| import json |
| import os |
|
|
| import datasets |
|
|
|
|
| _HF_ENDPOINT = os.getenv("HF_ENDPOINT", "https://huggingface.co") |
|
|
| _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 = "{}/datasets/BelleGroup/multiturn_chat_0.8M".format(_HF_ENDPOINT) |
| _LICENSE = "gpl-3.0" |
| _URL = "{}/datasets/BelleGroup/multiturn_chat_0.8M/resolve/main/multiturn_chat_0.8M.json".format(_HF_ENDPOINT) |
|
|
|
|
| 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} |
|
|
