archita091234/fine-tuned-translation

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English-to-Telugu Translation Model

Overview

This project is a deep learning-based English-to-Telugu translation model trained on a custom dataset. It uses Hugging Face Transformers for NLP and was developed in Google Colab. The model can be used for translating sentences with improved contextual accuracy.

Features

✅ Translates English text to Telugu
✅ Trained on a custom bilingual dataset
✅ Uses Transformer-based model ✅ Implemented and trained in Google Colab
✅ Can be fine-tuned for better accuracy

Tech Stack

• Programming Language: Python
• Framework: Hugging Face Transformers
• Model: mBART (Fine-tuned)
• Libraries:
  • transformers (Hugging Face)
  • torch (PyTorch)
  • sentencepiece (Tokenization)
• Platform: Google Colab

Dataset

• Used a custom English-Telugu parallel corpus
• Preprocessed using:
  • Tokenization (SentencePiece / WordPiece)
  • Lowercasing & Cleaning
  • Removing noisy data

Model Training

Training was done in Google Colab using a GPU. Here’s a snippet of the fine-tuning process:

from transformers import MarianMTModel, MarianTokenizer, Trainer, TrainingArguments

Load pre-trained model & tokenizer

model_name = "aryaumesh/english-to-telugu" # Base model tokenizer = MarianTokenizer.from_pretrained(model_name) model = MarianMTModel.from_pretrained(model_name)

Preprocess dataset (example)

def encode_data(texts): return tokenizer(texts, padding=True, truncation=True, return_tensors="pt")

Training arguments

training_args = TrainingArguments( output_dir="./results", per_device_train_batch_size=8, num_train_epochs=3, save_steps=1000, save_total_limit=2, )

trainer = Trainer( model=model, args=training_args, train_dataset=custom_dataset, )

trainer.train()

Run the Model

def translate(text): inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True) translated = model.generate(**inputs) return tokenizer.decode(translated[0], skip_special_tokens=True)

english_text = "Good morning, how are you?" telugu_translation = translate(english_text) print("Translated Text:", telugu_translation)

Future Improvements

🔹 Train on a larger dataset for better accuracy
🔹 Optimize inference speed for real-time use
🔹 Deploy as a cloud-based API (AWS/GCP)

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