# Bert-Multi-Label-Text-Classification

**Repository Path**: liugj101/Bert-Multi-Label-Text-Classification

## Basic Information

- **Project Name**: Bert-Multi-Label-Text-Classification
- **Description**: This repo contains a PyTorch implementation of a pretrained BERT model for multi-label text classification.
- **Primary Language**: Python
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-03-27
- **Last Updated**: 2021-11-02

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Bert multi-label text classification by PyTorch

This repo contains a PyTorch implementation of a pretrained BERT model  for multi-label text classification.

**note**: for the new `pytorch-pretrained-bert` package . use comd `from pytorch_pretrained_bert.modeling import BertPreTrainedModel`
## Structure of the code

At the root of the project, you will see:

```text
├── pybert
|  └── callback
|  |  └── lrscheduler.py　　
|  |  └── trainingmonitor.py　
|  |  └── ...
|  └── config
|  |  └── basic_config.py #a configuration file for storing model parameters
|  └── dataset　　　
|  └── io　　　　
|  |  └── dataset.py　　
|  |  └── data_transformer.py　　
|  └── model
|  |  └── nn　
|  |  └── pretrain　
|  └── output #save the ouput of model
|  └── preprocessing #text preprocessing 
|  └── train #used for training a model
|  |  └── trainer.py 
|  |  └── ...
|  └── utils # a set of utility functions
├── convert_tf_checkpoint_to_pytorch.py
├── train_bert_multi_label.py
├── inference.py
```
## Dependencies

- csv
- tqdm
- numpy
- pickle
- scikit-learn
- PyTorch 1.0
- matplotlib
- pandas
- pytorch_pretrained_bert (load bert model)

## How to use the code

you need download pretrained bert model (`uncased_L-12_H-768_A-12`)

1. Download the Bert pretrained model from [Google](https://storage.googleapis.com/bert_models/2018_10_18/uncased_L-12_H-768_A-12.zip) and place it into the `/pybert/model/pretrain` directory.
2. `pip install pytorch-pretrained-bert` from [github](https://github.com/huggingface/pytorch-pretrained-BERT).
3. Run `python convert_tf_checkpoint_to_pytorch.py` to transfer the pretrained model(tensorflow version)  into pytorch form .
4. Prepare [kaggle data](https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge/data), you can modify the `io.data_transformer.py` to adapt your data.
5. Modify configuration information in `pybert/config/basic_config.py`(the path of data,...).
6. Run `python train_bert_multi_label.py` to fine tuning bert model.
7. Run `python inference.py` to predict new data.

## Fine-tuning result

### training 

```text
[training] 8511/8511 [>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] -0.8s/step- loss: 0.0640
training result:
[2019-01-14 04:01:05]: bert-multi-label trainer.py[line:176] INFO  
Epoch: 2 - loss: 0.0338 - val_loss: 0.0373 - val_auc: 0.9922
```

### training figure

![]( https://lonepatient-1257945978.cos.ap-chengdu.myqcloud.com/20190214210111.png)

### result

```python
---- train report every label -----
Label: toxic - auc: 0.9903
Label: severe_toxic - auc: 0.9913
Label: obscene - auc: 0.9951
Label: threat - auc: 0.9898
Label: insult - auc: 0.9911
Label: identity_hate - auc: 0.9910
---- valid report every label -----
Label: toxic - auc: 0.9892
Label: severe_toxic - auc: 0.9911
Label: obscene - auc: 0.9945
Label: threat - auc: 0.9955
Label: insult - auc: 0.9903
Label: identity_hate - auc: 0.9927
```

## Tips

- When converting the tensorflow checkpoint into the pytorch, it's expected to choice the "bert_model.ckpt", instead of "bert_model.ckpt.index", as the input file. Otherwise, you will see that the model can learn nothing and give almost same random outputs for any inputs. This means, in fact, you have not loaded the true ckpt for your model
- When using multiple GPUs, the non-tensor calculations, such as accuracy and f1_score, are not supported by DataParallel instance
- As recommanded by Jocob in his paper <url>https://arxiv.org/pdf/1810.04805.pdf<url/>, in fine-tuning tasks, the hyperparameters are expected to set as following: **Batch_size**: 16 or 32, **learning_rate**: 5e-5 or 2e-5 or 3e-5, **num_train_epoch**: 3 or 4
- The pretrained model has a limit for the sentence of input that its length should is not larger than 512, the max position embedding dim. The data flows into the model as: Raw_data -> WordPieces -> Model. Note that the length of wordPieces is generally larger than that of raw_data, so a safe max length of raw_data is at ~128 - 256 
- Upon testing, we found that fine-tuning all layers could get much better results than those of only fine-tuning the last classfier layer. The latter is actually a feature-based way