LLaMA-Factory/data/belle_multiturn/belle_multiturn.py

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