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