LLaMA-Factory-Mirror/data/belle_multiturn/belle_multiturn.py

80 lines
2.9 KiB
Python

import json
import datasets
from typing import Any, Dict, List
_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 = "https://huggingface.co/datasets/BelleGroup/multiturn_chat_0.8M"
_LICENSE = "gpl-3.0"
_URL = "https://huggingface.co/datasets/BelleGroup/multiturn_chat_0.8M/resolve/main/multiturn_chat_0.8M.json"
class BelleMultiturn(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("0.0.0")
def _info(self) -> datasets.DatasetInfo:
features = datasets.Features({
"instruction": datasets.Value("string"),
"output": datasets.Value("string"),
"history": datasets.Sequence(datasets.Sequence(datasets.Value("string")))
})
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION
)
def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
file_path = dl_manager.download(_URL)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": file_path
}
)
]
def _generate_examples(self, filepath: str) -> Dict[int, Dict[str, Any]]: # generate multi-turn chat with history
with open(filepath, "r", encoding="utf-8") as f:
for key, row in enumerate(f):
data = json.loads(row)
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()
history = []
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()
history.insert(0, (old_query, old_resp))
else:
break
prompt = prompt[:human_idx].strip()
yield key, {
"instruction": query,
"output": response,
"history": history
}