Update data_args.py

src/llamafactory/hparams/data_args.py

@@ -31,12 +31,11 @@ class DataArguments:
     )
     dataset: Optional[str] = field(
         default=None,
-        metadata={"help": "The name of provided dataset(s) to use. Use commas to separate multiple datasets."},
+        metadata={"help": "The name of dataset(s) to use for training. Use commas to separate multiple datasets."},
     )
     eval_dataset: Optional[str] = field(
         default=None,
-        metadata={"help": "The name of provided dataset(s) to use for eval during training. "
-                          "Use commas to separate multiple datasets."},
+        metadata={"help": "The name of dataset(s) to use for evaluation. Use commas to separate multiple datasets."},
     )
     dataset_dir: str = field(
         default="data",
@@ -110,12 +109,33 @@ class DataArguments:
         default=None,
         metadata={"help": "Path to save or load the tokenized datasets."},
     )
-    eval_tokenized_path: Optional[str] = field(
-        default=None,
-        metadata={"help": "Path to save or load the tokenized eval datasets."},
-    )
 
     def __post_init__(self):
+        def split_arg(arg):
+            if isinstance(arg, str):
+                return [item.strip() for item in arg.split(",")]
+            return arg
+
+        self.dataset = split_arg(self.dataset)
+        self.eval_dataset = split_arg(self.eval_dataset)
+
+        if self.dataset is None and self.val_size > 1e-6:
+            raise ValueError("Cannot specify `val_size` if `dataset` is None.")
+
+        if self.eval_dataset is not None and self.val_size > 1e-6:
+            raise ValueError("Cannot specify `val_size` if `eval_dataset` is not None.")
+
+        if self.interleave_probs is not None:
+            if self.mix_strategy == "concat":
+                raise ValueError("`interleave_probs` is only valid for interleaved mixing.")
+
+            self.interleave_probs = list(map(float, split_arg(self.interleave_probs)))
+            if self.dataset is not None and len(self.dataset) != len(self.interleave_probs):
+                raise ValueError("The length of dataset and interleave probs should be identical.")
+
+            if self.eval_dataset is not None and len(self.eval_dataset) != len(self.interleave_probs):
+                raise ValueError("The length of eval dataset and interleave probs should be identical.")
+
         if self.streaming and self.val_size > 1e-6 and self.val_size < 1:
             raise ValueError("Streaming mode should have an integer val size.")