forked from p04798526/LLaMA-Factory-Mirror
78 lines
2.8 KiB
Python
78 lines
2.8 KiB
Python
import os
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import json
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import datasets
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_HF_ENDPOINT = os.getenv("HF_ENDPOINT", "https://huggingface.co")
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_DESCRIPTION = "BELLE multiturn chat dataset."
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_CITATION = """\
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@article{belle2023exploring,
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title={Exploring the Impact of Instruction Data Scaling on Large Language Models: An Empirical Study on Real-World Use Cases},
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author={Yunjie Ji, Yong Deng, Yan Gong, Yiping Peng, Qiang Niu, Lei Zhang, Baochang Ma, Xiangang Li},
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journal={arXiv preprint arXiv:2303.14742},
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year={2023}
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}
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"""
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_HOMEPAGE = f"{_HF_ENDPOINT}/datasets/BelleGroup/multiturn_chat_0.8M"
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_LICENSE = "gpl-3.0"
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_URL = f"{_HF_ENDPOINT}/datasets/BelleGroup/multiturn_chat_0.8M/resolve/main/multiturn_chat_0.8M.json"
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class BelleMultiturn(datasets.GeneratorBasedBuilder):
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VERSION = datasets.Version("0.0.0")
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def _info(self):
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features = datasets.Features({
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"conversations": [{"from": datasets.Value("string"), "value": datasets.Value("string")}]
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})
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=features,
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homepage=_HOMEPAGE,
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license=_LICENSE,
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citation=_CITATION
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)
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def _split_generators(self, dl_manager: datasets.DownloadManager):
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file_path = dl_manager.download(_URL)
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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gen_kwargs={
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"filepath": file_path
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}
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)
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]
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def _generate_examples(self, filepath: str):
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with open(filepath, "r", encoding="utf-8") as f:
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for key, row in enumerate(f):
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data = json.loads(row)
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conversations = []
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prompt = data["instruction"].strip()
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response = data["output"].strip()
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assist_idx = prompt.rfind("Assistant:")
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human_idx = prompt.rfind("Human:")
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query = prompt[human_idx+6:assist_idx].strip()
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prompt = prompt[:human_idx].strip()
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conversations.insert(0, {"from": "gpt", "value": response})
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conversations.insert(0, {"from": "human", "value": query})
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while prompt.rfind("Assistant:") != -1:
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assist_idx = prompt.rfind("Assistant:")
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human_idx = prompt.rfind("Human:")
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if human_idx != -1:
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old_query = prompt[human_idx+6:assist_idx].strip()
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old_resp = prompt[assist_idx+10:].strip()
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conversations.insert(0, {"from": "gpt", "value": old_resp})
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conversations.insert(0, {"from": "human", "value": old_query})
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else:
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break
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prompt = prompt[:human_idx].strip()
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yield key, {"conversations": conversations}
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