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| # 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} | |