Castor/char_cnn

Character-level Convolutional Network

Implementation of Char-CNN from Character-level Convolutional Networks for Text Classification (http://papers.nips.cc/paper/5782-character-level-convolutional-networks-for-text-classification.pdf)

Quick Start

To run the model on Reuters dataset, just run the following from the Castor working directory:

python -m char_cnn --dataset Reuters --gpu 1 --batch_size 128 --lr 0.001

To test the model, you can use the following command.

python -m char_cnn --trained_model kim_cnn/saves/Reuters/best_model.pt

Dataset

We experiment the model on the following datasets.

• Reuters Newswire (RCV-1)
• Arxiv Academic Paper Dataset (AAPD)

Settings

Adam is used for training.

Dataset Results

RCV-1

python -m char_cnn --dataset Reuters --gpu 1 --batch_size 128 --lr 0.001

	Accuracy	Avg. Precision	Avg. Recall	Avg. F1
Char-CNN (Dev)	0.585	0.702	0.569	0.628
Char-CNN (Test)	0.589	0.691	0.552	0.614

AAPD

python -m char_cnn --dataset AAPD --gpu 1 --batch_size 128 --lr 0.001

	Accuracy	Avg. Precision	Avg. Recall	Avg. F1
Char-CNN (Dev)	0.305	0.681	0.537	0.600
Char-CNN (Test)	0.294	0.681	0.526	0.593

TODO

• Support ONNX export. Currently throws a ONNX export failed (Couldn't export Python operator forward_flattened_wrapper) exception.
• Parameters tuning