https://github.com/FakerYFX/Bert-Pytorch-Chinese-TextClassification

Pytorch Bert Finetune in Chinese Text Classification
https://github.com/FakerYFX/Bert-Pytorch-Chinese-TextClassification

Last synced: 6 days ago
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Pytorch Bert Finetune in Chinese Text Classification

• Host: GitHub
• URL: https://github.com/FakerYFX/Bert-Pytorch-Chinese-TextClassification
• Owner: FakerYFX
• Created: 2019-01-05T08:37:07.000Z (almost 6 years ago)
• Default Branch: master
• Last Pushed: 2024-04-11T14:10:38.000Z (7 months ago)
• Last Synced: 2024-08-11T16:09:17.709Z (3 months ago)
• Language: Python
• Size: 23.4 KB
• Stars: 211
• Watchers: 3
• Forks: 35
• Open Issues: 4
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• awesome-bert - xieyufei1993/Bert-Pytorch-Chinese-TextClassification

README

        
# Bert-Pytorch-Chinese-TextClassification

Pytorch Bert Finetune in Chinese Text Classification

### Step 1

Download the pretrained TensorFlow model:[chinese_L-12_H-768_A-12](https://storage.googleapis.com/bert_models/2018_11_03/chinese_L-12_H-768_A-12.zip)

### Step 2

Change the TensorFlow Pretrained Model into Pytorch

```shell

cd  convert_tf_to_pytorch

```

```shell

export BERT_BASE_DIR=/workspace/mnt/group/ocr/xieyufei/bert-tf-chinese/chinese_L-12_H-768_A-12

python3 convert_tf_checkpoint_to_pytorch.py \

  --tf_checkpoint_path $BERT_BASE_DIR/bert_model.ckpt \

  --bert_config_file $BERT_BASE_DIR/bert_config.json \

  --pytorch_dump_path $BERT_BASE_DIR/pytorch_model.bin

```

### Step 3

Download the Chinese News DataSet:[Train](https://pan.baidu.com/s/15rkzx-YRbP5XRNeapzYWLw) for 5w and [Dev](https://pan.baidu.com/s/1HuYTacgAQFqGAJ8FYXNqOw) for 5k

### Step 4

Just Train and Test

```shell

cd src

```

```shell

export GLUE_DIR=/workspace/mnt/group/ocr/xieyufei/bert-tf-chinese/glue_data

export BERT_BASE_DIR=/workspace/mnt/group/ocr/xieyufei/bert-tf-chinese/chinese_L-12_H-768_A-12/

export BERT_PYTORCH_DIR=/workspace/mnt/group/ocr/xieyufei/bert-tf-chinese/chinese_L-12_H-768_A-12/

python3 run_classifier_word.py \

  --task_name NEWS \

  --do_train \

  --do_eval \

  --data_dir $GLUE_DIR/SouGou/ \

  --vocab_file $BERT_BASE_DIR/vocab.txt \

  --bert_config_file $BERT_BASE_DIR/bert_config.json \

  --init_checkpoint $BERT_PYTORCH_DIR/pytorch_model.bin \

  --max_seq_length 256 \

  --train_batch_size 24 \

  --learning_rate 2e-5 \

  --num_train_epochs 50.0 \

  --output_dir ./newsAll_output/ \

  --local_rank 3

```

1个Epoch的结果如下：

```

eval_accuracy = 0.9742

eval_loss = 0.10202122390270234

global_step = 2084

loss = 0.15899521649851786

```