Overview
This repository contains a machine translation model built from scratch using the Transformer architecture. The project focuses on translating text from English to Italian. By implementing the Transformer model from scratch, this project demonstrates key concepts like self-attention, multi-head attention, and positional encoding.The model has been designed to handle sequence-to-sequence translation tasks efficiently by leveraging transformers without relying on pre-trained models.
Features
*Transformer-based sequence-to-sequence architecture.
*Scalable to various language pairs (e.g., English to French, German to English).
*Full implementation of the Transformer model, including:
*Multi-head self-attention
*Positional encoding
*Encoder-decoder structure
*Layer normalization and residual connections
*Support for tokenization, padding, and sentence numericalization.
*Efficient use of GPUs (e.g., tested on Tesla T4 with CUDA for fast training).
*Evaluation using BLEU scores and translation quality metrics.
Requirements
*Python 3.8+
*PyTorch 2.0+
*Torchtext
*Torchmetrics
*Numpy
*tqdm
*Jupyter Notebook (for running experiments)
Improving Model Performance
Use Pre-trained Embeddings
*Integrate pre-trained word embeddings such as FastText, GloVe, or BERT embeddings into the encoder to improve initial translations, especially for low-resource languages.
Use Better Hardware
*GPU/TPU Utilization: Take advantage of GPUs or TPUs to train deeper models with larger datasets. This will also speed up training time and also enable to train the model for more epochs.
Tune Hyperparameters
*Learning Rate: Experiment with different learning rates to find the optimal value. A learning rate scheduler can also be employed to adjust the learning rate during training.
*Batch Size: Increasing the batch size can help with faster convergence, especially when training on powerful hardware like TPUs.
*Optimizer: Use advanced optimizers like AdamW or LAMB for faster convergence and better performance.