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CNNs for Sentence Classification in PyTorch
| Date | Stars |
|---|---|
| 2026-07-24 | 1044 |
| 2026-07-25 | 1044 |
| 2026-07-28 | 1044 |
| 2026-07-30 | 1044 |
| 2026-08-06 | 1044 |
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## 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 >= 3.8 * pytorch >= 2.0 No longer depends on `torchtext` or `numpy`. Data loading uses standard PyTorch `Dataset` and `DataLoader`. ## Result Two datasets were tested: MR and SST. |Dataset|Class Size|Best Result|Kim's Paper Result| |---|---|---|---| |MR|2|76.5%(CNN-rand)|76.1%(CNN-rand)| |SST|5|45.6%(CNN-rand)|45.0%(CNN-rand)| Both results are consistent with Kim's paper. The SST result uses phrase-level training data, Adadelta optimizer, and embedding dimension 300, matching the original paper's setup: ``` python3 main.py -dataset SST -embed-dim 300 -batch-size 50 -optimizer adadelta -lr 1.0 -epochs 15 -early-stop 10000 -test-interval 500 ``` ## Usage ``` python3 main.py -h ``` You will get: ``` CNN text classificer optional arguments: -h, --help show this help message and exit -lr LR initial learning rate [default: 0.001] -epochs N number of epochs for train [default: 256] -batch-size N batch size for training [default: 64] -log-interval N how many steps to wait before logging training status [default: 1] -test-interval N how many steps to wait before testing [default: 100] -save-interval N how many steps to wait before saving [default: 500] -save-dir DIR where to save the snapshot -early-stop N iteration numbers to stop without performance increasing [default: 1000] -save-best BOOL whether to save when get best performance [default: True] -dataset DATASET dataset to use: MR or SST [default: MR] -no-phrases SST: use sentence-level only (no phrase data) -shuffle shuffle the data every epoch -dropout DROPOUT the probability for dropout [default: 0.5] -max-norm FLOAT l2 constraint of parameters [default: 3.0] -embed-dim N number of embedding dimension [default: 128] -kernel-num N number of each kind of kernel [default: 100] -kernel-sizes STR comma-separated kernel size to use for convolution [default: 3,4,5] -static fix the embedding -optimizer OPTIMIZER optimizer: adam or adadelta [default: adam] -device DEVICE device to use for iterate data, -1 mean cpu [default: -1] -no-cuda disable the gpu -snapshot FILE filename of model snapshot [default: None] -predict TEXT predict the sentence given -test train or test ``` ## Train ``` python3 main.py ``` You will get: ``` Batch[100] - loss: 0.655424 acc: 59.3750% Evaluation - loss: 0.672396 acc: 57.6923%(615/1066) ``` ## Test ``` python3 main.py -test -snapshot="./snapshot/2017-02-11_15-50-53/best_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** ``` python3 main.py -predict="Hello my dear , I love you so much ." \ -snapshot="./snapshot/2017-02-11_15-50-53/best_steps1500.pt" ``` You will get: ``` Loading model from ./snapshot/2017-02-11_15-50-53/best_steps1500.pt... [Text] Hello my dear , I love you so much . [Label] positive ``` * **Example2** ``` python3 main.py -predict="You just make me so sad and I have to leave you ."\ -snapshot="./snapshot/2017-02-11_15-50-53/best_steps15
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Megvii Research · China
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:28bb982a62d0371c, topic:pytorch
matched fp:28bb982a62d0371c, name:text classification, readme:text classification