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+# PyTorch-based NLI Training with SNLI
+
+## 📝 Overview
+
+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.
+
+## ⚙️ Dependencies
+
+Install the necessary Python libraries with:
+
+```bash
+pip install -r requirements.txt
+```
+
+The `requirements.txt` file includes:
+
+```
+torch
+torchtext
+spacy
+```
+
+## 💻 Usage
+
+Start the training process with:
+
+```bash
+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]
+```
+
+## 🏋️‍♀️ Training
+
+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.
+
+## 📚 Scripts
+
+- `model.py`: Defines the `SNLIClassifier` model and auxiliary classes.
+- `util.py`: Contains utility functions for directory creation and command-line argument parsing.
+
+## 📣 Note
+
+Ensure the `model.py` and `util.py` scripts are available in your working directory.
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