QuickText
Toolkit For Text Classification
About •
Features •
Install •
Getting Started •
Examples
[](https://lbesson.mit-license.org/)
[](https://picturate.github.io/quickTextCassifier/)
[](https://codecov.io/gh/GDGVIT/quicktext)
# About
Quicktext is a framework for developing LSTM and CNN based text classification models.
# Features
- It is __easy__ to learn and use quicktext
- The classifiers can be added to __sPacy__ pipeline
- It's built using __PyTorch__, hence has inbuilt __quantization__ and __onnx__ support
# Installation
Install from source
```
pip install -q git+https://github.com/GDGVIT/quicktext.git
```
# Getting Started
```python
from quicktext import TextClassifier
from quicktext import Trainer
from quicktext.datasets import get_imdb
imdb = get_imdb()
classifier = TextClassifier(num_class=2, arch='bilstm')
trainer = Trainer(classifier)
trainer.fit(imdb.train, imdb.val, epochs=10, batch_size=64, gpus=1)
```
# Supported Models
- Bidirectional LSTM
- CNN 2D filters
- Fasttext
- RCNN
- Seq2Seq Attention
# Examples
- [Spam or ham, spam classification]()
# Contributors
|
Ramaneswaran
|
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