- random.shuffle(self.data_list)
-
- def read(self):
- return self.__next__()
diff --git a/9G-Train/cpm/generation/__init__.py b/9G-Train/cpm/generation/__init__.py
deleted file mode 100644
index 3d24491..0000000
--- a/9G-Train/cpm/generation/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-from .generation_utils import apply_repetition_penalty
-from .generation_utils import BeamHypotheses
-from .generation_utils import NBCE
-from .generation_utils import top_k_top_p_filtering
diff --git a/9G-Train/cpm/generation/generation_utils.py b/9G-Train/cpm/generation/generation_utils.py
deleted file mode 100644
index 35c4cd9..0000000
--- a/9G-Train/cpm/generation/generation_utils.py
+++ /dev/null
@@ -1,127 +0,0 @@
-import torch
-import torch.nn.functional as F
-
-
-def top_k_top_p_filtering(logits, top_k=0, top_p=0.0, filter_value=-float("inf")):
- # This function has been mostly taken from huggingface conversational ai code at
- # https://medium.com/huggingface/how-to-build-a-state-of-the-art-conversational-ai-with-transfer-learning-2d818ac26313
-
- if top_k > 0:
- # Remove all tokens with a probability less than the last token of the top-k
- indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
- logits[indices_to_remove] = filter_value
-
- batch_size = logits.size()[0]
- if top_p > 0.0:
- logits = logits.view(batch_size, -1).contiguous()
- for index in range(len(logits)):
-
- sorted_logits, sorted_indices = torch.sort(logits[index].view(-1), descending=True)
- cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)
- # Remove tokens with cumulative probability above the threshold
- sorted_indices_to_remove = cumulative_probs > top_p
- # Shift the indices to the right to keep also the first token above the threshold
- sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
- sorted_indices_to_remove[..., 0] = 0
- indices_to_remove = sorted_indices[sorted_indices_to_remove]
- logits[index][indices_to_remove] = filter_value
-
- logits = logits.view(batch_size, -1).contiguous()
-
- return logits
-
-
-def apply_repetition_penalty(
- logits,
- batch_size,
- num_beams,
- prev_output_tokens,
- repetition_penalty,
- start_idx=None,
- end_idx=None,
- window_size=None,
-):
- # only conduct repetition penalty for the output
- assert repetition_penalty >= 1, "repetition penalty coefficient should >= 1"
- # repetition penalty (from CTRL paper https://arxiv.org/abs/1909.05858)
- for i in range(batch_size * num_beams):
- if start_idx is None or end_idx is None:
- output_tokens = prev_output_tokens[i].tolist()
- else:
- if end_idx >= start_idx:
- if window_size:
- output_tokens = prev_output_tokens[i][
- max(start_idx, end_idx + 1 - window_size) : end_idx + 1
- ].tolist()
- else:
- output_tokens = prev_output_tokens[i][start_idx : end_idx + 1].tolist()
- else:
- output_tokens = []
- for previous_token in set(output_tokens):
- # if score < 0 then repetition penalty has to
- # multiplied to reduce the previous token probability
- if logits[i, previous_token] < 0:
- logits[i, previous_token] *= repetition_penalty
- else:
- logits[i, previous_token] /= repetition_penalty
-
-
-class BeamHypotheses:
- def __init__(self, n_hyp, max_len, length_penalty, early_stopping):
- """
- Initialize n-best list of hypotheses.
- """
- self.max_len = max_len
- self.length_penalty = length_penalty
- self.early_stopping = early_stopping
- self.n_hyp = n_hyp
- self.hyp = []
- self.worst_score = 1e9
-
- def __len__(self):
- """
- Number of hypotheses in the list.
- """
- return len(self.hyp)
-
- def add(self, hyp, sum_logprobs):
- """
- Add a new hypothesis to the list.
- """
- score = sum_logprobs / len(hyp) ** self.length_penalty
-
- if len(self) < self.n_hyp or score > self.worst_score:
- self.hyp.append((score, hyp))
- if len(self) > self.n_hyp:
- sorted_scores = sorted([(s, idx) for idx, (s, _) in enumerate(self.hyp)])
- del self.hyp[sorted_scores[0][1]]
- self.worst_score = sorted_scores[1][0]
- else:
- self.worst_score = min(score, self.worst_score)
-
- def is_done(self, best_sum_logprobs, cur_len):
- """
- If there are enough hypotheses and that none of the hypotheses being generated
- can become better than the worst one in the heap, then we are done with this sentence.
- """
- if len(self) < self.n_hyp:
- return False
- elif self.early_stopping:
- return True
- else:
- return self.worst_score >= best_sum_logprobs / cur_len**self.length_penalty
-
-
-def NBCE(logits):
- """
- Naive Bayes-based Context Extension
- blog: https://www.kexue.fm/archives/9617
- """
- beta = 0.25
- logits = logits[:, -1] # bsh -> bh
- logits = logits - logits.logsumexp(dim=-1, keepdims=True)
- k = (logits.exp() * logits).sum(dim=-1)[1:].argmax() + 1
- logits_max = logits[k]
- logits_uncond = logits[0]
- logits = (1 + beta) * logits_max - beta * logits_uncond
- return logits
diff --git a/9G-Train/cpm/native_layers/__init__.py b/9G-Train/cpm/native_layers/__init__.py
deleted file mode 100644
index accf518..0000000
--- a/9G-Train/cpm/native_layers/__init__.py
+++ /dev/null
@@ -1,12 +0,0 @@
-from .attention import Attention
-from .blocks import TransformerBlock
-from .embedding import Embedding
-from .embedding import EmbeddingExt
-from .feedforward import FeedForward
-from .layernorm import LayerNorm
-from .linear import Linear
-from .position_embedding import BucketPositionBias
-from .position_embedding import RotaryEmbedding
-from .position_embedding import RotaryEmbeddingESM
-from .position_embedding import SegmentPositionEmbedding
-from .transformer import Encoder
diff --git a/9G-Train/cpm/native_layers/attention.py b/9G-Train/cpm/native_layers/attention.py
deleted file mode 100644
index 0b01d57..0000000
--- a/9G-Train/cpm/native_layers/attention.py
+++ /dev/null
@@ -1,134 +0,0 @@
-import math
-from typing import Optional
-from typing import Tuple
-
-import torch
-from einops import rearrange
-
-from .linear import Linear
-
-
-class Attention(torch.nn.Module):
- def __init__(
- self,
- dim_model: int,
- num_heads: int,
- num_kv_heads: int,
- dim_head: int,
- dtype: torch.dtype = torch.half,
- dropout_p: Optional[float] = None,
- use_flash_attn: bool = False,
- scale: bool = True,
- ) -> None:
- super().__init__()
-
- self.dim_model = dim_model
- self.num_heads = num_heads
- self.dim_head = dim_head
- self.num_kv_heads = num_kv_heads
- self.head_groups = num_heads // num_kv_heads
-
- self.project_q = Linear(self.dim_model, self.num_heads * self.dim_head, dtype=dtype, scale=scale)
- self.project_k = Linear(self.dim_model, self.num_kv_heads * self.dim_head, dtype=dtype, scale=scale)
- self.project_v = Linear(self.dim_model, self.num_kv_heads * self.dim_head, dtype=dtype, scale=scale)
-
- self.attention_out = Linear(self.num_heads * self.dim_head, self.dim_model, dtype=dtype, scale=scale)
-
- self.softmax = torch.nn.Softmax(dim=-1)
-
- if dropout_p is not None:
- self.dropout = torch.nn.Dropout(p=dropout_p)
- else:
- self.dropout = None
-
- # if use_flash_attn:
- # self.core_attention_flash = FlashSelfAttention(causal=False, attention_dropout=0.0)
- # self.use_flash_attn = use_flash_attn
-
- def forward(
- self,
- hidden_q: torch.Tensor,
- hidden_kv: torch.Tensor,
- attention_mask: torch.BoolTensor,
- position_bias: torch.Tensor,
- use_cache: bool = False,
- past_kv: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
- pos_bias_type: Optional[str] = "relative",
- length_mask: Optional[torch.Tensor] = None,
- context_mask: Optional[torch.Tensor] = None,
- ):
- """
- Args:
- hidden_q (:obj:`torch.Tensor` of shape ``(batch, len_q, dim_model)``): Indices of input sequence tokens. It will be embedded by model's internal embedding lookup matrix.
- hidden_kv (:obj:`torch.Tensor` of shape ``(batch, len_k, dim_model)``): Length of input sequence before padding.
- attention_mask (:obj:`torch.Tensor` of shape ``(batch, len_q, len_k)``): Used to avoid performing attention on padding token indices.
- position_bias(:obj:`torch.Tensor` of shape ``(num_heads, len_q, len_k)`` or ``(1, num_heads, len_k, len_q)``): Provide positional information about tensor `key_value` and `query`.
- Return:
- out (:obj:`torch.Tensor` of shape ``(batch, len_q, dim_model)``): The attention output.
- """ # noqa: E501
-
- batch_size = hidden_q.size(0)
- len_q = hidden_q.size(1)
- len_k = hidden_kv.size(1)
-
- h_q = self.project_q(hidden_q)
- h_k = self.project_k(hidden_kv)
- h_v = self.project_v(hidden_kv)
-
- h_q = h_q / math.sqrt(math.sqrt(self.dim_head))
- h_k = h_k / math.sqrt(math.sqrt(self.dim_head))
-
- h_q = h_q.view(batch_size, len_q, self.num_heads, self.dim_head).permute(0, 2, 1, 3)
- h_k = h_k.view(batch_size, len_k, self.num_kv_heads, self.dim_head).permute(0, 2, 1, 3)
- h_v = h_v.view(batch_size, len_k, self.num_kv_heads, self.dim_head).permute(0, 2, 1, 3)
-
- if pos_bias_type == "rotary":
- # b h s d
- h_q, h_k = position_bias(h_q, h_k, -2, offset=past_kv[0].size(-2) if past_kv is not None else 0)
-
- if past_kv is not None:
- h_k = torch.cat([past_kv[0], h_k], dim=-2)
- h_v = torch.cat([past_kv[1], h_v], dim=-2)
- len_k = h_k.size(-2)
-
- # (b, n_h, len_q, d_h) @ (b, n_h, d_h, len_k) -> (b, n_h, len_q, len_k)
- if self.head_groups == 1:
- score = torch.matmul(h_q, h_k.transpose(-1, -2)) # / math.sqrt(self.dim_head) moved to line 75~76
- else:
- score = torch.matmul(
- h_q.reshape(batch_size, self.num_kv_heads, self.head_groups * len_q, self.dim_head),
- h_k.transpose(-1, -2),
- ).view(batch_size, self.num_heads, len_q, len_k)
-
- if pos_bias_type == "relative":
- score = score + position_bias
- score = torch.masked_fill(
- score,
- attention_mask.view(batch_size, 1, len_q, len_k) == False,
- torch.scalar_tensor(float("-inf"), device=score.device, dtype=score.dtype),
- )
-
- score = self.softmax(score)
-
- score = torch.masked_fill(
- score,
- attention_mask.view(batch_size, 1, len_q, len_k) == False,
- torch.scalar_tensor(0, device=score.device, dtype=score.dtype),
- )
-
- if self.dropout is not None:
- score = self.dropout(score)
-
- # (b, n_kv_h, n_h_groups*len_q, len_k) @ (b, n_kv_h, len_k, d_h) -> (b, n_kv_h, n_h_groups*len_q, d_h) -> (b, n_h, len_q, d_h)
- score = torch.matmul(score.view(batch_size, self.num_kv_heads, self.head_groups * len_q, len_k), h_v).view(
- batch_size, self.num_heads, len_q, self.dim_head
- )
-
- score = score.view(batch_size, self.num_heads, len_q, self.dim_head).permute(0, 2, 1, 3)
- score = score.contiguous().view(batch_size, len_q, self.num_heads * self.dim_head)
-
- score = self.attention_out(score)
- if use_cache:
- return score, (h_k, h_v)
- else:
- return score
diff --git a/9G-Train/cpm/native_layers/blocks.py b/9G-Train/cpm/native_layers/blocks.py
deleted file mode 100644
index ce29979..0000000
--- a/9G-Train/cpm/native_layers/blocks.py
+++ /dev/null
@@ -1,279 +0,0 @@
-from typing import Optional
-from typing import Tuple
-from typing import Union
-
-import torch
-
-from .attention import Attention
-from .feedforward import FeedForward
-from .layernorm import LayerNorm
-from .position_embedding import RotaryEmbedding
-from .position_embedding import RotaryEmbeddingESM
-
-
-class SelfAttentionBlock(torch.nn.Module):
- """The whole cross-attention block. A sequence of operation. Consists of layernorm, self-attention and residual connection.
-
- Args:
- dim_model (int): main dimension of modules in transformer blocks.
- num_heads (int): num_heads used in :py:class:`model_center.layer.Attention`.
- dim_head (int): dim_head used in :py:class:`model_center.layer.Attention`.
- dtype (optional): Defaults to torch.half.
- eps (float, optional): eps used in :py:class:`model_center.layer.LayerNorm`. Defaults to 1e-5.
- dropout_p (float, optional): Defaults to 0.
