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https://github.com/shawn1993/cnn-text-classification-pytorch
CNNs for Sentence Classification in PyTorch
https://github.com/shawn1993/cnn-text-classification-pytorch
cnn-model pytorch
Last synced: about 12 hours ago
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CNNs for Sentence Classification in PyTorch
- Host: GitHub
- URL: https://github.com/shawn1993/cnn-text-classification-pytorch
- Owner: Shawn1993
- License: apache-2.0
- Created: 2017-02-10T16:49:39.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2023-02-24T14:33:22.000Z (over 1 year ago)
- Last Synced: 2024-10-14T20:20:49.795Z (29 days ago)
- Topics: cnn-model, pytorch
- Language: Python
- Size: 12.8 MB
- Stars: 1,018
- Watchers: 15
- Forks: 278
- Open Issues: 8
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
## Introduction
This is the implementation of Kim's [Convolutional Neural Networks for Sentence Classification](https://arxiv.org/abs/1408.5882) paper in PyTorch.
1. Kim's implementation of the model in Theano:
[https://github.com/yoonkim/CNN_sentence](https://github.com/yoonkim/CNN_sentence)
2. Denny Britz has an implementation in Tensorflow:
[https://github.com/dennybritz/cnn-text-classification-tf](https://github.com/dennybritz/cnn-text-classification-tf)
3. Alexander Rakhlin's implementation in Keras;
[https://github.com/alexander-rakhlin/CNN-for-Sentence-Classification-in-Keras](https://github.com/alexander-rakhlin/CNN-for-Sentence-Classification-in-Keras)
## Requirement
* python 3
* pytorch > 0.1
* torchtext > 0.1
* numpy
## Result
I just tried two dataset, MR and SST.
|Dataset|Class Size|Best Result|Kim's Paper Result|
|---|---|---|---|
|MR|2|77.5%(CNN-rand-static)|76.1%(CNN-rand-nostatic)|
|SST|5|37.2%(CNN-rand-static)|45.0%(CNN-rand-nostatic)|
I haven't adjusted the hyper-parameters for SST seriously.
## Usage
```
./main.py -h
```
or
```
python3 main.py -h
```
You will get:
```
CNN text classificer
optional arguments:
-h, --help show this help message and exit
-batch-size N batch size for training [default: 50]
-lr LR initial learning rate [default: 0.01]
-epochs N number of epochs for train [default: 10]
-dropout the probability for dropout [default: 0.5]
-max_norm MAX_NORM l2 constraint of parameters
-cpu disable the gpu
-device DEVICE device to use for iterate data
-embed-dim EMBED_DIM
-static fix the embedding
-kernel-sizes KERNEL_SIZES
Comma-separated kernel size to use for convolution
-kernel-num KERNEL_NUM
number of each kind of kernel
-class-num CLASS_NUM number of class
-shuffle shuffle the data every epoch
-num-workers NUM_WORKERS
how many subprocesses to use for data loading
[default: 0]
-log-interval LOG_INTERVAL
how many batches to wait before logging training
status
-test-interval TEST_INTERVAL
how many epochs to wait before testing
-save-interval SAVE_INTERVAL
how many epochs to wait before saving
-predict PREDICT predict the sentence given
-snapshot SNAPSHOT filename of model snapshot [default: None]
-save-dir SAVE_DIR where to save the checkpoint
```
## Train
```
./main.py
```
You will get:
```
Batch[100] - loss: 0.655424 acc: 59.3750%
Evaluation - loss: 0.672396 acc: 57.6923%(615/1066)
```
## Test
If you has construct you test set, you make testing like:
```
/main.py -test -snapshot="./snapshot/2017-02-11_15-50-53/snapshot_steps1500.pt
```
The snapshot option means where your model load from. If you don't assign it, the model will start from scratch.
## Predict
* **Example1**
```
./main.py -predict="Hello my dear , I love you so much ." \
-snapshot="./snapshot/2017-02-11_15-50-53/snapshot_steps1500.pt"
```
You will get:
```
Loading model from [./snapshot/2017-02-11_15-50-53/snapshot_steps1500.pt]...
[Text] Hello my dear , I love you so much .
[Label] positive
```
* **Example2**
```
./main.py -predict="You just make me so sad and I have to leave you ."\
-snapshot="./snapshot/2017-02-11_15-50-53/snapshot_steps1500.pt"
```
You will get:
```
Loading model from [./snapshot/2017-02-11_15-50-53/snapshot_steps1500.pt]...
[Text] You just make me so sad and I have to leave you .
[Label] negative
```
Your text must be separated by space, even punctuation.And, your text should longer then the max kernel size.
## Reference
* [Convolutional Neural Networks for Sentence Classification](https://arxiv.org/abs/1408.5882)