bert-chunker-chinese

bert-chunker-chinese (中文分段器) is a Chinese version of bert-chunker. It's a Chinese text chunker based on BERT with a classifier head to predict the start token of chunks (for use in RAG, etc), and using a sliding window it cuts documents of any size into chunks. We see it as an alternative of semantic chunker. but specially, it not only works for the structured texts, but also the unstructured and messy texts.

It was finetuned on top of bge-small-zh-v1.5, dataset is synthesized from chinese-c4.

This repo includes model checkpoint, BertChunker class definition file and all the other files needed.

The latest, more stable version is（最新、更稳定的版本是） bert-chunker-Chinese-2.

Quickstart

Download this repository. Then enter it. Run the following:

# -*- coding: utf-8 -*-
import safetensors
from transformers import AutoConfig,AutoTokenizer
from modeling_bertchunke_zh import BertChunker

# load config and tokenizer
config = AutoConfig.from_pretrained(
    "tim1900/bert-chunker-chinese",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(
    "tim1900/bert-chunker-chinese",
    padding_side="right",
    model_max_length=config.max_position_embeddings,
    trust_remote_code=True,
)

# initialize model
model = BertChunker(config)
device='cpu' # or 'cuda'
model.to(device)

# load tim1900/bert-chunker-chinese/model.safetensors
state_dict = safetensors.torch.load_file(f"./model.safetensors")
model.load_state_dict(state_dict)

# text to be chunked
text='''起点中文网(www.qidian.com)创立于2002年5月，是国内知名的原创文学网站，隶属于阅文集团旗下。起点中文网以推动中国原创文学事业为宗旨，长期致力于原创文学作者的挖掘与培养，并取得了巨大成果：2003年10月，起点中文网开启“在线收费阅读”服务，成为真正意义上的网络文学赢利模式的先锋之一，就此奠定了原创文学的行业基础。此后，起点又推出了作家福利、文学交互、内容发掘推广、版权管理等机制和体系，为原创文学的发展注入了巨大活力，有力推动了中国文学原创事业的发展。在清晨的微光中，一只孤独的猫头鹰在古老的橡树上低声吟唱，它的歌声如同夜色的回声，穿越了时间的迷雾。树叶在微风中轻轻摇曳，仿佛在诉说着古老的故事，每一个音符都带着森林的秘密。一位年轻的程序员正专注地敲打着键盘，代码的海洋在他眼前展开。他的手指在键盘上飞舞，如同钢琴家在演奏一曲复杂的交响乐。屏幕上的光标闪烁，仿佛在等待着下一个指令，引领他进入未知的数字世界。'''

# chunk the text. The lower threshold is, the more chunks will be generated.
chunks=model.chunk_text(text, tokenizer, threshold=0.5)

# print chunks
for i, c in enumerate(chunks):
    print(f'-----chunk: {i}------------')
    print(c)