- """ # noqa: E501
-
- def __init__(
- self,
- dim_model: int,
- num_heads: int,
- dim_head: int,
- num_kv_heads: int,
- dtype=torch.half,
- eps: float = 1e-6,
- dropout_p: Optional[float] = None,
- scale: bool = True,
- use_flash_attn: bool = False,
- ):
- super().__init__()
-
- self.layernorm_before_attention = LayerNorm(
- dim_model,
- dtype=dtype,
- eps=eps,
- )
-
- self.self_attention = Attention(
- dim_model=dim_model,
- num_heads=num_heads,
- num_kv_heads=num_kv_heads,
- dim_head=dim_head,
- dtype=dtype,
- dropout_p=dropout_p,
- scale=scale,
- use_flash_attn=use_flash_attn,
- )
-
- if dropout_p:
- self.dropout = torch.nn.Dropout(dropout_p)
- else:
- self.dropout = None
-
- def forward(
- self,
- hidden_states: torch.Tensor,
- attention_mask: torch.Tensor,
- position_bias: Union[torch.Tensor, RotaryEmbedding, RotaryEmbeddingESM] = None,
- use_cache: bool = False,
- past_key_value: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
- pos_bias_type: Optional[str] = "relative",
- length_mask: Optional[torch.Tensor] = None,
- context_mask: Optional[torch.Tensor] = None,
- ):
- """
- Args:
- hidden_states (:obj:`torch.Tensor` of shape ``(batch, seq_self, dim_model)``): Input of self-attention block. It can be the embedding of a batch of sequences.
- attention_mask (:obj:`torch.Tensor` of shape ``(batch, seq_self, seq_self)``): Avoid invalid areas to participate in the calculation.
- position_bias (:obj:`torch.Tensor` of shape ``(num_heads, seq_self, seq_self)``): Provide positional information to self-attention block.
-
- Return:
- :obj:`torch.Tensor` of shape ``(batch, seq_self, dim_model)``: The output of attention block.
-
- """ # noqa: E501
- x = self.layernorm_before_attention(hidden_states)
- x = self.self_attention(
- x,
- x,
- attention_mask,
- position_bias,
- use_cache,
- past_key_value,
- pos_bias_type=pos_bias_type,
- length_mask=length_mask,
- context_mask=context_mask,
- )
- if use_cache:
- x, current_key_value = x
- else:
- current_key_value = None
-
- if self.dropout is not None:
- x = self.dropout(x)
-
- hidden_states = hidden_states + x
-
- if use_cache:
- return hidden_states, current_key_value
- else:
- return hidden_states
-
-
-class FFNBlock(torch.nn.Module):
- """The whole feed-forward block. A sequence of operation. Consists of layernorm, feed-forward and residual connection.
-
- Args:
- dim_model (int): main dimension of modules in transformer blocks.
- dim_ff (int): dim_ff used in :py:class:`model_center.layer.FeedForward`.
- dtype (optional): Defaults to torch.half.
- eps (float, optional): eps used in :py:class:`model_center.layer.LayerNorm`. Defaults to 1e-5.
- dropout_p (float, optional): Defaults to 0.
- """ # noqa: E501
-
- def __init__(
- self,
- dim_model: int,
- dim_ff: int,
- activate_fn: str,
- dtype=torch.half,
- eps: float = 1e-6,
- dropout_p: Optional[float] = 0,
- scale: bool = True,
- ):
- super().__init__()
-
- self.layernorm_before_ffn = LayerNorm(
- dim_model,
- dtype=dtype,
- eps=eps,
- )
-
- self.ffn = FeedForward(
- dim_model,
- dim_ff,
- activate_fn=activate_fn,
- dtype=dtype,
- dropout_p=dropout_p,
- scale=scale,
- )
-
- if dropout_p:
- self.dropout = torch.nn.Dropout(dropout_p)
- else:
- self.dropout = None
-
- def forward(
- self,
- hidden_states: torch.Tensor,
- ):
- """
- Args:
- hidden_states (:obj:`torch.Tensor` of shape ``(batch, seq_self, dim_model)``): Hidden states before feed forward layer.
-
- Return:
- :obj:`torch.Tensor` of shape ``(batch, seq_self, dim_model)``: The output of feed-forward block
-
- """ # noqa: E501
- x = self.layernorm_before_ffn(hidden_states)
- x = self.ffn(x)
- if self.dropout is not None:
- x = self.dropout(x)
-
- hidden_states = hidden_states + x
-
- return hidden_states
-
-
-class TransformerBlock(torch.nn.Module):
- """The whole transformer block. A sequence of operation. Consists of self-attention block[, cross-attention block] and feed-forward block.
-
- Args:
- dim_model (int): main dimension of modules in transformer blocks.
- dim_ff (int): dim_ff used in :py:class:`model_center.layer.FeedForward`.
- num_heads (int): num_heads used in :py:class:`model_center.layer.Attention`.
- dim_head (int): dim_head used in :py:class:`model_center.layer.Attention`.
- dtype (optional): Defaults to torch.half.
- eps (float, optional): eps used in :py:class:`model_center.layer.LayerNorm`. Defaults to 1e-5.
- dropout_p (float, optional): Defaults to 0.
- """ # noqa: E501
-
- def __init__(
- self,
- dim_model: int,
- dim_ff: int,
- num_heads: int,
- num_kv_heads: int,
- dim_head: int,
- activate_fn: str = "gelu",
- dtype=torch.half,
- eps: float = 1e-6,
- dropout_p: Optional[float] = None,
- scale: bool = True,
- mask_att: bool = False,
- mask_ffn: bool = False,
- use_flash_attn: bool = False,
- ):
- super().__init__()
- self.mask_att = mask_att
- self.mask_ffn = mask_ffn
-
- if not self.mask_att:
- self.self_att = SelfAttentionBlock(
- dim_model=dim_model,
- num_heads=num_heads,
- num_kv_heads=num_kv_heads,
- dim_head=dim_head,
- dtype=dtype,
- eps=eps,
- dropout_p=dropout_p,
- scale=scale,
- use_flash_attn=use_flash_attn,
- )
-
- if not self.mask_ffn:
- self.ffn = FFNBlock(
- dim_model=dim_model,
- dim_ff=dim_ff,
- activate_fn=activate_fn,
- dtype=dtype,
- eps=eps,
- dropout_p=dropout_p,
- scale=scale,
- )
-
- def forward(
- self,
- self_hidden_states: torch.Tensor,
- self_attention_mask: torch.Tensor,
- self_position_bias: Optional[torch.Tensor] = None,
- use_cache: bool = False,
- past_key_value: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
- pos_bias_type: Optional[str] = "relative",
- length_mask: Optional[torch.Tensor] = None,
- context_mask: Optional[torch.Tensor] = None,
- ):
- """
- Args:
- self_hidden_states (:obj:`torch.Tensor` of shape ``(batch, seq_self, dim_model)``): Input of transformer block(self-attention block). It can be the raw embedding of a batch of sequences.
- self_attention_mask (:obj:`torch.Tensor` of shape ``(batch, seq_self, seq_self)``): Avoid invalid areas to participate in the calculation of self-attention.
- self_position_bias (:obj:`torch.Tensor` of shape ``(num_heads, seq_self, seq_self)``): Provide positional information to self-attention block.
-
- Return:
- :obj:`torch.Tensor` of shape ``(batch, seq_self, dim_model)``: The output of transformer block.
-
- """ # noqa: E501
- # (batch, dim_model, seq_self)
- current_key_value = None
- if not self.mask_att:
- hidden_states = self.self_att(
- self_hidden_states,
- attention_mask=self_attention_mask,
- position_bias=self_position_bias,
- use_cache=use_cache,
- past_key_value=past_key_value,
- pos_bias_type=pos_bias_type,
- length_mask=length_mask,
- context_mask=context_mask,
- )
- if use_cache:
- hidden_states, current_key_value = hidden_states
- else:
- hidden_states = self_hidden_states
-
- # (batch, dim_model, seq_self)
- if not self.mask_ffn:
- hidden_states = self.ffn(hidden_states)
-
- if use_cache:
- return hidden_states, current_key_value
- else:
- return hidden_states
diff --git a/9G-Train/cpm/native_layers/embedding.py b/9G-Train/cpm/native_layers/embedding.py
deleted file mode 100644
index 3f03c85..0000000
--- a/9G-Train/cpm/native_layers/embedding.py
+++ /dev/null
@@ -1,100 +0,0 @@
-import math
-from typing import Optional
-
-import torch
-import torch.nn.functional as F
-
-from .position_embedding import RotaryEmbedding
-
-
-class Embedding(torch.nn.Module):
- def __init__(
- self,
- vocab_size: int,
- embedding_size: int,
- dtype: torch.dtype = torch.half,
- scale: bool = True,
- init_mean: float = 0.0,
- init_std: float = 1,
- ):
- super().__init__()
-
- self.dim_model = embedding_size
- self.weight = torch.nn.parameter.Parameter(torch.empty(vocab_size, embedding_size, dtype=dtype))
- self.scale = scale
-
- def forward(self, ids: torch.Tensor):
- """
- Args:
- ids (:obj:`torch.Tensor` of shape ``(batch_size, seq_len)``): Indices of input sequence tokens.
- Return:
- :obj:`torch.Tensor` of shape ``(batch_size, seq_len, embedding_size)``: The embedding output.
- """ # noqa: E501
-
- if self.scale:
- embeds = F.embedding(ids, self.weight) / math.sqrt(self.dim_model)
- else:
- embeds = F.embedding(ids, self.weight)
- return embeds.clone()
-
- def projection(self, x: torch.Tensor):
- """
- Projection based on embedding's weight. For example, embedding map vocab_size to embed_size, than projection map embed_size back to vocab_size.
- Args:
- x (:obj:`torch.Tensor` of shape ``(batch, seq_len, dim_model)``): Input of projection
- Returns:
- :obj:`torch.Tensor` of shape ``(batch, seq_len, vocab_output_size)``: The projection output.
- """ # noqa: E501
- if self.scale:
- logits = F.linear(x / math.sqrt(self.dim_model), self.weight)
- else:
- logits = F.linear(x, self.weight)
-
- return logits
-
-
-class EmbeddingExt(torch.nn.Module):
- def __init__(
- self,
- vocab_size: int,
- embedding_size: int,
- dtype: torch.dtype = torch.half,
- init_mean: float = 0.0,
- init_std: float = 1,
- distance_scale: int = 16,
- ):
- super().__init__()
-
- self.dim_model = embedding_size
- self.rotary_emb = RotaryEmbedding(dim=embedding_size, distance_scale=distance_scale, dtype=dtype)
-
- self.weight = torch.nn.parameter.Parameter(
- torch.empty(vocab_size, embedding_size, dtype=dtype),
- )
-
- def forward(self, ids: torch.Tensor, ids_sub: torch.Tensor):
- """
- Args:
- ids (:obj:`torch.Tensor` of shape ``(batch_size, seq_len)``): Indices of input sequence tokens.
- ids (:obj:`torch.Tensor` of shape ``(batch_size)``): Subscript of input sequence tokens.
- Return:
- :obj:`torch.Tensor` of shape ``(batch_size, seq_len, embedding_size)``: The embedding output.
- """ # noqa: E501
-
- embeds = F.embedding(ids, self.weight) / math.sqrt(self.dim_model)
- return self.rotary_emb(embeds, ids_sub)
-
- def projection(self, x: torch.Tensor, ext_table: Optional[torch.Tensor] = None):
- """
- Projection based on embedding's weight. For example, embedding map vocab_size to embed_size, than projection map embed_size back to vocab_size.
- Args:
- x (:obj:`torch.Tensor` of shape ``(batch, seq_len, dim_model)``): Input of projection
- ext_table (:obj:`torch.Tensor` of shape ``(ext_table_size, dim_model)``): Ext vocab table.
- Returns:
- :obj:`torch.Tensor` of shape ``(batch, seq_len, vocab_size + ext_table_size)``: The projection output.
- """ # noqa: E501
- logits = F.linear(x / math.sqrt(self.dim_model), self.weight)
- if ext_table is not None:
- logits_ext = F.linear(x, ext_table)
- logits = torch.cat([logits, logits_ext], dim=-1)
- return logits
diff --git a/9G-Train/cpm/native_layers/feedforward.py b/9G-Train/cpm/native_layers/feedforward.py
deleted file mode 100644
index c50077a..0000000
--- a/9G-Train/cpm/native_layers/feedforward.py
+++ /dev/null
@@ -1,120 +0,0 @@
-from typing import Optional
-
-import torch
-
-from .linear import Linear
-
-
-class DenseGatedACT(torch.nn.Module):
- def __init__(
- self,
- dim_in: int,
- dim_ff: int,
- dtype=torch.half,
- activate_fn: str = "gelu",
- scale: bool = True,
- ):
- super().__init__()
-
- self.w_0 = Linear(
- dim_in=dim_in,
- dim_out=dim_ff,
- dtype=dtype,
- scale=scale,
- scale_before=False,
- )
-
- self.w_1 = Linear(
- dim_in=dim_in,
- dim_out=dim_ff,
- dtype=dtype,
- scale=scale,
- scale_before=False,
- )
- if activate_fn == "gelu":
- self.act = torch.nn.GELU()
- elif activate_fn == "silu":
- self.act = torch.nn.functional.silu
- else:
- raise NotImplementedError(f"{activate_fn} is not supported")
-
- def forward(self, x: torch.Tensor):
- """Transform an input tensor from one feature space to another via a nonlinear operation
-
- Args:
- x (:obj:`torch.Tensor` of shape ``(batch, seq_len, dim_in)``): Tensor that will be subject to nonlinear operations.
