mirror of
https://github.com/hiyouga/LLaMA-Factory.git
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83 lines
3.3 KiB
Python
83 lines
3.3 KiB
Python
# Copyright 2025 the LlamaFactory team.
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# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import json
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import os
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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},
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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, features=features, homepage=_HOMEPAGE, license=_LICENSE, 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 [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, 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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