code-terminator / text_nn

Text classification models. Used a submodule for other projects.

text_nn

Text Classification models. Used as a submodule for other projects. Supports extractive rationale extraction like in Tao Lei's Rationalizing neural prediction. These departs from Tao's original frame work in the following way:

• I implement Generator training using the Gumbel Softmax instead of using REINFORCE
• I only implement the indepdent selector.

Usage:

Example run:

CUDA_VISIBLE_DEVICES=2 python -u scripts/main.py  --batch_size 64 --cuda --dataset full_beer --embedding 
glove --dropout 0.05 --weight_decay 5e-06 --num_layers 1 --model_form cnn --hidden_dim 100 --epochs 50 --init_lr 0.0001 --num_workers
 0 --objective cross_entropy --patience 5 --save_dir snapshot --train --test --results_path logs/adhoc_0.results  --gumbel_decay 1e-5 --get_rationales
 --aspect aroma --selection_lambda .005 --continuity_lambda .01

Use --get_rationales to enable extractive rationales.

To run grid search, see docs/dispatcher

Base Models Supported:

• TextCNN (Yoon 2014)

Extending:

How to add a new dataset:

• Add a pytorch Dataset object to rationale_net/datasets and register it to the dataset factory

How to add a new model base?

• Supported in research version of this repo, but it's involved. If there is interest, please contact me.