-
- Return:
- out (:obj:`torch.Tensor` of shape ``(batch, seq_len, dim_ff)``)
-
- """ # noqa: E501
- gate_score = self.act(self.w_0(x))
- x = self.w_1(x)
-
- x = gate_score * x
- return x
-
-
-class FeedForward(torch.nn.Module):
- r"""FeedForward module
-
- Args:
- dim_in (int): input dimension.
- dim_ff (int): middle dimension.
- dim_out (int, optional): output dimension. Defaults to None, which means dim_in = dim_out.
- dtype (optional): Defaults to torch.half.
- init_mean (float, optional): mean of :math:`\mathbf{W}\sim\mathcal{N}(\text{mean}, \text{std}^2)` for fully-connected module used in feed-forward layer. Defaults to 0.
- init_std (float, optional): std of :math:`\mathbf{W}\sim\mathcal{N}(\text{mean}, \text{std}^2)` for fully-connected module used in feed-forward layer. Defaults to 0.02.
- bias (bool, optional): whether to use bias term in fully-connected layers used in feed-forward module. Defaults to False.
- activate_fn (str, optional): Defaults to `gated_gelu`.
- dropout_p (int, optional): Defaults to 0.
- """ # noqa: E501
-
- def __init__(
- self,
- dim_model: int,
- dim_ff: int,
- activate_fn: str = "gelu",
- dtype=torch.half,
- dropout_p: Optional[float] = None,
- scale: bool = True,
- ):
- super().__init__()
-
- self.w_in = DenseGatedACT(
- dim_in=dim_model,
- dim_ff=dim_ff,
- activate_fn=activate_fn,
- dtype=dtype,
- scale=scale,
- )
-
- if dropout_p is not None:
- self.dropout = torch.nn.Dropout(dropout_p)
- else:
- self.dropout = None
-
- self.w_out = Linear(
- dim_in=dim_ff,
- dim_out=dim_model,
- dtype=dtype,
- scale=scale,
- scale_before=False,
- )
-
- def forward(self, x: torch.Tensor):
- """
- Args:
- x (:obj:`torch.Tensor` of shape ``(batch, seq_len, dim_in)``): The input of feed-forward module.
-
- Return:
- :obj:`torch.Tensor` of shape ``(batch, seq_len, dim_out)``: The output of feed-forward module.
- """ # noqa: E501
- x = self.w_in(x)
-
- if self.dropout is not None:
- x = self.dropout(x)
-
- x = self.w_out(x)
-
- return x
diff --git a/9G-Train/cpm/native_layers/layernorm.py b/9G-Train/cpm/native_layers/layernorm.py
deleted file mode 100644
index 068ea12..0000000
--- a/9G-Train/cpm/native_layers/layernorm.py
+++ /dev/null
@@ -1,37 +0,0 @@
-import torch
-
-
-@torch.jit.script
-def rms_layernorm(hidden: torch.Tensor, weight: torch.Tensor, eps: float):
- old_dtype = hidden.dtype
- variance = hidden.to(torch.float32).pow(2).mean(dim=-1, keepdim=True)
- hidden = (hidden * torch.rsqrt(variance + eps)).to(old_dtype)
- return hidden * weight
-
-
-class LayerNorm(torch.nn.Module):
- """RMS LayerNorm"""
-
- def __init__(
- self,
- dim_norm: int,
- dtype: torch.dtype = torch.half,
- eps: float = 1e-6,
- init_var: float = 1.0,
- ):
-
- super().__init__()
-
- self.eps = eps
- self.dim_norm = dim_norm
- self.weight = torch.nn.parameter.Parameter(torch.full((dim_norm,), init_var, dtype=dtype))
-
- def forward(self, x: torch.Tensor):
- """
- Args:
- x (:obj:`torch.Tensor` of shape ``(batch_size, seq_len, dim_norm)``): Input tensor that need to be normalized.
- Return:
- :obj:`torch.Tensor` of shape ``(batch_size, seq_len, dim_norm)``: The layernorm output.
- """
- assert x.size(-1) == self.dim_norm
- return rms_layernorm(x, self.weight, self.eps)
diff --git a/9G-Train/cpm/native_layers/linear.py b/9G-Train/cpm/native_layers/linear.py
deleted file mode 100644
index 08743cb..0000000
--- a/9G-Train/cpm/native_layers/linear.py
+++ /dev/null
@@ -1,44 +0,0 @@
-import math
-
-import torch
-import torch.nn.functional as F
-
-
-class Linear(torch.nn.Module):
- def __init__(
- self,
- dim_in: int,
- dim_out: int,
- dtype: torch.dtype = torch.half,
- init_mean: float = 0.0,
- init_std: float = 1,
- scale: bool = True,
- scale_before: bool = False,
- ):
- super().__init__()
- self.dim_in = self.in_features = dim_in
- self.dim_out = self.out_features = dim_out
- self.scale = scale
- self.scale_before = scale_before
- self.weight = torch.nn.parameter.Parameter(torch.empty((dim_out, dim_in), dtype=dtype))
- torch.nn.init.normal_(self.weight, mean=init_mean, std=init_std)
-
- def forward(self, x: torch.Tensor):
- """
- Args:
- x (:obj:`torch.Tensor` of shape ``(batch, seq_len, dim_in)``): The input of linear layer
- Returns:
- :obj:`torch.Tensor` of shape ``(batch, seq_len, dim_out)``: The output of the linear transform y.
- """ # noqa: E501
- if self.scale:
- if self.scale_before:
- x = x / math.sqrt(self.dim_in)
-
- x = F.linear(x, self.weight)
- else:
- x = F.linear(x, self.weight)
- x = x / math.sqrt(self.dim_in)
-
- else:
- x = F.linear(x, self.weight)
- return x
diff --git a/9G-Train/cpm/native_layers/position_embedding.py b/9G-Train/cpm/native_layers/position_embedding.py
deleted file mode 100644
index cf3426b..0000000
--- a/9G-Train/cpm/native_layers/position_embedding.py
+++ /dev/null
@@ -1,286 +0,0 @@
-import math
-from typing import Tuple
-from typing import Union
-
-import torch
-import torch.nn.functional as F
-
-
-class SegmentPositionEmbedding(torch.nn.Module):
- def __init__(
- self,
- num_heads: int,
- num_segments: int = 1,
- num_buckets: int = 32,
- max_distance: int = 128,
- bidirectional: bool = False,
- dtype: torch.dtype = torch.half,
- init_mean: float = 0.0,
- init_std: float = 1,
- ):
- super().__init__()
-
- self.num_heads = num_heads
- self.num_buckets = num_buckets
- self.max_distance = max_distance
- self.bidirectional = bidirectional
- self.num_segments = num_segments
-
- self.relative_attention_bias = torch.nn.parameter.Parameter(
- torch.empty(num_segments * num_segments + num_buckets, num_heads, dtype=dtype)
- )
-
- def forward(
- self,
- key_pos: torch.Tensor,
- query_pos: torch.Tensor,
- key_segment: torch.Tensor,
- query_segment: torch.Tensor,
- ):
- with torch.no_grad():
- batch = key_pos.size(0)
- keylen = key_pos.size(1)
- querylen = query_pos.size(1)
-
- assert key_pos.size(0) == query_pos.size(0)
- assert keylen == key_segment.size(1) and querylen == query_segment.size(1)
-
- key_pos = key_pos.view(batch, -1, keylen)
- query_pos = query_pos.view(batch, querylen, -1)
- key_segment = key_segment.view(batch, -1, keylen)
- query_segment = query_segment.view(batch, querylen, -1)
-
- relative_position_bucket = self._segment_relative_position_bucket(query_segment, key_segment)
- relative_position_bucket = relative_position_bucket + self.num_buckets # 与相对位置编码区间不重叠
-
- # b*q*k
- absolute_position_bucket = self._position_bucket(
- torch.arange(keylen, dtype=torch.int32, device=relative_position_bucket.device)[None, :]
- - torch.arange(querylen, dtype=torch.int32, device=relative_position_bucket.device)[:, None],
- bidirectional=self.bidirectional,
- num_buckets=self.num_buckets,
- max_distance=self.max_distance,
- )
- relative_position_bucket = torch.where(
- (key_segment == query_segment),
- absolute_position_bucket[None, :, :],
- relative_position_bucket,
- )
- # (batch, len_q, len_k)
-
- # (batch, len_q, len_k, num_heads)
- embeds = F.embedding(relative_position_bucket, self.relative_attention_bias)
- # (batch, num_heads, len_q, len_k)
- embeds = embeds.permute(0, 3, 1, 2).contiguous()
- return embeds
-
- def _segment_relative_position_bucket(self, query_segment, key_segment):
- return query_segment * self.num_segments + key_segment
-
- def _position_bucket(self, relative_position, bidirectional=True, num_buckets=32, max_distance=128):
- relative_buckets = 0
- if bidirectional:
- num_buckets //= 2
- relative_buckets = (relative_position > 0).to(torch.int32) * num_buckets
- relative_position = torch.abs(relative_position)
- else:
- relative_position = -torch.min(relative_position, torch.zeros_like(relative_position))
- max_exact = num_buckets // 2
- is_small = relative_position < max_exact
- relative_postion_if_large = max_exact + (
- torch.log(relative_position.float() / max_exact)
- / math.log(max_distance / max_exact)
- * (num_buckets - max_exact)
- ).to(torch.int32)
- relative_postion_if_large = torch.min(
- relative_postion_if_large,
- torch.full_like(relative_postion_if_large, num_buckets - 1),
- )
- relative_buckets += torch.where(is_small, relative_position.to(torch.int32), relative_postion_if_large)
- return relative_buckets
-
-
-class BucketPositionBias(torch.nn.Module):
- def __init__(
- self,
- num_heads: int,
- num_buckets: int = 32,
- num_segment_bucket: int = 32,
- max_distance: int = 128,
- dtype: torch.dtype = torch.half,
- init_mean: float = 0.0,
- init_std: float = 1,
- ) -> None:
- super().__init__()
-
- self.num_heads = num_heads
- self.num_buckets = num_buckets
- self.num_segment_bucket = num_segment_bucket
- self.max_distance = max_distance
-
- self.relative_attention_bias = torch.nn.parameter.Parameter(
- torch.empty(num_buckets + num_segment_bucket, num_heads, dtype=dtype)
- )
-
- def forward(
- self,
- query_pos: torch.Tensor, # (batch, len_q)
- key_pos: torch.Tensor, # (batch, len_k)
- rel_buckets: torch.Tensor, # (batch, len_q, len_k)
- ):
- with torch.no_grad():
- batch = key_pos.size(0)
- keylen = key_pos.size(1)
- querylen = query_pos.size(1)
-
- assert key_pos.size(0) == query_pos.size(0)
- assert rel_buckets.size(0) == batch and rel_buckets.size(1) == querylen and rel_buckets.size(2) == keylen
-
- relative_position_bucket = rel_buckets - 1 + self.num_buckets # 与相对位置编码区间不重叠
-
- # b*q*k
- inner_segment_bucket = self._position_bucket(
- key_pos[..., None, :] - query_pos[..., :, None],
- num_buckets=self.num_buckets,
- max_distance=self.max_distance,
- )
- relative_position_bucket = torch.where(
- rel_buckets == 0,
- inner_segment_bucket,
- relative_position_bucket,
- )
- # (batch, len_q, len_k)
-
- # (batch, len_q, len_k, num_heads)
- embeds = F.embedding(relative_position_bucket, self.relative_attention_bias)
- # (batch, num_heads, len_q, len_k)
- embeds = embeds.permute(0, 3, 1, 2).contiguous()
- return embeds
-
- def _position_bucket(self, relative_position, num_buckets=32, max_distance=128):
- relative_buckets = 0
- num_buckets //= 2
- relative_buckets = (relative_position > 0).to(torch.int32) * num_buckets
- relative_position = torch.abs(relative_position)
-
- max_exact = num_buckets // 2
- is_small = relative_position < max_exact
- relative_postion_if_large = max_exact + (
- torch.log(relative_position.float() / max_exact)
- / math.log(max_distance / max_exact)
- * (num_buckets - max_exact)
- ).to(torch.int32)
- relative_postion_if_large = torch.min(
- relative_postion_if_large,
- torch.full_like(relative_postion_if_large, num_buckets - 1),
- )
- relative_buckets += torch.where(is_small, relative_position.to(torch.int32), relative_postion_if_large)
- return relative_buckets
-
-
-class RotaryEmbedding(torch.nn.Module):
- def __init__(
- self,
- dim,
- base=10000,
- distance_scale: Union[int, float] = 1,
- dtype: torch.dtype = torch.half,
- ):
- super().__init__()
- inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, device="cuda", dtype=torch.float32) / dim))
- inv_freq = inv_freq.to(dtype)
- self.distance_scale = distance_scale
- self.dtype = dtype
- self.inv_freq = inv_freq
-
- def forward(self, x: torch.Tensor, x_pos: torch.Tensor):
- """
- Args:
- x (:obj:`torch.Tensor` of shape ``(..., dim)``): Inputs.
