metadata
language: zh
tags:
- glycebert
inference: false
https://github.com/JunnYu/ChineseBert_pytorch
ChineseBert_pytorch
本项目主要自定义了tokenization_chinesebert_fast.py文件中的ChineseBertTokenizerFast代码。从而可以从huggingface.co调用。
pretrained_tokenizer_name = "junnyu/ChineseBERT-base"
tokenizer = ChineseBertTokenizerFast.from_pretrained(pretrained_tokenizer_name)
Paper
ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin Information
Zijun Sun, Xiaoya Li, Xiaofei Sun, Yuxian Meng, Xiang Ao, Qing He, Fei Wu and Jiwei Li
Install
pip install chinesebert
or
pip install git+https://github.com/JunnYu/ChineseBert_pytorch.git
Usage
import torch
from chinesebert import ChineseBertForMaskedLM, ChineseBertTokenizerFast, ChineseBertConfig
pretrained_model_name = "junnyu/ChineseBERT-base"
tokenizer = ChineseBertTokenizerFast.from_pretrained(pretrained_model_name)
chinese_bert = ChineseBertForMaskedLM.from_pretrained(pretrained_model_name)
text = "北京是[MASK]国的首都。"
inputs = tokenizer(text, return_tensors="pt")
print(inputs)
maskpos = 4
with torch.no_grad():
o = chinese_bert(**inputs)
value, index = o.logits.softmax(-1)[0, maskpos].topk(10)
pred_tokens = tokenizer.convert_ids_to_tokens(index.tolist())
pred_values = value.tolist()
outputs = []
for t, p in zip(pred_tokens, pred_values):
outputs.append(f"{t}|{round(p,4)}")
print(outputs)
# base ['中|0.711', '我|0.2488', '祖|0.016', '法|0.0057', '美|0.0048', '全|0.0042', '韩|0.0015', '英|0.0011', '两|0.0008', '王|0.0006']
# large ['中|0.8341', '我|0.1479', '祖|0.0157', '全|0.0007', '国|0.0005', '帝|0.0001', '该|0.0001', '法|0.0001', '一|0.0001', '咱|0.0001']