mirror of
https://github.com/hiyouga/LLaMA-Factory.git
synced 2025-07-31 10:42:50 +08:00
75 lines
2.9 KiB
Python
75 lines
2.9 KiB
Python
# Copyright 2025 the LlamaFactory team.
|
|
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
import json
|
|
import os
|
|
|
|
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 others},
|
|
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 = f"{_HF_ENDPOINT}/datasets/stingning/ultrachat"
|
|
_LICENSE = "cc-by-nc-4.0"
|
|
_BASE_DATA_URL = f"{_HF_ENDPOINT}/datasets/stingning/ultrachat/resolve/main/train_{{idx}}.jsonl"
|
|
|
|
|
|
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, 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}
|