This project focuses on building a Neural Machine Translation (NMT) system to translate English sentences to Hindi. NMT has revolutionized the field of language translation by leveraging deep learning techniques to produce more accurate and natural-sounding translations.

• Encoder-Decoder Architecture: The NMT system employs an encoder-decoder architecture, where the encoder encodes the input English sentence into a fixed-size context vector, and the decoder generates the corresponding Hindi translation from the context vector.

• Attention Mechanism: To handle longer sentences and capture relevant information effectively, an attention mechanism is integrated. This allows the model to focus on different parts of the input sentence while generating the output.

• Data Preprocessing: The project includes data preprocessing steps to clean and normalize input sentences, ensuring better alignment and accuracy in translation.

• Training and Evaluation: The model is trained on a parallel corpus of English-Hindi sentence pairs. During training, the model learns to minimize the translation loss. The evaluation process demonstrates the model's translation quality with selected input sentences.

• Visualization of Attention: The project offers a visualization of attention weights, showing how the model attends to different parts of the input during translation.

1. Data Preparation: Prepare your parallel corpus of English-Hindi sentence pairs. Ensure that your data is properly formatted and cleaned.

2. Model Configuration: Set up the encoder and attention-based decoder architecture in the code. Define the hyperparameters, such as hidden size, learning rate, and dropout rate.

3. Training: Train the model using the provided training functions. Adjust the number of training iterations, print intervals, and other parameters as needed.

4. Evaluation and Visualization: Evaluate the model's translation quality using the evaluateAndShowAttention function. Provide your English input sentences and observe both the translated output and attention visualization.

• Python 3.x
• PyTorch
• Matplotlib

Contributions to this project are welcome! Whether it's improving the model's performance, enhancing the visualization, or extending the features, your contributions can make a significant impact.

This project is licensed under the MIT License.

Refrences

• Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation_
• Sequence to Sequence Learning with Neural Networks_
• Neural Machine Translation by Jointly Learning to Align and Translate_
• A Neural Conversational Model_

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If you have any questions, suggestions, or just want to say hello, you can reach out to us at Tushar Aggarwal. We would love to hear from you!