gguf license:apache-2.0 endpoints_compatible region:us
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license: apache-2.0
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# Improved Code-Mixed Sentence Translation Using Decoder-Only Transformers

## Overview

This project addresses the limitations of traditional Neural Machine Translation (NMT) models in translating code-mixed sentences by utilizing a decoder-only transformer model. Inspired by the training methodologies of models like GPT and Llama, this approach leverages self-supervised learning to understand the context of languages more deeply. After learning the context, the model is fine-tuned on a smaller translation dataset, making it effective for translating both regular and code-mixed sentences.

## Benefits

1. **Fraction of Translation Dataset**: The model requires only a small amount of translation data for fine-tuning, which reduces the data preparation overhead.
2. **Rich and Meaningful Translation**: By understanding the underlying context of languages, the model provides more accurate and meaningful translations for both regular and code-mixed sentences.
3. **Multilingual Capability**: A single model can potentially translate multiple languages, making it a versatile solution for diverse translation needs.

## Approach

1. **Context Learning**: Train a decoder-only transformer model on a large corpus of text using self-supervised learning. This stage allows the model to grasp the contextual nuances of different languages.
2. **Fine-Tuning**: Fine-tune the pre-trained model on a smaller dataset specifically for translation tasks. This step adapts the model to effectively handle translation while retaining its contextual understanding.

## Example

Here is a comparison between the traditional Google Translate and the proposed approach:

- **Text**: “Sun ka diameter kya hoga?”

- **Google Translate**: “what will happen to sun's demetre”

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![image/png](https://cdn-uploads.huggingface.co/production/uploads/64cf8e4fe7b70e91a24ed55d/bmSMnK9jpVNneOG7DPnYc.png)

- **Proposed Approach**: “What is the diameter of the Sun?”

The proposed method outperforms traditional translation models by providing a more accurate translation that respects the context and meaning of the original sentence.

## Usage

1. **Pre-training**: Train the decoder-only transformer model on a large text corpus.
2. **Fine-tuning**: Fine-tune the model on a smaller dataset of translated sentences.
3. **Translation**: Use the fine-tuned model to translate both regular and code-mixed sentences.

## Future Work

- **Evaluation**: Conduct thorough evaluations and comparisons with other state-of-the-art translation models.
- **Expansion**: Explore additional languages and code-mixed scenarios to enhance the model's versatility.

## License

This project is licensed under the [MIT License](LICENSE).

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