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Model Description
SAWiT.AI Hackathon - 2024
The challenge is to create a dataset in one of six Indian languages—Tamil, Telugu, Malayalam, Hindi, Bengali, or Marathi—and use it to train an LLM for better natural language understanding and generation.
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🚀 Project Overview
- Objective: Translate English sentences into Colloquial Hinglish sentences.
- Dataset: Custom-built English-Hinglish sentence pairs.
📂 Dataset
The dataset consists of 1000+ unique English-Hinglish pairs. It was manually curated and formatted for training the transformer-based model.
🛠 Model Training & Fine-Tuning
- Training done on Google Colab CPU due to GPU limitations.
🔧 Challenges & Workarounds
- Compute Limitations: No GPU access, model trained on CPU.
- Dataset Quality: Custom dataset caused low BLEU scores; external datasets were tested.
- Time Constraints: Rapid iterations to optimize results within submission deadlines.
📦 Usage
- Load the fine-tuned model from Hugging Face.
- Use the tokenizer for encoding input text.
- Generate translations and decode outputs.
📌 Future Improvements
- Enhance dataset quality by incorporating larger, well-labeled Hinglish datasets.
- Fine-tune using low-rank adaptation (LoRA) for better efficiency.
- Deploy the model via an API for real-world applications.
🎯 Conclusion
This project demonstrates AI-powered Hinglish translation despite resource constraints. While BLEU scores remained low due to dataset limitations, the approach lays the groundwork for further refinements and improvements.
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
How to Get Started with the Model
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Training Details
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Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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