- 最好配合huggingface/tokenizers 使用(当然中文简单按字分词也差不太多)
- 配合hub食用更佳,当然下载下来只用tf.keras.models.load_model也可以
python:
上面两个版本的区别相当于,一个在tensorflow_hub.load的时候,trainable=True,另一个trainable=False
更新BERT | 不更新 | |
---|---|---|
pros | 部分任务会效果更好 | 训练速度较快 |
cons | 训练速度会慢,因为要保存大量BERT的梯度 | 部分任务效果可能变差 |
node:
NodeJS的文本分类模型训练
下面是一个简单的文本分类例子
最简单用法:
安装依赖
$ pip install tensorflow tensorflow-text tensorflow-hub
import tensorflow as tf
import tensorflow_text
import tensorflow_hub as hub
tokenizer = hub.load(
'https://code.aliyun.com/qhduan/bert_v4/raw/500019068f2c715d4b344c3e2216cef280a7f800/bert_tokenizer_chinese.tar.gz'
)
albert = hub.load(
'https://code.aliyun.com/qhduan/bert_v4/raw/500019068f2c715d4b344c3e2216cef280a7f800/albert_tiny.tar.gz'
)
out = albert(tokenizer(['你好']))
assert out['sequence_output'].shape == (1, 2, 312)
assert out['pooled_output'].shape == (1, 312)
# 分词器 78k
https://code.aliyun.com/qhduan/bert_v4/raw/500019068f2c715d4b344c3e2216cef280a7f800/bert_tokenizer_chinese.tar.gz
# 16MB
https://code.aliyun.com/qhduan/bert_v4/raw/500019068f2c715d4b344c3e2216cef280a7f800/albert_tiny.tar.gz
# 18MB
https://code.aliyun.com/qhduan/bert_v4/raw/500019068f2c715d4b344c3e2216cef280a7f800/albert_small.tar.gz
# 39MB
https://code.aliyun.com/qhduan/bert_v4/raw/500019068f2c715d4b344c3e2216cef280a7f800/albert_base.tar.gz
# 61MB
https://code.aliyun.com/qhduan/bert_v4/raw/500019068f2c715d4b344c3e2216cef280a7f800/albert_large.tar.gz
# 207MB
https://code.aliyun.com/qhduan/bert_v4/raw/500019068f2c715d4b344c3e2216cef280a7f800/albert_xlarge.tar.gz
# 364MB
https://code.aliyun.com/qhduan/bert_v4/raw/500019068f2c715d4b344c3e2216cef280a7f800/bert.tar.gz
# 364MB
https://code.aliyun.com/qhduan/bert_v4/raw/500019068f2c715d4b344c3e2216cef280a7f800/bert_wwm.tar.gz
# 46MB
https://code.aliyun.com/qhduan/bert_v4/raw/500019068f2c715d4b344c3e2216cef280a7f800/electra_small.tar.gz
# 91MB
https://code.aliyun.com/qhduan/bert_v4/raw/500019068f2c715d4b344c3e2216cef280a7f800/electra_smallex.tar.gz
# 365MB
https://code.aliyun.com/qhduan/bert_v4/raw/500019068f2c715d4b344c3e2216cef280a7f800/electra_base.tar.gz
# 364MB
https://code.aliyun.com/qhduan/bert_v4/raw/500019068f2c715d4b344c3e2216cef280a7f800/roberta_wwm.tar.gz
# 1层roberta-wwm 87MB
https://code.aliyun.com/qhduan/bert_part/raw/508f4beacff09890d60fbaf273dff6c0d4932863/roberta_wwm_1.tar.gz
# 1层roberta-wwm-large 128MB
https://code.aliyun.com/qhduan/bert_part/raw/508f4beacff09890d60fbaf273dff6c0d4932863/roberta_wwm_large_1.tar.gz
# 3层roberta-wwm 138MB
https://code.aliyun.com/qhduan/bert_part/raw/6c8b798cf7d6d0a12de20c4f90c870df2e107977/roberta_wwm_3.tar.gz
# 3层roberta-wwm-large 217MB
https://code.aliyun.com/qhduan/bert_part/raw/6c8b798cf7d6d0a12de20c4f90c870df2e107977/roberta_wwm_large_3.tar.gz
# FinBERT https://github.com/valuesimplex/FinBERT
https://code.aliyun.com/qhduan/bert_part/raw/0cf046f83ee12cb6ee0311eaa00f5f1e062f2c72/FinBERT.tar.gz
词表,所有的中文BERT几乎都是基于谷歌最开始发布的词表
https://code.aliyun.com/qhduan/zh-bert/raw/0fb1d96ec2133fe25e66bee12fe387cbe1e52938/vocab.txt
新的版本(new)有四个输出,老的版本(old)只有三个输出