- x_pos (:obj:`torch.Tensor` of shape ``(...)``): Positions of inputs.
- """
- x_pos = x_pos * self.distance_scale
- freqs = x_pos[..., None].to(self.dtype) * self.inv_freq[None, :] # (..., dim/2)
-
- # the same implementation as sat
- emb = torch.cat((freqs, freqs), dim=-1) # (..., dim)
- emb_cos = emb.cos() # (..., dim)
- emb_sin = emb.sin() # (..., dim)
-
- rotate_x = torch.cat([-x[..., x.size(-1) // 2 :], x[..., : x.size(-1) // 2]], dim=-1) # (..., dim)
-
- return x * emb_cos + rotate_x * emb_sin
-
-
-def rotate_half(x):
- x1, x2 = x.chunk(2, dim=-1)
- return torch.cat((-x2, x1), dim=-1)
-
-
-def apply_rotary_pos_emb(x, cos, sin, seq_dim, offset):
- if x.size(seq_dim) < cos.size(seq_dim):
- cos = cos.narrow(seq_dim, offset, x.size(seq_dim))
- sin = sin.narrow(seq_dim, offset, x.size(seq_dim))
- return (x * cos) + (rotate_half(x) * sin)
-
-
-class RotaryEmbeddingESM(torch.nn.Module):
- """
- Rotary position embeddings based on those in
- [RoFormer](https://huggingface.co/docs/transformers/model_doc/roformer). Query and keys are transformed by rotation
- matrices which depend on their relative positions.
- """
-
- def __init__(
- self,
- dim: int,
- base: Union[int, float] = 10000,
- distance_scale: Union[int, float] = 1,
- dtype=torch.half,
- persistent=True,
- mixed_precision=False,
- ):
- super().__init__()
- self.base = base
- self.distance_scale = distance_scale
- self.dtype = dtype
-
- # Generate and save the inverse frequency buffer (non trainable)
- inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, device="cuda", dtype=torch.float32) / dim))
- if mixed_precision:
- self.register_buffer("inv_freq", inv_freq, persistent=persistent)
- else:
- self.register_buffer("inv_freq", inv_freq.to(self.dtype), persistent=persistent)
-
- self._seq_len_cached = -1
- self._cos_cached = None
- self._sin_cached = None
- self.mixed_precision = mixed_precision
-
- self.apply_rotary_pos_emb = apply_rotary_pos_emb
-
- def _update_cos_sin_tables(self, x, seq_dim, offset):
- seq_len = x.size(seq_dim) + offset
- if seq_len > self._seq_len_cached or self._cos_cached.device != x.device:
- self._seq_len_cached = seq_len
- t = torch.arange(seq_len, device=x.device).type_as(self.inv_freq)
- freqs = torch.outer(t * self.distance_scale, self.inv_freq)
- emb = torch.cat((freqs, freqs), dim=-1)
- for i in range(x.dim() - 1):
- if i != seq_dim:
- emb = emb.unsqueeze_(i)
- if self.mixed_precision:
- self._cos_cached = emb.cos().to(self.dtype)
- self._sin_cached = emb.sin().to(self.dtype)
- else:
- self._cos_cached = emb.cos()
- self._sin_cached = emb.sin()
- return self._cos_cached, self._sin_cached
-
- def forward(self, q: torch.Tensor, k: torch.Tensor, seq_dim, offset=0) -> Tuple[torch.Tensor, torch.Tensor]:
- seq_dim = (seq_dim + k.dim()) % k.dim()
- self._cos_cached, self._sin_cached = self._update_cos_sin_tables(k, seq_dim, offset)
- return (
- self.apply_rotary_pos_emb(q, self._cos_cached, self._sin_cached, seq_dim, offset),
- self.apply_rotary_pos_emb(k, self._cos_cached, self._sin_cached, seq_dim, offset),
- )
diff --git a/9G-Train/cpm/native_layers/transformer.py b/9G-Train/cpm/native_layers/transformer.py
deleted file mode 100644
index f08cd51..0000000
--- a/9G-Train/cpm/native_layers/transformer.py
+++ /dev/null
@@ -1,132 +0,0 @@
-from typing import List
-from typing import Optional
-from typing import Tuple
-
-import torch
-
-from .blocks import TransformerBlock
-from .layernorm import LayerNorm
-
-
-class Encoder(torch.nn.Module):
- """Layers of encoder transformer blocks plus an final layernorm.
-
- Args:
- num_layers (int): number of layers.
- dim_model (int): main dimension of modules in transformer blocks.
- dim_ff (int): dim_ff used in :py:class:`model_center.layer.FeedForward`.
- num_heads (int): num_heads used in :py:class:`model_center.layer.Attention`.
- dim_head (int): dim_head used in :py:class:`model_center.layer.Attention`.
- dtype (optional): Defaults to torch.half.
- eps (float, optional): eps used in :py:class:`model_center.layer.LayerNorm`. Defaults to 1e-6.
- dropout_p (float, optional): Defaults to 0.
- """
-
- def __init__(
- self,
- num_layers: int,
- dim_model: int,
- dim_ff: int,
- num_heads: int,
- dim_head: int,
- num_kv_heads: int = -1,
- activate_fn: str = "gelu",
- dtype: torch.dtype = torch.half,
- eps: float = 1e-6,
- dropout_p: Optional[float] = None,
- scale: bool = True,
- mask_modules: Optional[List[Tuple[bool, bool]]] = None,
- use_flash_attn: bool = False,
- ):
- super().__init__()
- if num_kv_heads == -1:
- num_kv_heads = num_heads
- self.num_layers = num_layers
-
- if mask_modules is not None:
- assert len(mask_modules) == num_layers, "The total number of masks should equal to num_layers"
- for mask_module in mask_modules:
- assert len(mask_module) == 2, "For encoder, each mask should be (mask_att, mask_ffn)"
- else:
- mask_modules = [(False, False)] * num_layers
-
- self.layers = torch.nn.ModuleList(
- [
- TransformerBlock(
- dim_model=dim_model,
- dim_ff=dim_ff,
- num_heads=num_heads,
- num_kv_heads=num_kv_heads,
- dim_head=dim_head,
- activate_fn=activate_fn,
- dtype=dtype,
- eps=eps,
- dropout_p=dropout_p,
- scale=scale,
- mask_att=mask_modules[ith][0],
- mask_ffn=mask_modules[ith][1],
- use_flash_attn=use_flash_attn,
- )
- for ith in range(num_layers)
- ]
- )
-
- self.output_layernorm = LayerNorm(dim_norm=dim_model, dtype=dtype, eps=eps)
-
- def forward(
- self,
- hidden_states: torch.Tensor,
- attention_mask: torch.Tensor,
- position_bias: torch.Tensor,
- use_cache: bool = False,
- past_key_values: Optional[List[Tuple[torch.Tensor, torch.Tensor]]] = None,
- pos_bias_type: Optional[str] = "relative",
- length_mask: Optional[torch.Tensor] = None,
- context_mask: Optional[torch.Tensor] = None,
- ):
- """
- Args:
- hidden-states (:obj:`torch.Tensor` of shape ``(batch, seq_enc, dim_model)``): Input of encoder, might be the embedding of a batch of sequences.
- attention_mask (:obj:`torch.Tensor` of shape ``(batch, seq_enc, seq_enc)``): Avoid invalid areas to participate in the calculation
- position_bias(:obj:`torch.Tensor` of shape ``(num_heads, seq_enc, seq_enc)``) Provides position information to attention mechanism.
-
- Return:
- :obj:`torch.Tensor` of shape ``(batch, seq_enc, dim_model)``: The encoder output.
-
- """
- if not use_cache:
- for layer in self.layers:
- hidden_states = layer(
- hidden_states,
- attention_mask,
- position_bias,
- pos_bias_type=pos_bias_type,
- length_mask=length_mask,
- context_mask=context_mask,
- )
- hidden_states = self.output_layernorm(hidden_states)
- return hidden_states
- else:
- with torch.no_grad():
- current_key_values = []
- current_hidden_states = []
- for i, module in enumerate(self.layers):
- hidden_states = module(
- hidden_states,
- attention_mask,
- position_bias,
- past_key_value=past_key_values[i] if past_key_values else None,
- use_cache=use_cache,
- pos_bias_type=pos_bias_type,
- length_mask=length_mask,
- context_mask=context_mask,
- )
- if use_cache:
- current_key_values.append(hidden_states[1])
- current_hidden_states.append(hidden_states[0])
- hidden_states = hidden_states[0]
- hidden_states = self.output_layernorm(hidden_states)
- if use_cache:
- return hidden_states, current_key_values, current_hidden_states
- else:
- return hidden_states
diff --git a/9G-Train/cpm/utils/data_utils.py b/9G-Train/cpm/utils/data_utils.py
deleted file mode 100644
index 3c94dab..0000000
--- a/9G-Train/cpm/utils/data_utils.py
+++ /dev/null
@@ -1,62 +0,0 @@
-import torch
-
-
-def pad(orig_items, key, padding_value=0, padding_side="left"):
- items = []
- if isinstance(orig_items[0][key], list):
- assert isinstance(orig_items[0][key][0], torch.Tensor)
- for it in orig_items:
- for tr in it[key]:
- items.append({key: tr})
- else:
- assert isinstance(orig_items[0][key], torch.Tensor)
- items = orig_items
-
- batch_size = len(items)
- shape = items[0][key].shape
- dim = len(shape)
- assert dim <= 3
- max_length = max(item[key].shape[-1] for item in items)
- min_length = min(item[key].shape[-1] for item in items)
- dtype = items[0][key].dtype
-
- if dim == 1:
- return torch.cat([item[key] for item in items], dim=0)
- elif dim == 2:
- if max_length == min_length:
- return torch.cat([item[key] for item in items], dim=0)
- tensor = torch.zeros((batch_size, max_length), dtype=dtype) + padding_value
- else:
- tensor = torch.zeros((batch_size, max_length, shape[-1]), dtype=dtype) + padding_value
-
- for i, item in enumerate(items):
- if dim == 2:
- if padding_side == "left":
- tensor[i, -len(item[key][0]) :] = item[key][0].clone()
- else:
- tensor[i, : len(item[key][0])] = item[key][0].clone()
- elif dim == 3:
- if padding_side == "left":
- tensor[i, -len(item[key][0]) :, :] = item[key][0].clone()
- else:
- tensor[i, : len(item[key][0]), :] = item[key][0].clone()
-
- return tensor
-
-
-def pad_raw(orig_items, max_length=1024, padding_value=0, padding_side="left"):
- max_cols = max(tensor.size(1) for tensor in orig_items)
-
- padded_arrays = []
- for tensor in orig_items:
- pad_cols = max_cols - tensor.size(1)
- if padding_side == "left":
- padded_tensor = torch.cat([torch.zeros(tensor.size(0), pad_cols), tensor], dim=1)
- elif padding_side == "right":
- padded_tensor = torch.cat([tensor, torch.zeros(tensor.size(0), pad_cols)], dim=1)
- else:
- raise ValueError("Invalid 'side' parameter. Must be 'left' or 'right'.")
