This is the implementation of Convolutional Neural Networks for Sentence Classification (Y.Kim, EMNLP 2014) on Pytorch.
Below are results corresponding to all 4 models proposed in the paper for each dataset. Experiments have been done with a learning rate = 0.1 up to 300 epochs and all details are tuned to follow the settings defined in the paper.
(Measure: Accuracy)
| Model | Dataset | MR | TREC |
|---|---|---|---|
| Rand | Results | 70.0 | 87.8 |
| Baseline | 76.1 | 91.2 | |
| Static | Results | 82.4 | 93.8 |
| Baseline | 81.0 | 92.8 | |
| Non-static | Results | 81.4 | 93.6 |
| Baseline | 81.5 | 93.6 | |
| Multichannel | Results | 81.6 | 92.6 |
| Baseline | 81.1 | 92.2 |
- model.py: CNN sentnece classifier implementation proposed by Y. Kim.
- run.py: train and test a model with configs.
- OS: Ubuntu 16.04 LTS (64bit)
- Language: Python 3.6.2.
- GPU: GTX 1080
This model is based on pre-trained Word2vec(GoogleNews-vectors-negative300.bin) by T.Mikolov et al. You should download this file and place it in the root folder.
Also you should follow library requirements specified in the requirements.txt.
numpy==1.12.1
gensim==2.3.0
scikit_learn==0.19.0
python run.py
usage: run.py [-h] [--mode MODE] [--model MODEL] [--dataset DATASET]
[--save_model SAVE_MODEL] [--early_stopping EARLY_STOPPING]
[--epoch EPOCH] [--learning_rate LEARNING_RATE]
-----[CNN-classifier]-----
optional arguments:
-h, --help show this help message and exit
--mode MODE train: train (with test) a model / test: test saved models
--model MODEL available models: rand, static, non-static, multichannel
--dataset DATASET available datasets: MR, TREC
--save_model SAVE_MODEL whether saving model or not (T/F)
--early_stopping EARLY_STOPPING whether to apply early stopping(T/F)
--epoch EPOCH number of max epoch
--learning_rate LEARNING_RATE learning rate