| 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") | |
| def _info(self): | |
| features = datasets.Features({ | |
| "conversations": [{"from": datasets.Value("string"), "value": datasets.Value("string")}] | |
| }) | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=features, | |
| homepage=_HOMEPAGE, | |
| license=_LICENSE, | |
| citation=_CITATION | |
| ) | |
| def _split_generators(self, dl_manager: datasets.DownloadManager): | |
| file_path = dl_manager.download(_URL) | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| gen_kwargs={ | |
| "filepath": file_path | |
| } | |
| ) | |
| ] | |
| def _generate_examples(self, filepath: str): | |
| with open(filepath, "r", encoding="utf-8") as f: | |
| for key, row in enumerate(f): | |
| data = json.loads(row) | |
| conversations = [] | |
| 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() | |
| conversations.insert(0, {"from": "gpt", "value": response}) | |
| conversations.insert(0, {"from": "human", "value": query}) | |
| 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() | |
| conversations.insert(0, {"from": "gpt", "value": old_resp}) | |
| conversations.insert(0, {"from": "human", "value": old_query}) | |
| else: | |
| break | |
| prompt = prompt[:human_idx].strip() | |
| yield key, {"conversations": conversations} | |