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.
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”
The proposed method outperforms traditional translation models by providing a more accurate translation that respects the context and meaning of the original sentence.
This project is licensed under the
MIT License.
