m25csa011-transformer-english-hindi – AI Model by MSG1999 | AlphaNeural AI

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MSG1999/
m25csa011-transformer-english-hindi

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🚀 English → Hindi Transformer

Python

PyTorch

Task

BLEU

Optimization

📌 Overview

This model implements a Transformer-based Neural Machine Translation (NMT) system for English → Hindi translation using PyTorch.

Optimized using Ray Tune + Optuna + ASHA, achieving high BLEU score with reduced training time.

🏗️ Model Architecture

Encoder–Decoder Transformer
6 Encoder + 6 Decoder layers
Multi-head attention
Feed-forward network
Positional encoding
Residual connections + LayerNorm

Configuration

d_model: 512
num_heads: 4
num_layers: 6
d_ff: 4096
dropout: 0.054

⚙️ Training Details

Dataset: ~13,186 English-Hindi sentence pairs
Optimizer: AdamW
Loss: CrossEntropy (ignore padding)
Scheduler: CosineAnnealingLR
Device: GPU

📊 Results

Metric	Baseline	Tuned
Epochs	100	30
Time	129.42 min	79.31 min
Loss	0.0972	0.0959
BLEU	68.02	90.38

⚙️ Best Hyperparameters

LR: 0.0001009
Batch size: 64
Heads: 4
d_ff: 4096
Dropout: 0.054
Weight decay: 0.000261

🧪 Sample Outputs

EN: I love you HI: मैं तुमसे प्यार करता हूँ
EN: What is your name? HI: आपका नाम क्या है?
EN: How are you? HI: आप कैसे हैं?

📂 Files

M25CSA011_ass_4_best_model.pth
en_vocab.pkl
hi_vocab.pkl
best_config.json

👤 Author

Mahek Shankesh Gadiya M.Tech AI – IIT Jodhpur

📚 Assignment

Transformer Optimization using Ray Tune + Optuna