This repository contains Python scripts to train a Natural Language Inference (NLI) model, specifically the SNLIClassifier, using the Stanford Natural Language Inference (SNLI) corpus. The trained model predicts textual entailment, identifying if a statement is entailed, contradicted, or neither by another statement.

Install the necessary Python libraries with:

pip install -r requirements.txt

The requirements.txt file includes:

torch
torchtext
spacy

Start the training process with:

python train.py --lower --word-vectors [PATH_TO_WORD_VECTORS] --vector-cache [PATH_TO_VECTOR_CACHE] --epochs [NUMBER_OF_EPOCHS] --batch-size [BATCH_SIZE] --save-path [PATH_TO_SAVE_MODEL] --gpu [GPU_NUMBER]

The script trains the model on mini-batches of data across a specified number of epochs. It saves the best-performing model on the validation set as a .pt file in the specified directory.

• model.py: Defines the SNLIClassifier model and auxiliary classes.
• util.py: Contains utility functions for directory creation and command-line argument parsing.

Ensure the model.py and util.py scripts are available in your working directory.