tiny~15MB
small~18MB
base~40MB
large~60MB
xlarge~200MB
albert的学习率设计比较麻烦,建议尝试5e-5左右
https://code.aliyun.com/qhduan/bert/raw/ff3e375101b6dc11e4aed06042aac7e4656c78ea/albert_tiny.tar.gz
https://code.aliyun.com/qhduan/bert/raw/ff3e375101b6dc11e4aed06042aac7e4656c78ea/albert_small.tar.gz
https://code.aliyun.com/qhduan/bert/raw/0a53cbdce78a16053ab0034cefe21caa37bdf128/albert_base.tar.gz
https://code.aliyun.com/qhduan/bert/raw/0a53cbdce78a16053ab0034cefe21caa37bdf128/albert_large.tar.gz
https://code.aliyun.com/qhduan/bert/raw/0a53cbdce78a16053ab0034cefe21caa37bdf128/albert_xlarge.tar.gz
~366MB
https://code.aliyun.com/qhduan/bert/raw/c046e359e0f48593db2762a691dae6b6b83942bb/zh-bert-L12.tar.gz
https://code.aliyun.com/qhduan/bert/raw/c046e359e0f48593db2762a691dae6b6b83942bb/zh-bert-wwm-L12.tar.gz
https://code.aliyun.com/qhduan/bert/raw/c046e359e0f48593db2762a691dae6b6b83942bb/zh-roberta-wwm-L12.tar.gz
谷歌最开始的预训练中文,12层,文件中的LX,代表包含几层,L12就是完整模型
https://code.aliyun.com/qhduan/zh-bert/raw/0fb1d96ec2133fe25e66bee12fe387cbe1e52938/zh-bert-L1.tar.gz
https://code.aliyun.com/qhduan/zh-bert/raw/0fb1d96ec2133fe25e66bee12fe387cbe1e52938/zh-bert-L3.tar.gz
https://code.aliyun.com/qhduan/zh-bert/raw/0fb1d96ec2133fe25e66bee12fe387cbe1e52938/zh-bert-L6.tar.gz
https://code.aliyun.com/qhduan/zh-bert/raw/0fb1d96ec2133fe25e66bee12fe387cbe1e52938/zh-bert-L9.tar.gz
https://code.aliyun.com/qhduan/zh-bert/raw/0fb1d96ec2133fe25e66bee12fe387cbe1e52938/zh-bert-L12.tar.gz
ymcui/Chinese-BERT-wwm 发布的预训练模型,12层,文件中的LX,代表包含几层,L12就是完整模型
https://code.aliyun.com/qhduan/zh-bert-wwm/raw/51f499fb7df00cf4d0f283cefd0a7cb34316b866/zh-bert-wwm-L1.tar.gz
https://code.aliyun.com/qhduan/zh-bert-wwm/raw/51f499fb7df00cf4d0f283cefd0a7cb34316b866/zh-bert-wwm-L3.tar.gz
https://code.aliyun.com/qhduan/zh-bert-wwm/raw/51f499fb7df00cf4d0f283cefd0a7cb34316b866/zh-bert-wwm-L6.tar.gz
https://code.aliyun.com/qhduan/zh-bert-wwm/raw/51f499fb7df00cf4d0f283cefd0a7cb34316b866/zh-bert-wwm-L9.tar.gz
https://code.aliyun.com/qhduan/zh-bert-wwm/raw/51f499fb7df00cf4d0f283cefd0a7cb34316b866/zh-bert-wwm-L12.tar.gz
ymcui/Chinese-BERT-wwm 发布的预训练模型,12层,文件中的LX,代表包含几层,L12就是完整模型
roBERTa
https://code.aliyun.com/qhduan/zh-roberta-wwm/raw/2c0d7fd709e4719a9ab2ca297f51b24e20586dbe/zh-roberta-wwm-L1.tar.gz
https://code.aliyun.com/qhduan/zh-roberta-wwm/raw/2c0d7fd709e4719a9ab2ca297f51b24e20586dbe/zh-roberta-wwm-L3.tar.gz
https://code.aliyun.com/qhduan/zh-roberta-wwm/raw/096f6b9459fff95ee3ea6370c38bcbce3c883f7d/zh-roberta-wwm-L6.tar.gz
https://code.aliyun.com/qhduan/zh-roberta-wwm/raw/2c0d7fd709e4719a9ab2ca297f51b24e20586dbe/zh-roberta-wwm-L9.tar.gz
https://code.aliyun.com/qhduan/zh-roberta-wwm/raw/2c0d7fd709e4719a9ab2ca297f51b24e20586dbe/zh-roberta-wwm-L12.tar.gz
ymcui/Chinese-BERT-wwm 发布的预训练模型,24层,文件中的LX,代表包含几层,L12就是完整模型
https://code.aliyun.com/qhduan/zh-roberta-wwm-large/raw/47e2d9f33e0fa5f06eeb4a90e68c79c4d3b03ce7/zh-roberta-wwm-large-L12.tar.gz
https://code.aliyun.com/qhduan/zh-roberta-wwm-large/raw/47e2d9f33e0fa5f06eeb4a90e68c79c4d3b03ce7/zh-roberta-wwm-large-L24.tar.gz