LLaMA-Factory-310P3/data
hiyouga 7159bc54ed add datasets 2023-07-19 20:59:15 +08:00
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belle_multiturn add belle multiturn dataset 2023-06-16 20:01:16 +08:00
example_dataset Initial commit 2023-05-28 18:09:04 +08:00
hh_rlhf_en Initial commit 2023-05-28 18:09:04 +08:00
ultra_chat Initial commit 2023-05-28 18:09:04 +08:00
README.md add datasets 2023-07-19 20:59:15 +08:00
alpaca_data_en_52k.json Initial commit 2023-05-28 18:09:04 +08:00
alpaca_data_zh_51k.json Initial commit 2023-05-28 18:09:04 +08:00
alpaca_gpt4_data_en.json Initial commit 2023-05-28 18:09:04 +08:00
alpaca_gpt4_data_zh.json Initial commit 2023-05-28 18:09:04 +08:00
comparison_gpt4_data_en.json support RM metrics, add generating Args 2023-06-12 15:48:48 +08:00
comparison_gpt4_data_zh.json support RM metrics, add generating Args 2023-06-12 15:48:48 +08:00
dataset_info.json add datasets 2023-07-19 20:59:15 +08:00
oaast_rm.json add open assistant dataset 2023-06-28 23:09:33 +08:00
oaast_rm_zh.json add open assistant dataset 2023-06-28 23:09:33 +08:00
oaast_sft.json add open assistant dataset 2023-06-28 23:09:33 +08:00
oaast_sft_zh.json add open assistant dataset 2023-06-28 23:09:33 +08:00
refgpt_zh_50k_p1.json add datasets 2023-07-19 20:59:15 +08:00
refgpt_zh_50k_p2.json add datasets 2023-07-19 20:59:15 +08:00
self_cognition.json add datasets 2023-07-19 20:59:15 +08:00
sharegpt_zh_27k.json add datasets 2023-07-19 20:59:15 +08:00
wiki_demo.txt add pre-training script 2023-05-29 21:37:22 +08:00

README.md

If you are using a custom dataset, please provide your dataset definition in the following format in dataset_info.json.

"dataset_name": {
    "hf_hub_url": "the name of the dataset repository on the HuggingFace hub. (if specified, ignore below 3 arguments)",
    "script_url": "the name of the directory containing a dataset loading script. (if specified, ignore below 2 arguments)",
    "file_name": "the name of the dataset file in the this directory. (required if above are not specified)",
    "file_sha1": "the SHA-1 hash value of the dataset file. (optional)",
    "columns": {
        "prompt": "the name of the column in the datasets containing the prompts. (default: instruction)",
        "query": "the name of the column in the datasets containing the queries. (default: input)",
        "response": "the name of the column in the datasets containing the responses. (default: output)",
        "history": "the name of the column in the datasets containing the history of chat. (default: None)"
    }
}

where the prompt and response columns should contain non-empty values. The query column will be concatenated with the prompt column and used as input for the model. The history column should contain a list where each element is a string tuple representing a query-response pair.