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CodeMix Translation Project

This project explores machine translation between English and Hinglish (Hindi written in the Roman script). The primary objective is to build, fine-tune, and compare models for translating English text to Hinglish, leveraging both pre-trained models and custom architectures.

Directory Structure

  .
  ├── README.md
  ├── Report.pdf
  ├── final-ppt.ppt
  ├── codes/
  │   ├── bart-base.py
  │   ├── custom-transformer.py
  │   ├── mbart-large-50-many-to-many-mmt.py
  │   ├── opus-mt-en-ROMANCE.py
  │   └── t5-small.py
  ├── notebooks/
  │   ├── bart-base.ipynb
  │   ├── mbart-large-50-many-to-many-mmt.ipynb
  │   ├── opus-mt-en-ROMANCE.ipynb
  │   └── t5-small.ipynb

Descriptions

  • README.md: Main documentation for the project.
  • codes/: Contains Python scripts for model implementations.
  • notebooks/: Jupyter notebooks for training and evaluation of respective models.

Models Included

1. T5 Small

  • Dataset: findnitai/english-to-hinglish
  • Training Setup:
    • The model was trained on 189,102 sentences using a T5-small architecture.
    • Training took 5 hours on 2 Tesla T4 GPUs.
  • Evaluation:
    • BLEU Score on Test Data: 26.98
  • Description:
    • This model demonstrates fine-tuning of a pre-trained T5-small architecture on the English-to-Hinglish dataset. The dataset contains English sentences paired with their Hinglish translations in the Roman script.

2. Custom Transformer Encoder-Decoder Model

  • Dataset: findnitai/english-to-hinglish
  • Training Setup:
    • Utilized a custom Transformer-based Encoder-Decoder architecture for English-to-Hinglish translation (Devanagari script).
    • Ran for 10 epochs on 2 Tesla T4 GPUs.
  • Evaluation:
    • BLEU Score on Test Data: 20.58
  • Description:
    • This model was specifically designed to work with Hinglish translations written in the Devanagari script, exploring the challenges of alternate script systems.

3. BART-Base

  • Dataset: findnitai/english-to-hinglish
  • Training Setup:
    • Fine-tuned on the full English-to-Hinglish dataset using lora .
    • Training took 5 hours on 2 Tesla T4 GPUs.
  • Evaluation:
    • BLEU Score on Test Data: 33.06
  • Description:
    • This model fine-tunes the BART-base architecture for machine translation tasks, leveraging its seq2seq capabilities.

4. mBART-Large-50

  • Dataset: findnitai/english-to-hinglish
  • Training Setup:
    • Fine-tuned on the full dataset for 3 hours using 2 Tesla T4 GPUs.
  • Evaluation:
    • BLEU Score on Test Data: 43.23
  • Description:
    • The mBART-large-50 model leverages multilingual pre-training and fine-tuning, showing improved performance due to its multilingual capabilities.

5. Helsinki-NLP/opus-mt-en-ROMANCE

  • Dataset: findnitai/english-to-hinglish
  • Training Setup:
    • Fine-tuned on the full dataset for 3.5 hours using a 2 Tesla T4 GPUs using lora.
  • Evaluation:
    • BLEU Score on Test Data: 32.22
  • Description:
    • This model fine-tunes the Helsinki-NLP model specialized for translating English to Romance languages. It serves as a baseline comparison for models pre-trained on related language pairs.

Dataset Information

Roman Script Dataset

Results Summary

Model Dataset Epochs BLEU Score Training Time GPUs
T5 Small findnitai/english-to-hinglish 5 26.98 5 hours 2x Tesla T4
T5 Small (30% Dataset) findnitai/english-to-hinglish 3 14.16 45 minutes 2x Tesla T4
Custom Transformer English-to-Hinglish (Devanagari Script) 10 7.98 2 hours P100
Custom Transformer findnitai/english-to-hinglish 10 20.58 3 hours P100
BART-Base findnitai/english-to-hinglish 4 33.06 4 hours 2x Tesla T4
mBART-Large-50 findnitai/english-to-hinglish 3 43.23 3.36 hours 2x Tesla T4
Helsinki-NLP/opus-mt-en-ROMANCE findnitai/english-to-hinglish 5 32.22 4 hours P100

Usage

To explore or use the models:

  1. T5 Models:
    • Fine-tuning and evaluation are detailed in the T5 Small notebooks.
  2. Custom Transformer:
    • Contains implementation details for the custom Transformer architecture.
  3. BART/mBART/Helsinki-NLP:
    • Includes pre-trained multilingual setups and fine-tuning instructions.

Run the notebooks on a GPU environment for better performance and to reproduce the results.

Resources

For further insights, refer to the detailed project report included in the Directory.