Pattern-Exploiting Natural Language Inference.

(My graduation thesis)

To install all frameworks and libraries used in this project:

pip install -r requirements.txt

This project uses two Natural Language Inference Datasets: e-SNLI, which is an extension of the SNLI dataset, and MNLI. These two datasets should be stored in directories similar to this:

PromptCLED
│   README.md
│   requirements.txt    
│   test.py
│   ...
└───datasets
│   └───e-SNLI
│   │   │esnli_dev.csv
│   │   │esnli_test.csv
│   │   │esnli_train_1.csv
│   │   │esnli_train_2.csv
│   │   
│   └───multinli_1.0
│   │   │multinli_1.0_dev_matched.jsonl
│   │   │multinli_1.0_dev_matched.txt
│   │   │multinli_1.0_dev_mismatched.jsonl
│   │   │multinli_1.0_dev_mismatched.txt   
│   │   │multinli_1.0_train.jsonl   
│   │   │multinli_1.0_train.txt  
│   
└───...
    │   ...
    │   ...
...

These two datasets are publicly available and can be downloaded from the provided links. Please download and place them inside the folders following the structure above.

To train a model, run train.py. For example:

python train.py --config=./configs/default_ed.json --device=cuda

For more information about the arguments of this python script:

python train.py --help

To evaluate a trained model, run test.py. For example:

python test.py --config=./configs/default_ed.json --device=cuda --best_ckpt

For more information about the arguments of this python script:

python test.py --help

To further fine-tune a trained model using RL, run train_rl.py. For example:

python train.py --config=./configs/default_ed.json --device=cuda

The supervised fine-tuned checkpoint of the corresponding config must exist before RL training. For more information about the arguments of this python script:

python train.py --help