DeepClassifier

DeepClassifier is a python package based on pytorch, which is easy-use and general for text classification task. You can install DeepClassifier by pip install -U deepclassifier。 If you want to know more information about DeepClassifier, please see the documentation. So let's start!🤩

If you think DeepClassifier is good, please star and fork it to give me motivation to continue maintenance！🤩 And it's my pleasure that if Deepclassifier is helpful to you!🥰

Installation

Just like other Python packages, DeepClassifier also can be installed through pip.The command of installation is pip install -U deepclassifier.

Models

Here is a list of models that have been integrated into DeepClassifier. In the future, we will integrate more models into DeepClassifier. Welcome to join us!🤩

1. TextCNN:Convolutional Neural Networks for Sentence Classification ,2014 EMNLP
2. RCNN:Recurrent Convolutional Neural Networks for Text Classification,2015,IJCAI
3. DPCNN:Deep Pyramid Convolutional Neural Networks for Text Categorization ,2017,ACL
4. HAN:Hierarchical Attention Networks for Document Classification, 2016,ACL
5. BERT:BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,2018, ACL
6. BertTextCNN: BERT+TextCNN
7. BertRCNN: BERT+RCNN
8. BertDPCNN: BERT+DPCNN
9. BertHAN: BERT+HAN ...

Quick start

I wiil show you that how to use DeepClassifier below.🥰 Click [here] to display the complete code.

you can define model like that(take BertTextCNN model as example):👇

fromdeepclassifier.modelsimportBertTextCNN# parameters of modelembedding_dim=768# if you use bert, the default is 768.dropout_rate=0.2num_class=2bert_path="/Users/codewithzichao/Desktop/bert-base-uncased/"my_model=BertTextCNN(embedding_dim=embedding_dim,
dropout_rate=dropout_rate,
num_class=num_class,
bert_path=bert_path)
optimizer=optim.Adam(my_model.parameters())
loss_fn=nn.CrossEntropyLoss()

After defining model, you can train/test/predict model like that:👇

fromdeepclassifier.trainersimportTrainermodel_name="berttextcnn"save_path="best.ckpt"writer=SummaryWriter("logfie/1")
max_norm=0.25eval_step_interval=20my_trainer=Trainer(model_name=model_name,model=my_model,
train_loader=train_loader,dev_loader=dev_loader,
test_loader=test_loader, optimizer=optimizer, loss_fn=loss_fn,save_path=save_path, epochs=1, writer=writer, max_norm=max_norm,
eval_step_interval=eval_step_interval)
# trainingmy_trainer.train()
# print the best F1 value on dev setprint(my_trainer.best_f1)
# testingp, r, f1=my_trainer.test()
print(p, r, f1)
# predictpred_data=DataLoader(pred_data, batch_size=1)
pred_label=my_trainer.predict(pred_data)
print(pred_label)

Contact me

If you want any questions about DeepClassifier, welcome to submit issue or pull requests! And welcome to communicate with me through 2843656167@qq.com.🥳

Citation

@misc{zichao2020deepclassifier,
author = {Zichao Li},
title = {DeepClassifier: use-friendly and flexiable package of NLP based text classification models},
year = {2020},
publisher = {GitHub},
journal = {GitHub Repository},
howpublished = {\url{https://github.com/codewithzichao/DeepClassifier}},
}