ddonng/ernie-text-classification-pytorch

This repo contains a PyTorch implementation of a pretrained ERNIE model for text classification.

ERNIE text classification by PyTorch

This repo contains a PyTorch implementation of a pretrained ERNIE model for text classification.

arxiv: https://arxiv.org/abs/1904.09223v1

Structure of the code

At the root of the project, you will see:

├── pyernie
|  └── 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_ernie_to_pytorch.py
├── fine_tune_ernie.py

Dependencies

• csv
• tqdm
• numpy
• pickle
• scikit-learn
• PyTorch 1.0
• matplotlib
• tensorboardX
• Tensorflow (to be able to run TensorboardX)

How to use the code

you need download pretrained ERNIE model

1. Download the pretrained ERNIE model from baiduPan {password: uwds} and place it into the /pyernie/model/pretrain directory.

2. prepare Chinese raw data(example,news data), you can modify the io.data_transformer.py to adapt your data.

3. Modify configuration information in pyernie/config/basic_config.py(the path of data,...).

4. run fine_tune_ernie.py.

Fine-tuning result

training

Epoch: 4 - loss: 0.0136 - f1: 0.9967 - valid_loss: 0.0761 - valid_f1: 0.9798

train classify_report

label	precision	recall	f1-score	support
财经	0.99	0.99	0.99	3500
体育	1.00	1.00	1.00	3500
娱乐	1.00	1.00	1.00	3500
家居	1.00	1.00	1.00	3500
房产	0.99	0.99	0.99	3500
教育	1.00	0.99	1.00	3500
时尚	1.00	1.00	1.00	3500
时政	1.00	1.00	1.00	3500
游戏	1.00	1.00	1.00	3500
科技	0.99	1.00	1.00	3500
avg / total	1.00	1.00	1.00	35000

valid classify_report

label	precision	recall	f1-score	support
财经	0.97	0.96	0.96	1500
体育	1.00	1.00	1.00	1500
娱乐	0.99	0.99	0.99	1500
家居	0.99	0.99	0.99	1500
房产	0.96	0.96	0.96	1500
教育	0.98	0.98	0.98	1500
时尚	0.99	0.99	0.99	1500
时政	0.97	0.98	0.98	1500
游戏	0.99	0.99	0.99	1500
科技	0.97	0.97	0.97	1500
avg / total	0.98	0.98	0.98	15000

training figure

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 https://arxiv.org/pdf/1810.04805.pdf, 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