https://github.com/codewithzichao/deepclassifier
DeepClassifier is aimed at building general text classification model library.It's easy and user-friendly to build any text classification task.
https://github.com/codewithzichao/deepclassifier
deep-learning deepclassifier nlp pytorch text-classification torch
Last synced: 8 months ago
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DeepClassifier is aimed at building general text classification model library.It's easy and user-friendly to build any text classification task.
- Host: GitHub
- URL: https://github.com/codewithzichao/deepclassifier
- Owner: codewithzichao
- License: apache-2.0
- Created: 2020-12-18T09:38:29.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2021-02-15T04:23:42.000Z (over 5 years ago)
- Last Synced: 2025-03-26T20:51:33.385Z (over 1 year ago)
- Topics: deep-learning, deepclassifier, nlp, pytorch, text-classification, torch
- Language: Python
- Homepage: https://deepclassifier.readthedocs.io
- Size: 5.74 MB
- Stars: 31
- Watchers: 2
- Forks: 7
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# **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**](https://deepclassifier.readthedocs.io/en/latest/). 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](https://www.aclweb.org/anthology/D14-1181.pdf) ,2014 EMNLP
2. **RCNN:** [Recurrent Convolutional Neural Networks for Text Classification](https://www.deeplearningitalia.com/wp-content/uploads/2018/03/Recurrent-Convolutional-Neural-Networks-for-Text-Classification.pdf),2015,IJCAI
3. **DPCNN:** [Deep Pyramid Convolutional Neural Networks for Text Categorization](https://ai.tencent.com/ailab/media/publications/ACL3-Brady.pdf) ,2017,ACL
4. **HAN:** [Hierarchical Attention Networks for Document Classification](https://www.aclweb.org/anthology/N16-1174.pdf), 2016,ACL
5. **BERT:** [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/pdf/1810.04805.pdf),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]**](https://github.com/codewithzichao/DeepClassifier/blob/master/examples) to display the complete code.
you can define model like that(take BertTextCNN model as example):👇
```python
from deepclassifier.models import BertTextCNN
# parameters of model
embedding_dim = 768 # if you use bert, the default is 768.
dropout_rate = 0.2
num_class = 2
bert_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:👇
```python
from deepclassifier.trainers import Trainer
model_name = "berttextcnn"
save_path = "best.ckpt"
writer = SummaryWriter("logfie/1")
max_norm = 0.25
eval_step_interval = 20
my_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)
# training
my_trainer.train()
# print the best F1 value on dev set
print(my_trainer.best_f1)
# testing
p, r, f1 = my_trainer.test()
print(p, r, f1)
# predict
pred_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
```tex
@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}},
}
```