70 lines
2.4 KiB
Python
70 lines
2.4 KiB
Python
import json
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import datasets
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from typing import List
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_DESCRIPTION = "UltraChat: Large-scale, Informative, and Diverse Multi-round Dialogue Data."
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_CITATION = """\
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@misc{UltraChat,
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author = {Ding, Ning and Chen, Yulin and Xu, Bokai and Hu, Shengding and Qin, Yujia and Liu, Zhiyuan and Sun, Maosong and Zhou, Bowen},
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title = {UltraChat: A Large-scale Auto-generated Multi-round Dialogue Data},
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year = {2023},
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publisher = {GitHub},
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journal = {GitHub repository},
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howpublished = {\\url{https://github.com/thunlp/ultrachat}},
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}
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"""
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_HOMEPAGE = "https://huggingface.co/datasets/stingning/ultrachat"
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_LICENSE = "cc-by-nc-4.0"
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_BASE_DATA_URL = "https://huggingface.co/datasets/stingning/ultrachat/resolve/main/train_{idx}.jsonl"
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class UltraChat(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_paths = [dl_manager.download(_BASE_DATA_URL.format(idx=idx)) for idx in range(10)] # multiple shards
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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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"filepaths": file_paths
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}
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)
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]
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def _generate_examples(self, filepaths: List[str]):
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for filepath in filepaths:
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with open(filepath, "r", encoding="utf-8") as f:
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for row in f:
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try:
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data = json.loads(row)
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except:
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continue
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key: int = data["id"]
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content: List[str] = data["data"]
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if len(content) % 2 == 1:
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content.pop(-1)
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if len(content) < 2:
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continue
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conversations = [{
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"from": "human" if i % 2 == 0 else "gpt",
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"value": content[i]
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} for i in range(len(content))]
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yield key, {"conversations": conversations}
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