- padded_arrays.append(padded_tensor)
-
- padded_tensor = torch.cat(padded_arrays, dim=0).to(dtype=torch.int32)
- return padded_tensor
diff --git a/9G-Train/cpm/utils/exporter.py b/9G-Train/cpm/utils/exporter.py
deleted file mode 100644
index 9be007c..0000000
--- a/9G-Train/cpm/utils/exporter.py
+++ /dev/null
@@ -1,130 +0,0 @@
-import functools
-import json
-import os
-import shutil
-import time
-from typing import List
-
-import bmtrain as bmt
-import torch
-
-from .log import logger
-
-
-def rename_if_exists(file_path):
- if not os.path.exists(file_path):
- return
- timestamp = time.strftime("%Y%m%d%H%M%S")
- file_dir, file_name = os.path.split(file_path)
- file_root, file_ext = os.path.splitext(file_name)
- new_file_name = f"{file_root}_bak_{timestamp}{file_ext}"
- new_file_path = os.path.join(file_dir, new_file_name)
- try:
- os.rename(file_path, new_file_path)
- logger.info(f"File '{file_name}' already exists. Renamed to '{new_file_name}'")
- except Exception as e:
- logger.warn(
- "rename file failed,file_path={file_path}, new_file_path={new_file_path},err={err}".format(
- file_path=file_path, new_file_path=new_file_path, err=str(e)
- )
- )
-
-
-def rename_if_exists_decorator(func):
- @functools.wraps(func)
- def wrapper(file_path, *args, **kwargs):
- rename_if_exists(file_path)
- return func(file_path, *args, **kwargs)
-
- return wrapper
-
-
-@rename_if_exists_decorator
-def bmt_save(file_path: str, model: torch.nn.Module, export_files: List[str] = None):
- bmt.save(model, file_path)
- if export_files is not None:
- export_files.append(file_path)
-
-
-@rename_if_exists_decorator
-def torch_save(file_path: str, obj: object, export_files: List[str] = None):
- torch.save(obj, file_path)
- if export_files is not None:
- export_files.append(file_path)
-
-
-@rename_if_exists_decorator
-def json_save(file_path: str, obj: object, export_files: List[str] = None):
- with open(file_path, "w") as data_f:
- json.dump(obj, data_f)
- if export_files is not None:
- export_files.append(file_path)
-
-
-def export(
- model: torch.nn.Module, dataloader, optimizer: bmt.optim.AdamOffloadOptimizer, global_step, args, final_save=False
-):
- """
- 一次 ckpt 保存:
- /{args.save}/
- ├── {save_name}-{global_step}.rank-0.opt
- ├── {save_name}-{global_step}.rank-n.opt
- ├── job_{job_id}_ckpt_{global_step}/ # checkpoint 导出为模型版本时,job_{job_id}_ckpt_{global_step}/ 路径下文件会一起导出,创建一个模型组版本
- ├── config.json
- ├── vocabs.txt
- ├── {args.save_name}-{global_step}.pt
- ├── {args.save_name}-{global_step}.data
- ├── {args.save_name}-{global_step}.data.json
- └── {args.save_name}-{global_step}.success
-
- """
- export_model_dir = os.path.join(args.save, f"l_{global_step}")
- os.makedirs(export_model_dir, exist_ok=True)
- base_file_name = f"{args.save_name}-{global_step}" if global_step > -1 else args.save_name
- logger.info(f"start to export ckpt, save_dir={export_model_dir}, file prefix={base_file_name}")
- export_files = []
-
- # model checkpoint
- bmt_save(
- file_path=os.path.join(export_model_dir, base_file_name + ".pt"),
- model=model,
- export_files=export_files,
- )
-
- # opt is only used for continual pre-training, not the final opt
- if not final_save:
- grad_path = os.path.join(
- args.save,
- args.save_name + ("-%d.rank-%d.opt" % (global_step % (args.save_iters * 5), bmt.rank())),
- )
- torch.save(optimizer.state_dict(), grad_path)
- logger.info(f"Successfully save grad file: {grad_path}")
-
- all_states = dataloader.state_dict()
- if bmt.rank() == 0:
- # data checkpoint
- # rank 0 writes the dataloader state
- torch_save(
- file_path=os.path.join(export_model_dir, base_file_name + ".data"),
- obj=all_states,
- export_files=export_files,
- )
- # data checkpoint json
- # rank 0 writes the dataloader state into the json file
- data_p_json = {k: v for k, v in all_states.items()}
- for k in data_p_json:
- data_p_json[k] = {k_of_v: data_p_json[k][k_of_v].tolist() for k_of_v in data_p_json[k]}
- json_save(
- file_path=os.path.join(export_model_dir, base_file_name + ".data.json"),
- obj=data_p_json,
- export_files=export_files,
- )
- # config 和 vocabs 和模型文件一起存储
- model_cfg_path = os.path.join(export_model_dir, "config.json")
- model_vocab_path = os.path.join(export_model_dir, "vocabs.txt")
- export_files.extend([model_cfg_path, model_vocab_path])
- shutil.copy(args.model_config, model_cfg_path)
- shutil.copy(args.vocab, model_vocab_path)
- logger.info(f"Successfully save model files! {export_files}")
- del all_states
- return export_model_dir
diff --git a/FM_9G/apps/__init__.py b/FM_9G/apps/__init__.py
new file mode 100644
index 0000000..3bee73f
--- /dev/null
+++ b/FM_9G/apps/__init__.py
@@ -0,0 +1,4 @@
+# !/usr/bin/python
+# -*- coding: utf-8 -*-
+#
+# Copyright @2024, QiYuan Inc
diff --git a/FM_9G/apps/fm9g_2b/dataset_configs/0124_hq_data/add_userai/__pycache__/script_cpmc.cpython-310.pyc b/FM_9G/apps/fm9g_2b/dataset_configs/0124_hq_data/add_userai/__pycache__/script_cpmc.cpython-310.pyc
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index 0000000..f834cdd
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diff --git a/FM_9G/apps/fm9g_2b/dataset_configs/0124_hq_data/add_userai/__pycache__/script_cpmc.cpython-39.pyc b/FM_9G/apps/fm9g_2b/dataset_configs/0124_hq_data/add_userai/__pycache__/script_cpmc.cpython-39.pyc
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diff --git a/FM_9G/apps/fm9g_2b/dataset_configs/0124_hq_data/add_userai/script_cpmc.py b/FM_9G/apps/fm9g_2b/dataset_configs/0124_hq_data/add_userai/script_cpmc.py
new file mode 100644
index 0000000..8daef59
--- /dev/null
+++ b/FM_9G/apps/fm9g_2b/dataset_configs/0124_hq_data/add_userai/script_cpmc.py
@@ -0,0 +1,20 @@
+
+import random
+
+
+def rand(n: int, r: random.Random):
+ return int(r.random() * n)
+
+def transform(data, num_sample: int, r: random.Random):
+ if 'input' in data:
+ _input = "<用户>"+data['input']+""
+ else:
+ _input = ""
+
+ if 'output' in data:
+ _output = data['output']
+ else:
+ _output = ""
+ return {"input": _input,
+ "output": _output,
+ }
diff --git a/FM_9G/apps/fm9g_2b/dataset_configs/0124_hq_data/general/__pycache__/script_cpmc.cpython-310.pyc b/FM_9G/apps/fm9g_2b/dataset_configs/0124_hq_data/general/__pycache__/script_cpmc.cpython-310.pyc
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diff --git a/FM_9G/apps/fm9g_2b/dataset_configs/0124_hq_data/general/__pycache__/script_cpmc.cpython-39.pyc b/FM_9G/apps/fm9g_2b/dataset_configs/0124_hq_data/general/__pycache__/script_cpmc.cpython-39.pyc
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diff --git a/FM_9G/apps/fm9g_2b/dataset_configs/0124_hq_data/general/script_cpmc.py b/FM_9G/apps/fm9g_2b/dataset_configs/0124_hq_data/general/script_cpmc.py
new file mode 100644
index 0000000..9d98516
--- /dev/null
+++ b/FM_9G/apps/fm9g_2b/dataset_configs/0124_hq_data/general/script_cpmc.py
@@ -0,0 +1,20 @@
+
+import random
+
+
+def rand(n: int, r: random.Random):
+ return int(r.random() * n)
+
+def transform(data, num_sample: int, r: random.Random):
+ if 'input' in data:
+ _input = data['input']
+ else:
+ _input = ""
+
+ if 'output' in data:
+ _output = data['output']
+ else:
+ _output = ""
+ return {"input": _input,
+ "output": _output,
+ }
diff --git a/FM_9G/apps/fm9g_2b/dataset_configs/fm9g_sft.json b/FM_9G/apps/fm9g_2b/dataset_configs/fm9g_sft.json
new file mode 100644
index 0000000..933ccfc
--- /dev/null
+++ b/FM_9G/apps/fm9g_2b/dataset_configs/fm9g_sft.json
@@ -0,0 +1,134 @@
+[
+ {
+ "dataset_name": "humanevallike_clean_dedup",
+ "task_name": "humanevallike_clean_dedup",
+ "abs_weight": 0.2,
+ "path": "/data/groups/QY_LLM_Core/sa_data/sft_data/0124_hq_data/humanevallike_clean_dedup",
+ "transforms": "0124_hq_data/general/script_cpmc.py",
+ "allow_repeat": true,
+ "nlines": 995339,
+ "ave_tokens_per_line": 100,
+ "total_tokens": 0.1
+ },
+ {
+ "dataset_name": "leetcode_pass_code_0125",
+ "task_name": "leetcode_pass_code_0125",
+ "abs_weight": 0.006,
+ "path": "/data/groups/QY_LLM_Core/sa_data/sft_data/0124_hq_data/leetcode_pass_code_0125",
+ "transforms": "0124_hq_data/general/script_cpmc.py",
+ "allow_repeat": true,
+ "nlines": 10724,
+ "ave_tokens_per_line": 200,
+ "total_tokens": 0.002
+ },
+ {
+ "dataset_name": "logiv2Annotate",
+ "task_name": "logiv2Annotate",
+ "abs_weight": 0.004,
+ "path": "/data/groups/QY_LLM_Core/sa_data/sft_data/0124_hq_data/logiv2Annotate",
+ "transforms": "0124_hq_data/general/script_cpmc.py",
+ "allow_repeat": true,
+ "nlines": 12566,
+ "ave_tokens_per_line": 512,
+ "total_tokens": 0.006
+ },
+ {
+ "dataset_name": "mmlu_enhance",
+ "task_name": "mmlu_enhance",
+ "abs_weight": 0.1,
+ "path": "/data/groups/QY_LLM_Core/sa_data/sft_data/0124_hq_data/mmlu_enhance",
+ "transforms": "0124_hq_data/general/script_cpmc.py",
+ "allow_repeat": true,
+ "nlines": 169771,
+ "ave_tokens_per_line": 300,
+ "total_tokens": 0.05
+ },
+ {
+ "dataset_name": "mtbench_like",
+ "task_name": "mtbench_like",
+ "abs_weight": 0.2,
+ "path": "/data/groups/QY_LLM_Core/sa_data/sft_data/0124_hq_data/mtbench_like",
+ "transforms": "0124_hq_data/general/script_cpmc.py",
+ "allow_repeat": true,
+ "nlines": 319080,
+ "ave_tokens_per_line": 500,
+ "total_tokens": 0.15
+ },
+ {
+ "dataset_name": "ultra_dataset_new",
+ "task_name": "ultra_dataset_new",
+ "abs_weight": 2.0,
+ "path": "/data/groups/QY_LLM_Core/sa_data/sft_data/0124_hq_data/ultra_dataset_new",
+ "transforms": "0124_hq_data/general/script_cpmc.py",
+ "allow_repeat": true,
+ "nlines": 385045,
+ "ave_tokens_per_line": 200.296266559615,
+ "total_tokens": 2.0
+ },
+ {
+ "dataset_name": "sft_data_zh_wowru",
+ "task_name": "sft_data_zh_wowru",
+ "abs_weight": 1.0,
+ "path": "/data/groups/QY_LLM_Core/sa_data/sft_data/0124_hq_data/sft_data_zh_wowru",
+ "transforms": "0124_hq_data/general/script_cpmc.py",
+ "allow_repeat": true,
+ "nlines": 2963260,
+ "ave_tokens_per_line": 200.296266559615,
+ "total_tokens": 1
+ },
+ {
+ "dataset_name": "math_data",
+ "task_name": "math_data",
+ "abs_weight": 0.003,
+ "path": "/data/groups/QY_LLM_Core/sa_data/sft_data/0124_hq_data/math_data",
+ "transforms": "0124_hq_data/add_userai/script_cpmc.py",
+ "allow_repeat": true,
+ "nlines": 2963260,
+ "ave_tokens_per_line": 200.296266559615,
+ "total_tokens": 0.005
+ },
+ {
+ "dataset_name": "t0",
+ "task_name": "t0",
+ "abs_weight": 0.1,
+ "path": "/data/groups/QY_LLM_Core/sa_data/sft_data/0124_hq_data/t0",
+ "transforms": "0124_hq_data/add_userai/script_cpmc.py",
+ "allow_repeat": true,
+ "nlines": 1650309,
+ "ave_tokens_per_line": 500.296266559615,
+ "total_tokens": 0.82
+ },
+ {
+ "dataset_name": "wikihow",
+ "task_name": "wikihow",
+ "abs_weight": 0.1,
+ "path": "/data/groups/QY_LLM_Core/sa_data/sft_data/0124_hq_data/wikihow",
+ "transforms": "0124_hq_data/general/script_cpmc.py",
+ "allow_repeat": true,
+ "nlines": 180128,
+ "ave_tokens_per_line": 900.296266559615,
+ "total_tokens": 0.16
+ },
+ {
+ "dataset_name": "reclor",
+ "task_name": "reclor",
+ "abs_weight": 0.002,
+ "path": "/data/groups/QY_LLM_Core/sa_data/sft_data/0124_hq_data/reclor",
+ "transforms": "0124_hq_data/general/script_cpmc.py",
+ "allow_repeat": true,
+ "nlines": 4174,
+ "ave_tokens_per_line": 700.296266559615,
+ "total_tokens": 0.003
+ },
+ {
+ "dataset_name": "logic_test_lx_0127",
+ "task_name": "logic_test_lx_0127",
+ "abs_weight": 0.001,
+ "path": "/data/groups/QY_LLM_Core/sa_data/sft_data/0124_hq_data/logic_test_lx_0127",
+ "transforms": "0124_hq_data/add_userai/script_cpmc.py",
+ "allow_repeat": true,
+ "nlines": 2800,
+ "ave_tokens_per_line": 200.96266559615,
+ "total_tokens": 0.0004
+ }
+]
diff --git a/FM_9G/apps/fm9g_2b/model_configs/2.4b.json b/FM_9G/apps/fm9g_2b/model_configs/2.4b.json
new file mode 100644
index 0000000..3ddf830
--- /dev/null
+++ b/FM_9G/apps/fm9g_2b/model_configs/2.4b.json
@@ -0,0 +1,28 @@
+{
+ "vocab_size": 122753,
+ "dropout_p": 0.0,
+ "eps": 1e-05,
+ "half": true,
+ "half_type": "bf16",
+ "use_flash_attn": true,
+ "flash_attn_mask_shape": "2d",
+ "dim_model": 2304,
+ "dim_ff": 5760,
+ "dim_head": 64,
+ "num_heads": 36,
+ "num_kv_heads": 36,
+ "num_layers": 40,
+ "activate_fn": "silu",
+ "init_std": 0.10,
+ "scale": true,
+ "scale_emb": 12,
+ "scale_depth": 1.4,
+ "dim_model_base": 256,
+ "model_type": "fm9g",
+ "architectures": [
+ "FM9GForCausalLM"
+ ],
+ "qk_norm": false,
+ "tie_lm_head": true,
+ "ffn_gated": true
+}
diff --git a/FM_9G/apps/fm9g_2b/pretrain_dragonfly.py b/FM_9G/apps/fm9g_2b/pretrain_dragonfly.py
new file mode 100644
index 0000000..d334dce
--- /dev/null
+++ b/FM_9G/apps/fm9g_2b/pretrain_dragonfly.py
@@ -0,0 +1,548 @@
+# coding=utf-8
+# Copyright 2024 QiYuan Inc.
