Skip to content
master
Switch branches/tags
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
 
 
 
 
 
 
 
 
 
 
 
 

self-explaining-NLP

Code, models and Datasets for《Self-Explaining Structures Improve NLP Models》.

installation

pip install -r requirements.txt

Prepare Datasets and Models

  • Download the SST-5 dataset, the official corpus can be found HERE. We provide processed raw text which you can download HERE. Save the processed raw text dataset at [SST_PATA_PATH].
  • Download the SNLI dataset, the official corpus can be found HERE. Save the SNLI dataset at [SNLI_PATA_PATH].
  • Download the vanilla RoBERTa-base model released by HuggingFace. Save the model at [ROBERTA_BASE_PATH], it can be found HERE
  • Download the model checkpoints we trained for different tasks. You can use our checkpoint for evaluation. the checkpoints can be download HERE

Reproduce paper results step by step

In this paper, we utilize self-explaining structures in different NLP tasks. This repo contains all train and evaluate codes, but here, we only provide commands for SST-5 task as an example. For other tasks, you can reproduce the results simply by modifying the commands.

1.Train the self-explaining model

SST-5 is a task with five classes, so we should modify the Roberta-base config file. Open [ROBERTA_BASE_PATH]\config.json and set num_labels=5. Then run the following commands.

cd explain
python trainer.py \
--bert_path [ROBERTA_BASE_PATH] \
--data_dir [SST_PATA_PATH] \
--task sst5 \
--save_path [SELF_EXPLAINING_MODEL_CHECKPOINTS] \
--gpus=0,1,2,3  \
--precision 16 \
--lr=2e-5 \
--batch_size=10 \
--lamb=1.0 \
--workers=4 \
--max_epoch=20

After training, the checkpoints and training log will be saved at [SELF_EXPLAINING_MODEL_CHECKPOINTS].

2.Evaluate the self-explaining model

Run the following evaluation command to get the performance on test dataset. You can use the checkpoint you trained or just download our checkpoint to evaluate test dataset. After evaluation, you will get two output file at [SPAN_SAVE_PATH]: output.txt and test.txt. output.txt records visual extract spans and prediction results. text.txt only records top-ranked span as span-base test data for next stage.

cd explain
python trainer.py \
--bert_path [ROBERTA_BASE_PATH] \
--data_dir [SST_PATA_PATH] \
--task sst5 \
--checkpoint_path [SELF_EXPLAINING_MODEL_CHECKPOINTS]/***.ckpt \
--save_path [SPAN_SAVE_PATH] \
--gpus=0, \
--mode eval

3.Check the extracted span

In previous stage, we got span-based test data. You can use the same method to get span-based train data.
To check the extracted span, we set four experiments which are full-full mode, full-span mode, span-full mode and span-span mode. For example, full-span mode means we use origin SST-5 train data as train data, and use span-based test data as test data.
You should save the origin SST-5 train data and span-base test data at [FULL_SPAN_PATH]

scp [SST_PATA_PATH]/train.txt  [FULL_SPAN_PATH]
scp [SPAN_SAVE_PATH]/test/txt [FULL_SPAN_PATH]
cd check
python trainer.py \
--bert_path [ROBERTA_BASE_PATH] \
--data_dir [FULL_SPAN_PATH] \
--task sst5 \
--save_path [CHECK_MODEL_CHECKPOINTS] \
--gpus=0,1,2,3  \
--precision 16 \
--lr=2e-5 \
--batch_size=10 \
--workers=4 \
--max_epoch=20

About

No description, website, or topics provided.

Resources

License

Releases

No releases published

Packages

No packages published

Languages