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README.md

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+# Transformer Model for Language Translation
+
+## Overview
+This project implements a Transformer model for language translation between English and Italian. Built from scratch, it aims to provide a deeper understanding of the Transformer architecture, which has become a cornerstone in natural language processing tasks. The project explores key elements of the architecture, such as the attention mechanism, and demonstrates hands-on experience with data preprocessing, model training, and evaluation.
+
+## Learning Objectives
+- Understand and implement the Transformer model architecture.
+- Explore the attention mechanism and its application in language translation.
+- Gain practical experience with data preprocessing, model training, and evaluation in NLP.
+
+## Model Card on Hugging Face
+You can find and use the pre-trained model on Hugging Face here:
+[Model on Hugging Face](https://huggingface.co/amc-madalin/amc-en-it/tree/main)
+
+```python
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+# Load model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("your-huggingface-model-url")
+model = AutoModelForSeq2SeqLM.from_pretrained("your-huggingface-model-url")
+
+# Translation Example
+text = "Hello, how are you?"
+inputs = tokenizer(text, return_tensors="pt")
+outputs = model.generate(**inputs)
+translated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(translated_text)
+```
+
+## Project Structure
+
+- **Attention Visualization** (`attention_visual.ipynb`): A notebook for visualizing attention maps to understand how the model focuses on different sentence parts during translation.
+- **Configuration Settings** (`config.py`): Includes hyperparameters and other modifiable settings.
+- **Dataset Processing** (`dataset.py`): Handles loading and preprocessing of English and Italian datasets.
+- **Model Architecture** (`model.py`): Defines the Transformer model architecture.
+- **Project Documentation** (`README.md`): This file, which provides a complete overview of the project.
+- **Experiment Logs** (`runs/`): Logs and outputs from model training sessions.
+- **Tokenizers** (`tokenizer_en.json`, `tokenizer_it.json`): Tokenizers for English and Italian text preprocessing.
+- **Training Script** (`train.py`): The script that encapsulates the training process.
+- **Saved Model Weights** (`weights/`): Stores the trained model weights for future use.
+
+## Installation
+
+To set up and run the project locally, follow these steps:
+
+1. **Clone the Repository:**
+    ```bash
+    git clone https://github.com/amc-madalin/transformer-for-language-translation.git
+    ```
+
+2. **Create a Python Environment:**  
+    Create a Conda environment:
+    ```bash
+    conda create --name transformer python=3.x
+    ```
+    Replace `3.x` with your preferred Python version.
+
+3. **Activate the Environment:**
+    ```bash
+    conda activate transformer
+    ```
+
+4. **Install Dependencies:**
+    Install required packages from `requirements.txt`:
+    ```bash
+    pip install -r requirements.txt
+    ```
+
+5. **Prepare Data:**
+    The dataset will be automatically downloaded. Modify the source (`lang_src`) and target (`lang_tgt`) languages in `config.py`, if necessary. The default is set to English (`en`) and Italian (`it`):
+    ```json
+    "lang_src": "en",
+    "lang_tgt": "it",
+    ```
+
+6. **Train the Model:**
+    Start the training process with:
+    ```bash
+    python train.py
+    ```
+
+7. **Use the Model:**
+    The trained model weights will be saved in the `weights/` directory. Use these weights for inference, evaluation, or further applications.
+
+## Using the Model with Hugging Face
+Once trained, the model can be uploaded to Hugging Face for easy access and use.
+
+### Uploading the Model to Hugging Face
+Use the following steps to upload your trained model to Hugging Face:
+```bash
+huggingface-cli login
+transformers-cli upload ./weights/ --organization your-organization
+```
+
+### Loading the Model from Hugging Face for Inference
+You can easily load the model for translation tasks directly from Hugging Face:
+```python
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+tokenizer = AutoTokenizer.from_pretrained("your-huggingface-model-url")
+model = AutoModelForSeq2SeqLM.from_pretrained("your-huggingface-model-url")
+
+# Translate text
+text = "How are you?"
+inputs = tokenizer(text, return_tensors="pt")
+outputs = model.generate(**inputs)
+translation = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(translation)
+```
+
+## Learning Resources
+- [YouTube - Coding a Transformer from Scratch on PyTorch](https://youtube.com/your-video-link)  
+  A detailed walkthrough of coding a Transformer model from scratch using PyTorch, including training and inference.
+
+## Acknowledgements
+Special thanks to **Umar Jamil** for his guidance and contributions that supported the completion of this project.
+