+import inspect
+import json
+import math
+import os
+import re
+import sys
+import time
+from collections import defaultdict
+from itertools import chain
+from typing import Any
+from typing import Dict
+from typing import List
+from typing import Union
+
+import bmtrain as bmt
+import numpy as np
+import torch
+from bmtrain import nccl
+from bmtrain.global_var import config as bmt_config
+
+sys.path.append("../../")
+from fm9g.arguments import get_args
+from fm9g.dragonfly.modeling_dragonfly import Dragonfly
+from fm9g.dragonfly.modeling_dragonfly import DragonflyConfig
+from fm9g.dragonfly.training_tasks.pretrain_indexed import CudaPrefetcher
+from fm9g.dragonfly.training_tasks.pretrain_indexed import MixedIndexedDataset
+from fm9g.dragonfly.training_tasks.pretrain_indexed import UnpadBatchedMixedDataset
+from fm9g.utils import exporter
+from fm9g.utils import logger
+from fm9g.utils.exporter import save_every_step_stats
+from fm9g.utils.training_stats import num_non_embedding_parameters
+from fm9g.utils.training_stats import num_parameters
+
+
+def get_tokenizer(args):
+ from transformers import LlamaTokenizerFast
+
+ tokenizer = LlamaTokenizerFast(vocab_file=args.tokenizer_path)
+ return tokenizer
+
+
+def get_model(args):
+ config = DragonflyConfig.from_json_file(args.model_config)
+ config.tp = 1 if args.tp_size != 1 else 0 # TODO
+ config.pose_prob = args.pose_prob
+ config.pose_scaling_factor = args.pose_scaling_factor
+ config.rope_scaling_type = args.rope_scaling_type
+ config.rope_scaling_factor = args.rope_scaling_factor
+ config.orig_max_length = args.orig_max_length
+
+ bmt.print_rank("model config: {}".format(config))
+ bmt.print_rank("bmt config: {}".format(bmt.config))
+
+ model = Dragonfly(config)
+ if args.load is not None:
+ bmt.print_rank("args.load is not None, start to load checkpoints" + args.load)
+ exporter.load_model_ckpt(args, model)
+ else:
+ bmt.print_rank("args.load is None, start to initialize parameters")
+ bmt.init_parameters(model)
+ return model
+
+
+def get_optimizer(args, model):
+ scale_lr_group = []
+ normal_group = []
+ scale_lr_group_name, normal_group_name = [], []
+ for n, p in model.named_parameters():
+ if n.endswith(".weight") and "layernorm" not in n and "embedding" not in n and "lm_head" not in n:
+ scale_lr_group.append(p)
+ scale_lr_group_name.append(n)
+ else:
+ normal_group.append(p)
+ normal_group_name.append(n)
+ bmt.print_rank(scale_lr_group_name, normal_group_name)
+ param_groups = [
+ {"params": scale_lr_group, "lr": args.lr / model.config.scale_width},
+ {"params": normal_group, "lr": args.lr},
+ ]
+
+ if args.offload:
+ optimizer = bmt.optim.AdamOffloadOptimizer(param_groups, betas=(0.9, 0.95), weight_decay=args.weight_decay)
+ else:
+ optimizer = bmt.optim.AdamOptimizer(param_groups, betas=(0.9, 0.95), weight_decay=args.weight_decay)
+ if args.load is not None and args.load_grad:
+ exporter.load_optimizer_ckpt(args, optimizer)
+ bmt.print_rank("optimizer is loaded!")
+ return optimizer
+
+
+def get_learning_rate_scheduler(args, optimizer):
+ from fm9g.training_utils.lr_scheduler import Cosine
+ from fm9g.training_utils.lr_scheduler import WarmupStableDrop
+
+ end_iter = args.train_iters
+ if 0 < args.warmup_iters < 1: # 需要支持按固定比例step用来做warmup的
+ warmup_iters = int(end_iter * args.warmup_iters)
+ else:
+ warmup_iters = int(args.warmup_iters)
+
+ if 0 < args.drop_iters < 1: # 需要支持按固定比例step用来做drop的
+ drop_iters = int(end_iter * args.drop_iters)
+ else:
+ drop_iters = int(args.drop_iters)
+
+ if args.lr_scheduler == "cosine":
+ lr_scheduler = Cosine(
+ optimizer,
+ start_lr=args.lr,
+ warmup_iter=warmup_iters,
+ end_iter=end_iter, # 原来是lr_decay_iter
+ num_iter=args.start_step,
+ #lr_end_restart=args.lr_end_restart,
+ #resume_no_optimze=args.resume_no_optimze,
+ )
+ elif args.lr_scheduler == "warmupstabledrop":
+ lr_scheduler = WarmupStableDrop(
+ optimizer,
+ start_lr=args.lr,
+ warmup_iter=warmup_iters,
+ end_iter=end_iter, # 原来是lr_decay_iter
+ drop_iter=drop_iters,
+ num_iter=args.start_step,
+ resume_no_optimze=args.resume_no_optimze,
+ )
+ return lr_scheduler
+
+
+def setup_model_and_optimizer(args):
+ start = time.time()
+ tokenizer = get_tokenizer(args)
+ bmt.synchronize()
+ logger.info("load tokenizer in {:.2f}s".format(time.time() - start))
+
+ start = time.time()
+ model = get_model(args)
+ logger.info("load model in {:.2f}s".format(time.time() - start))
+
+ start = time.time()
+ optimizer = get_optimizer(args, model)
+ lr_scheduler = get_learning_rate_scheduler(args, optimizer)
+ bmt.synchronize()
+ logger.info("load lr_scheduler in {:.2f}s".format(time.time() - start))
+
+ return tokenizer, model, optimizer, lr_scheduler
+
+
+def resume_training(args):
+ ckpts = sorted(
+ [z for z in chain(*[[os.path.join(x[0], y) for y in x[2]] for x in os.walk(args.save)]) if z.endswith(".pt")],
+ reverse=True,
+ key=lambda x: (int)(re.search("(\d+).pt", x)[1]),
+ )
+ # find newest job
+ ckpts = sorted(
+ ckpts,
+ reverse=True,
+ key=lambda x: (int)(re.search("job_(\d+)_ckpt", x)[1]),
+ )
+
+ if len(ckpts) > 0:
+ bmt.print_rank(f"resuming with last checkpoint: {ckpts[0]}")
+ args.load = ckpts[0]
+ # by default, do not load grad file
+ args.load_grad = False
+ args.start_step = 0
+ else:
+ # no ckpts, nothing we can do
+ os._exit(1)
+
+
+def initialize():
+ args = get_args(pretrain=True)
+ bmt.init_distributed(seed=args.seed, tp_size=args.tp_size)
+
+ if args.save is not None:
+ os.makedirs(args.save, exist_ok=True)
+ if args.load is not None:
+ if args.only_load_model == 0:
+ if args.start_step == 0:
+ log_ckpt = exporter.load_log_ckpt(args)
+ if "iteration" in log_ckpt:
+ args.start_step = log_ckpt["iteration"]
+ else:
+ args.start_step = (int)(re.findall("(\d+)", args.load)[-1])
+ logger.info("Start from step {}".format(args.start_step))
+ elif args.only_load_model == 1:
+ logger.info("You load model ckpt, and choose to completely start the 0 step.")
+ else:
+ raise NotImplementedError
+ else:
+ logger.info("You do not load model")
+
+ return args
+
+
+def see_memory(detail=False):
+ if detail:
+ res = torch.cuda.memory_summary()
+ else:
+ res = (
+ round(torch.cuda.max_memory_allocated() / (1024 * 1024 * 1024), 2),
+ round(torch.cuda.memory_reserved() / (1024 * 1024 * 1024), 2),
+ round(torch.cuda.max_memory_reserved() / (1024 * 1024 * 1024), 2),
+ )
+ torch.cuda.reset_peak_memory_stats()
+ return res
+
+
+def add_mem_time(info, mem_usage, tim_usage):
+ torch.cuda.synchronize()
+ bmt.synchronize()
+ mem_usage[info] = see_memory()
+ tim_usage[info] = time.time()
+ return mem_usage, tim_usage
+
+
+def get_task_loss_and_token(loss, task_ids, task_num, targets):
+ # task_ids 可能有-1 来代表无效token
+ _task_num = task_num + 1
+ _task_ids = (task_ids.clone() + 1).to(torch.int64) # [batch_size, seq_len]
+ # gen masks
+ _task_mask = torch.zeros((_task_num, *_task_ids.shape), device=_task_ids.device)
+ _task_mask.scatter_(0, _task_ids.unsqueeze(0), 1) # [task_num, batch_size, seq_len]
+ _loss_mask = torch.ne(targets, -100).to(torch.int32)
+ _mask = _task_mask * _loss_mask.unsqueeze(0) # [task_num, batch_size, seq_len]
+ # calc loss and tokens
+ _task_losses = (loss.unsqueeze(0) * _mask).view((_task_num, -1)).sum(dim=-1)[1:] # [task_num]
+ _task_tokens = _mask.view((_task_num, -1)).sum(dim=-1)[1:] # [task_num]
+ # return token-wise avg losses and tokens
+ return torch.nan_to_num(_task_losses / _task_tokens, nan=0.0), _task_tokens
+
+
+class ChunkAve:
+ def __init__(self, chunk_size=100):
+ self.ave_list = []
+ self.chunk_size = chunk_size
+
+ def record(self, time):
+ self.ave_list.append(time)
+ self.ave_list = self.ave_list[-self.chunk_size :]
+
+ def get(self):
+ return sum(self.ave_list) / len(self.ave_list)
+
+
+def pretrain(
+ args,
+ tokenizer,
+ model: Dragonfly,
+ optimizer,
+ lr_scheduler: bmt.lr_scheduler.WarmupLRScheduler,
+):
+ ave_model_time = ChunkAve(chunk_size=100)
+ ave_iter_time = ChunkAve(chunk_size=100)
+
+ loss_func = bmt.loss.FusedCrossEntropy(ignore_index=-100, reduction="none")
+ optim_manager = bmt.optim.OptimManager(
+ loss_scale=None,
+ loss_scale_steps=args.loss_scale_steps,
+ loss_scale_factor=2,
+ max_loss_scale=args.max_loss_scale,
+ min_loss_scale=args.min_loss_scale,
+ )
+ optim_manager.add_optimizer(optimizer, lr_scheduler)
+
+ start_step = args.start_step
+
+ if args.tensorboard is not None and bmt.rank() == 0:
+ import distutils.version # noqa: F401
+
+ from tensorboardX import SummaryWriter
+
+ if not os.path.exists(args.tensorboard):
+ os.makedirs(args.tensorboard)
+ writer = SummaryWriter(log_dir=args.tensorboard)
+
+ if args.load is not None:
+ log_ckpt = exporter.load_log_ckpt(args)
+ else:
+ log_ckpt = {}
+ global_token_pass = log_ckpt.get("global_token_pass", 0.0)
+ global_total_task_token = defaultdict(int, log_ckpt.get("global_total_task_token", {})) # token by task
+
+ global_world_size = bmt.world_size()
+ bmt.print_rank("Begin preparing dataset")
+ if args.tp_size == 1 or bmt.config["tp_rank"] == 0:
+ mixed_indexed_dataset = MixedIndexedDataset(
+ cfg_path=args.dataset,
+ cfg_json_str=None,
+ tokenizer=tokenizer,
+ max_length=args.max_length,
+ nthreads=args.dataloader_num_threads,
+ prefetch_slice=args.dataloader_prefetch,
+ weight_by_size=True,
+ )
+
+ if args.load is not None and args.only_load_model == 0 and args.load_dataloader_ckpt == 1:
+ exporter.load_dataloader_ckpt(args, mixed_indexed_dataset)
+
+ batched_dataset = UnpadBatchedMixedDataset(mixed_indexed_dataset, args.batch_size, args.max_length)
+ dataloader = torch.utils.data.DataLoader(
+ batched_dataset,
+ batch_size=None,
+ collate_fn=lambda x: x,
+ num_workers=args.dataloader_num_workers,
+ prefetch_factor=args.dataloader_prefetch_factor,
+ )
+ else:
+
+ def dummy_generator():
+ while True:
+ yield None
+
+ mixed_indexed_dataset = dummy_generator()
+ dataloader = mixed_indexed_dataset
+
+ DataIterator = CudaPrefetcher(dataloader, tp_size=args.tp_size, tp_rank=bmt.config["tp_rank"])
+
+ bmt.print_rank("Preparing dataset done.")
+
+ # inspect at init
+ model_inspect = bmt.inspect.inspect_model(model, "*")
+ bmt.print_rank(bmt.inspect.format_summary(model_inspect))
+
+ try:
+ mem_usage, tim_usage = {}, {}
+ mem_usage, tim_usage = add_mem_time("before_log", mem_usage, tim_usage)
+
+ for iteration, data in enumerate(DataIterator, start=start_step + 1):
+ if args.tp_size == 1 or bmt.config["tp_rank"] == 0:
+ mixed_indexed_dataset.update_states(data["task_ids"], data["indexes"])
+
+ mem_usage, tim_usage = add_mem_time("init", mem_usage, tim_usage)
+
+ logits = model(
+ input=data["inputs"],
+ cu_seqlens=data["cu_seqlens"],
+ max_seqlen=data["max_seqlen"],
+ position_ids=data["position_ids"],
+ )
+
+ # chunk targets and task_ids
+ data["targets"] = (
+ data["targets"]
+ .view(-1)
+ .chunk(bmt.config["tp_size"])[bmt.config["tp_rank"]]
+ .view(data["targets"].shape[0], -1)
+ )
+ data["task_ids"] = (
+ data["task_ids"]
+ .view(-1)
+ .chunk(bmt.config["tp_size"])[bmt.config["tp_rank"]]
+ .view(data["task_ids"].shape[0], -1)
+ )
+
+ _target = data["targets"].view(-1)
+ non_reduced_loss = loss_func(logits.view(-1, logits.size(-1)), _target)
+ _w = (_target != -100).int()
+ loss = non_reduced_loss.sum() / _w.sum().float()
+
+ global_loss = bmt.sum_loss(loss).item()
+ mem_usage, tim_usage = add_mem_time("forward", mem_usage, tim_usage)
+
+ optim_manager.backward(loss)
+ mem_usage, tim_usage = add_mem_time("backward", mem_usage, tim_usage)
+
+ if iteration % args.grad_accum == 0 or iteration == args.train_iters:
+ grad_accum_init_time = tim_usage["init"]
+
+ grad_norm = optim_manager.clip_grad_norm(optimizer.param_groups, args.clip_grad, norm_type=2)
+ optim_manager.step()
+ optim_manager.zero_grad()
+ mem_usage, tim_usage = add_mem_time("optim", mem_usage, tim_usage)
+ model_time = tim_usage["optim"] - grad_accum_init_time
+ ave_model_time.record(model_time)
+ else:
+ # dummy optim step
+ grad_norm = torch.Tensor([0.0]).cuda()
+ tim_usage["optim"] = tim_usage["backward"]
+ mem_usage["optim"] = mem_usage["backward"]
+
+ with torch.no_grad():
+ task_num = len(data["task_names"])
+ task_loss, task_token = get_task_loss_and_token(
+ non_reduced_loss, data["task_ids"], task_num, data["targets"]
+ )
+ task_loss_map: Dict[str, float] = {}
+ gatherd_task_loss_map = bmt.distributed.all_gather(task_loss)
+ gatherd_task_token_map = bmt.distributed.all_gather(task_token)
+ gatherd_task_loss_token_map = gatherd_task_loss_map * gatherd_task_token_map
+ sum_task_loss = gatherd_task_loss_token_map.sum(dim=0)
+ tot_task_token = gatherd_task_token_map.sum(dim=0)
+ ave_task_loss = sum_task_loss / tot_task_token
+ for i in range(task_num):
+ task_loss_map[data["task_names"][i]] = ave_task_loss[i].item()
+ global_total_task_token[data["task_names"][i]] += tot_task_token[i].item()
+
+ local_total_rate = torch.Tensor([data["lengths"].float().mean() / args.max_length]).cuda()
+ local_total_rate = bmt.sum_loss(local_total_rate).item()
+ global_token_pass += (
+ (global_world_size // args.tp_size) * local_total_rate * args.max_length * args.batch_size
+ )
+
+ bmt.print_rank(
+ "=========================================" + time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
+ )
+ last_before_log_time = tim_usage["before_log"]
+ mem_usage, tim_usage = add_mem_time("before_log", mem_usage, tim_usage)
+
+ iter_time = tim_usage["before_log"] - last_before_log_time
+
+ ave_iter_time.record(iter_time)
+
+ train_info = {
+ "time": iter_time,
+ "iteration": iteration,
+ "loss": global_loss,
+ "lr": lr_scheduler.current_lr,
+ "token_max": local_total_rate,
+ "token_pass": global_token_pass,
+ "throughout": args.max_length * args.batch_size * local_total_rate / ave_iter_time.get() / args.tp_size,
+ "grad_norm": grad_norm.item(),
+ "mask_max": ((data["targets"] >= 0).sum(-1).float().mean() / args.max_length).item(),
+ "task_loss": task_loss_map,
+ "total_task_token": global_total_task_token,
+ }
+ global_token_pass_str = convert_to_k_and_b(global_token_pass)
+
+ bmt.print_rank(
+ (
+ "| Iter: {iteration:6d} | loss: {loss:.4f} | lr: {lr:.4e} | model_time: {model_time:.2f} | iter_time: {iter_time:.2f}| chunk_ave_time: {chunk_ave_time:.2f}"
+ + " token/max: {tokenrate:.4f} | mask/max: {maskrate:.4f} | grad_norm: {grad_norm:.4f} | global_token_pass (B):"
+ + "{global_token_pass} | mem_usage {mem_usage} | "
+ ).format(
+ iteration=iteration,
+ loss=global_loss,
+ lr=lr_scheduler.current_lr,
+ model_time=model_time,
+ iter_time=iter_time,
+ chunk_ave_time=ave_iter_time.get(),
+ tokenrate=data["lengths"].float().mean() / args.max_length / args.batch_size,
+ maskrate=(data["targets"] >= 0).sum(-1).float().mean() / args.max_length / args.batch_size,
+ grad_norm=grad_norm.item(),
+ global_token_pass=global_token_pass_str,
+ mem_usage=max([value for key, value in mem_usage.items()]),
+ )
+ )
+
+ bmt.print_rank(
+ "task_loss:\t| "
+ + " | ".join(["{}: {:.4f}".format(task_name, loss) for task_name, loss in task_loss_map.items()])
+ + " |"
+ )
+
+ if iteration % 10 == 0:
+ bmt.print_rank(
+ "task_tokens (B):\t| "
+ + " | ".join(
+ [
+ "{}: {:.4f}".format(task_name, task_token / 10**9)
+ for task_name, task_token in global_total_task_token.items()
+ ]
+ )
+ + " |"
+ )
+
+ if iteration % args.inspect_iters == 0:
+ model_inspect = bmt.inspect.inspect_model(model, "*")
+ bmt.print_rank(bmt.inspect.format_summary(model_inspect))
+
+ if args.log_dir is not None and bmt.rank() == 0:
+ if args.save is not None:
+ save_every_step_stats(train_info, args.save)
+
+ if args.tensorboard is not None and bmt.rank() == 0:
+ writer.add_scalar("Loss/train", global_loss, iteration)
+ writer.add_scalar("Optimizer/lr", lr_scheduler.current_lr, iteration)
+ writer.add_scalar("Optimizer/scale", optim_manager.loss_scale, iteration)
+ writer.add_scalar("Optimizer/grad_norm", grad_norm.item(), iteration)
+ for task_name, loss in task_loss_map.items():
+ if not math.isnan(loss):
+ writer.add_scalar("Loss/train/{}".format(task_name), loss, iteration)
+
+ # -------- save file. If need to backup by Klara platform, use export.xx_save --------
+ log_ckpt = {
+ "global_total_task_token": global_total_task_token,
+ "global_token_pass": global_token_pass,
+ "iteration": iteration,
+ }
+
+ if args.save is not None and iteration % args.save_iters == 0:
+ exporter.export(
+ model,
+ mixed_indexed_dataset,
+ tokenizer,
+ optimizer,
+ iteration,
+ args,
+ log_ckpt=log_ckpt,
+ final_save=False,
+ )
+
+ if iteration == args.train_iters and args.stop_when_end == 1:
+ break
+
+ except Exception as e:
+ print(f"train loop err: {e}")
+ raise e
+ finally:
+ pass
+
+ exporter.export(model, mixed_indexed_dataset, tokenizer, optimizer, -1, args, final_save=False)
+
+
+def convert_to_k_and_b(number):
+ if number >= 1e9: # 大于或等于10亿
+ b_number = number / 1e9
+ return f"{b_number:.2f}B"
+ elif number >= 1e6: # 大于或等于1百万
+ k_number = number / 1e6
+ return f"{k_number:.2f}M"
+ elif number >= 1e3:
+ k_number = number / 1e3
+ return f"{k_number:.2f}K"
+ else:
+ return str(number)
+
+
+def main():
+ args = initialize()
+ bmt.synchronize()
+ tokenizer, model, optimizer, lr_scheduler = setup_model_and_optimizer(args)
+ bmt.print_rank("finish loading")
+ bmt.print_rank(
+ "Number of parameter {}, Number of non-e parameter {}".format(
+ num_parameters(model), num_non_embedding_parameters(model)
+ )
+ )
+ bmt.print_rank("args: {}".format(args))
+
+ pretrain(args, tokenizer, model, optimizer, lr_scheduler)
+
+
+if __name__ == "__main__":
+ main()
diff --git a/FM_9G/apps/fm9g_2b/pretrain_dragonfly.sh b/FM_9G/apps/fm9g_2b/pretrain_dragonfly.sh
new file mode 100644
index 0000000..3e88379
--- /dev/null
+++ b/FM_9G/apps/fm9g_2b/pretrain_dragonfly.sh
@@ -0,0 +1,234 @@
+#!/bin/bash
+
+#export OMP_NUM_THREADS=16
+
+declare -A args # Declare an associative array to store arguments and values
+
+args["model_unique"]="2b_0701"
+args["resume_ckpt"]=""
+args["config"]="2.4b"
+args["flash"]="cuda"
+args["batch_size"]="1"
+args["max_length"]="4096"
+args["save_iters"]="500"
+args["train_iters"]="10"
+args["dataset_config"]="fm9g_sft"
+args["local"]="False"
+args["dataloader"]="indexed"
+args["save"]="True"
+args["dataloader_num_threads"]=1
+args["dataloader_prefetch"]=1
+args["dataloader_prefetch_factor"]=1
+args["dataloader_num_workers"]=1
+args["lr"]="1e-5"
+args["warmup_iters"]="20"
+args["drop_iters"]="0.1"
+args["tokenizer_path"]="./tokenizer/tokenizer.model" # /user/tc_agi/klara/baichuan2/baichuan2.tokenizer.model
+args["load_grad"]="False"
+args["grad_ckpt_num"]="160"
+args["exp_group"]=""
+args["ignore_cuda_oom"]="1"
+args["tensorboard_all_tasks"]="0"
+args["stop_when_end"]="0"
+args["only_run_dataloader"]="0"
+args["eps"]="1e-6"
+args["inspect_iters"]="100"
+args["strict_state_dict"]="1"
+args["only_load_model"]="1"
+args["lr_scheduler"]="cosine"
+args["resume_no_optimze"]="0"
+args["tp_size"]="1"
+args["parallel_load_datastate"]="8"
+args["async_save"]="False"
+args["load_dataloader_ckpt"]="0"
+args["drop_begin"]="-1"
+args["drop_rate"]="0.5"
+args["use_checkpoint"]="0"
+
+
+# Loop through the arguments
+for ((i=1; i<=$#; i++)); do
+ arg="${!i}"
+ # Check if the argument starts with "--"
+ if [[ "$arg" == --* ]]; then
+ arg_name="${arg:2}" # Remove leading "--"
+ valueid=$((i+1))
+ # Get the value of the argument if it exists
+ if ((i+1 <= $#)); then
+ args["$arg_name"]="${!valueid}"
+ i=$((i+1)) # Skip the next argument (its value)
+ else
+ args["$arg_name"]="" # Set empty value if no value provided
+ fi
+ fi
+done
+
+# 使用 Python 读取 JSON 文件并更新 Bash 字典
+while read -r key value; do
+ args["$key"]="$value"
+done < <(python -c 'import json, sys; obj = json.load(open("train_configs/'${args['config']}'.json"))["pretrain"]; print("\n".join(["{} {}".format(k, v) for k, v in obj.items()]))')
+
+
+
+# 用cmd arg 再更新一次
+# Loop through the arguments
+for ((i=1; i<=$#; i++)); do
+ arg="${!i}"
+ # Check if the argument starts with "--"
+ if [[ "$arg" == --* ]]; then
+ arg_name="${arg:2}" # Remove leading "--"
+ valueid=$((i+1))
+
+ # Get the value of the argument if it exists
+ if ((i+1 <= $#)); then
+ args["$arg_name"]="${!valueid}"
+ i=$((i+1)) # Skip the next argument (its value)
+ else
+ args["$arg_name"]="" # Set empty value if no value provided
+ fi
+ fi
+done
+
+# Print the values of the arguments
+echo "----------- CMD args ----------"
+for key in "${!args[@]}"; do
+ echo "$key: ${args[$key]}"
+done
+echo "--------- END CMD args --------"
+
+
+if [[ ${args["flash"]} == "triton" ]]; then
+ sudo cp /usr/local/cuda-11.6/compat/libcuda.so.510.108.03 /usr/lib/x86_64-linux-gnu/libcuda.so.510.108.03
+ sudo ln /usr/lib/x86_64-linux-gnu/libcuda.so.510.108.03 /usr/lib/x86_64-linux-gnu/libcuda.so
+ echo "triton flash"
+fi
+
+
+
+
+
+
+GPUS_PER_NODE=$(nvidia-smi --query-gpu=gpu_name --format=csv,noheader | wc -l)
+# GPUS_PER_NODE=1
+echo "Using ${GPUS_PER_NODE} GPU each machine"
+
+
+if [[ ${args["model_unique"]} == "" ]]; then
+ MODEL_UNIQUE=${JEEVES_JOB_ID} # 写入的位置,没传的话自动构造
+ # JOBID+CreateTime, 本次run的唯一标识符。在白箱里可以通过/projects/${PROJECTID}-${PROJECTNAME}/checkpoints/${MODEL_UNIQUE} 拿到 checkpoint
+# 通过/projects/${PROJECTID}-${PROJECTNAME}/tensorboard/${MODEL_UNIQUE} 拿到 tensorboard
+else
+ MODEL_UNIQUE=${args["model_unique"]} # 给了写入的位置
+fi
+echo "model_unique: "$MODEL_UNIQUE
+
+# --------------- 运行参数 ---------------
+
+OPTS+=" --model-config model_configs/"${args['config']}".json" # [CHANGE]
+OPTS+=" --batch-size ${args["batch_size"]}"
+OPTS+=" --train-iters ${args["train_iters"]}"
+OPTS+=" --save-iters ${args["save_iters"]}"
+OPTS+=" --save-name fm9g_live_checkpoint"
+OPTS+=" --max-length ${args["max_length"]}"
+OPTS+=" --lr ${args["lr"]}"
+OPTS+=" --inspect-iters ${args["inspect_iters"]}"
+OPTS+=" --warmup-iters ${args["warmup_iters"]}"
+OPTS+=" --drop-iters ${args["drop_iters"]}"
+OPTS+=" --lr_scheduler ${args["lr_scheduler"]}"
+OPTS+=" --offload"
+#OPTS+=" --vocab ./tokenizer/vocab.txt"
+OPTS+=" --flash ${args["flash"]}"
+OPTS+=" --tensorboard_all_tasks ${args["tensorboard_all_tasks"]}"
+OPTS+=" --ignore_cuda_oom ${args["ignore_cuda_oom"]}"
+OPTS+=" --stop_when_end ${args["stop_when_end"]}"
+OPTS+=" --only_run_dataloader ${args["only_run_dataloader"]}"
+OPTS+=" --eps ${args["eps"]}"
+OPTS+=" --strict_state_dict ${args["strict_state_dict"]}"
+OPTS+=" --only_load_model ${args["only_load_model"]}"
+OPTS+=" --resume_no_optimze ${args["resume_no_optimze"]}"
+OPTS+=" --tokenizer_path ${args["tokenizer_path"]}"
+OPTS+=" --weight-decay 0.1"
+OPTS+=" --tp-size ${args["tp_size"]}"
+OPTS+=" --parallel_load_datastate ${args["parallel_load_datastate"]}"
+OPTS+=" --load_dataloader_ckpt ${args["load_dataloader_ckpt"]}"
+OPTS+=" --drop_begin ${args["drop_begin"]}"
+OPTS+=" --drop_rate ${args["drop_rate"]}"
+OPTS+=" --use_checkpoint ${args["use_checkpoint"]}"
+
+if [[ ${args["load_grad"]} == "True" ]]; then
+ OPTS+=" --load-grad"
+ OPTS+=" --grad-ckpt-num ${args["grad_ckpt_num"]}"
+fi
+
+
+if [[ ${args["async_save"]} == "True" ]]; then
+ OPTS+=" --async_save"
+fi
+
+
+if [[ ${args["dataloader"]} == "indexed" ]]; then
+ OPTS+=" --dataloader_num_threads ${args["dataloader_num_threads"]}"
+ OPTS+=" --dataloader_prefetch ${args["dataloader_prefetch"]}"
+ OPTS+=" --dataloader_num_workers ${args["dataloader_num_workers"]}"
+ OPTS+=" --dataloader_prefetch_factor ${args["dataloader_prefetch_factor"]}"
+fi
+
+
+# --------------- 写文件路径 ---------------
+## checkpoint
+if [[ ${args["save"]} == "True" ]]; then
+
+ OPTS+=" --save ./data/checkpoints/${MODEL_UNIQUE}/"
+ OPTS+=" --save-model ./not_exist/${MODEL_UNIQUE}/"
+else
+ echo "won't save model"
+fi
+
+
+## logs,/local/logs 等价于 ./datalogs(软链)
+mkdir -p ./data/checkpoints/logs/${MODEL_UNIQUE}
+OPTS+=" --log-dir ./data/checkpoints/logs/${MODEL_UNIQUE}"
+OPTS+=" --tensorboard ./data/tensorboard/${args["exp_group"]}${MODEL_UNIQUE}/"
+
+
+
+if [[ ${args["local"]} == "True" ]]; then
+ current_dir=$(pwd)
+ OPTS+=" --dataset ${current_dir}/dataset_configs/${args["dataset_config"]}.json"
+else
+ current_dir=$(pwd)
+ OPTS+=" --dataset ${current_dir}/dataset_configs/${args["dataset_config"]}.json"
+ echo "Platform config:"${PLATFORM_CONFIG_PATH}
+fi
+
+
+## checkpoint,兼容 CHECKPOINT 和 LATEST_CHECKPOINT。debug 时建议不加载 checkpoint,启动会比较快
+if [ "${args["resume_ckpt"]}" != "" ]; then
+ OPTS+=" --load ./data/checkpoints/${MODEL_UNIQUE}/${args["resume_ckpt"]}"
+else
+ echo "No checkpoint to load"
+fi
+
+
+filename="pretrain_dragonfly"
+
+if [[ ${args["local"]} == "True" ]]; then
+ PRETRAIN_ENTRY="$filename.py"
+else
+ PRETRAIN_ENTRY="$filename.py"
+fi
+
+
+GPUS_PER_NODE=1
+NNODES=1
+RANK=0
+MASTER_ENDPOINT=g3006
+MASTER_PORT=23456
+#CMD="torchrun --nnodes=${NNODES} --nproc_per_node=${GPUS_PER_NODE} --node_rank=${RANK} --master_addr=${MASTER_ENDPOINT} --master_port=${MASTER_PORT} ${PRETRAIN_ENTRY} ${OPTS}"
+CMD="torchrun --nnodes=${NNODES} --nproc_per_node=${GPUS_PER_NODE} --node_rank=${RANK} --rdzv_id=1 --rdzv_backend=c10d --rdzv_endpoint=${MASTER_ENDPOINT}:${MASTER_PORT} ${PRETRAIN_ENTRY} ${OPTS}"
+
+echo "-------final CMD is------"
+echo "${CMD}"
+echo "-------final CMD end------"
+
+$CMD
diff --git a/FM_9G/apps/fm9g_2b/tokenizer/tokenizer.json b/FM_9G/apps/fm9g_2b/tokenizer/tokenizer.json
new file mode 100644
index 0000000..209e947
--- /dev/null
+++ b/FM_9G/apps/fm9g_2b/tokenizer/tokenizer.json
@@ -0,0 +1,294435 @@
+{
+ "version": "1.0",
+ "truncation": null,
+ "padding": null,
+ "added_tokens": [
+ {
+ "id": 0,
+ "content": "",
+ "single_word": false,
+ "lstrip": false,
+ "rstrip": false,
+ "normalized": false,
+ "special": true
+ },
+ {
+ "id": 1,
+ "content": "",
+ "single_word": false,
+ "lstrip": false,
+ "rstrip": false,
+ "normalized": false,
+ "special": true
+ },
+ {
+ "id": 2,
+ "content": "",
+ "single_word": false,
+ "lstrip": false,
+ "rstrip": false,
+ "normalized": false,
+ "special": true
+ }
+ ],
+ "normalizer": {
+ "type": "Sequence",
+ "normalizers": [
+ {
+ "type": "Prepend",
+ "prepend": "▁"
+ },
+ {
+ "type": "Replace",
+ "pattern": {
+ "String": " "
+ },
+ "content": "▁"
+ }
+ ]
+ },
+ "pre_tokenizer": null,
+ "post_processor": {
+ "type": "TemplateProcessing",
+ "single": [
+ {
+ "SpecialToken": {
+ "id": "",
+ "type_id": 0
+ }
+ },
+ {
+ "Sequence": {
+ "id": "A",
+ "type_id": 0
+ }
+ }
+ ],
+ "pair": [
+ {
+ "SpecialToken": {
+ "id": "",
+ "type_id": 0
+ }
+ },
+ {
+ "Sequence": {
+ "id": "A",
+ "type_id": 0
+ }
+ },
+ {
+ "SpecialToken": {
+ "id": "",
+ "type_id": 1
+ }
+ },
+ {
+ "Sequence": {
+ "id": "B",
+ "type_id": 1
+ }
+ }
+ ],
+ "special_tokens": {
+ "": {
+ "id": "",
+ "ids": [
+ 1
+ ],
+ "tokens": [
+ ""
+ ]
+ }
+ }
+ },
+ "decoder": {
+ "type": "Sequence",
+ "decoders": [
+ {
+ "type": "Replace",
+ "pattern": {
+ "String": "▁"
+ },
+ "content": " "
+ },
+ {
+ "type": "ByteFallback"
+ },
+ {
+ "type": "Fuse"
+ },
+ {
+ "type": "Strip",
+ "content": " ",
+ "start": 1,
+ "stop": 0
+ }
+ ]
+ },
+ "model": {
+ "type": "BPE",
+ "dropout": null,
+ "unk_token": "",
+ "continuing_subword_prefix": null,
+ "end_of_word_suffix": null,
+ "fuse_unk": true,
+ "byte_fallback": true,
+ "vocab": {
+ "": 0,
+ "": 1,
+ "": 2,
+ "": 3,
+ "": 4,
+ "\n": 5,
+ "\t": 6,
+ "": 7,
+ "": 8,
+ "": 9,
+ "": 10,
+ "": 11,
+ "
": 12,
+ "": 13,
+ " | | ": 14,
+ "": 15,
+ "": 16,
+ "": 17,
+ "": 18,
+ "": 21,
+ "": 22,
+ "
": 23,
+ "": 24,
+ "": 25,
+ "": 26,
+ "": 27,
+ "": 28,
+ "": 29,
+ "": 30,
+ "": 31,
+ "": 32,
+ "
": 33,
+ "
": 34,
+ "
": 35,
+ "": 36,
+ "": 37,
+ "": 38,
+ "
": 39,
+ "": 40,
+ "": 41,
+ "
": 42,
+ "": 43,
+ "
": 44,
+ "
": 45,
+ "": 46,
+ "": 47,
+ "
": 48,
+ "": 49,
+ "": 50,
+ "": 51,
+ "0": 52,
+ "1": 53,
+ "2": 54,
+ "3": 55,
+ "4": 56,
+ "5": 57,
+ "6": 58,
+ "7": 59,
+ "8": 60,
+ "9": 61,
+ "+": 62,
+ "-": 63,
+ "=": 64,
+ ",": 65,
+ "。": 66,
+ "!": 67,
+ "?": 68,
+ "、": 69,
+ ":": 70,
+ "¥": 71,
+ ".": 72,
+ "!": 73,
+ "?": 74,
+ "...": 75,
+ "。。。": 76,
+ "。。。。。。": 77,
+ "《": 78,
+ "》": 79,
+ "【": 80,
+ "】": 81,
+ "『": 82,
+ "』": 83,
+ "```": 84,
+ "": 86,
+ "---": 87,
+ "": 88,
+ ";": 89,
+ ".": 90,
+ "=": 91,
+ "<": 92,
+ ">": 93,
+ "-": 94,
+ "+": 95,
+ "%": 96,
+ "‼": 97,
+ "㊣": 98,
+ "/": 99,
+ "|": 100,
+ "": 101,
+ "": 102,
+ "": 103,
+ "": 104,
+ "": 105,
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