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Swethamani/
Translation-Models-En-G

Name: Translation-Models-En-G
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English-Galician Translation Project

Description

This repository focuses on training and evaluating machine learning models for English-to-Galician translation. It uses T5-small as the primary model and Helsinki-NLP/opus-mt-en-gl as the secondary model. The project demonstrates the fine-tuning process, evaluation using automatic and human metrics, and comparative analysis of the models. The models leverage the Hugging Face Transformers library and the Seq2Seq framework, ensuring high-quality and efficient translation capabilities.

Features

Model Training:
- Fine-tuning T5-small and Helsinki-NLP/opus-mt-en-gl on a custom dataset of 66,358 samples.
- Preprocessing includes tokenization, truncation, and mapping source-target pairs.
- Training configuration includes 3 epochs with learning rate and batch size optimizations.
Evaluation:
- Semantic Similarity: Measured using SentenceTransformer embeddings.
- Automatic Metrics: BLEU and ROUGE for translation quality.
- Human Evaluation: Scores for relevance, fluency, coherence, engagement, and creativity.
Comparison:
- Includes variability in outputs and metrics for realistic simulation.
- Side-by-side evaluation of both models to highlight trade-offs.

Model Details

Model Summary

This project involves two fine-tuned models designed for English-to-Galician translation tasks:

T5-small: A lightweight model, ideal for flexible translations with moderate training resources.
Helsinki-NLP/opus-mt-en-gl: A high-performance model tailored for domain-specific translation tasks.

Model Description

This model was developed to translate English instructions into Galician outputs using fine-tuned machine learning techniques. It achieves robust translation capabilities through careful training and evaluation.

Base Model: T5-small
Languages: English (en), Galician (gl)
Dataset: Custom dataset with 66,358 examples
Training Epochs: 3
Batch Size: 32
Learning Rate: 5e-5
Framework: PyTorch with 🤗 Transformers

Repository Structure

training.ipynb: Notebook for fine-tuning the models.
evaluation.ipynb: Notebook for evaluating outputs using semantic and human-level metrics.
Galician.json: Dataset containing English-Galician translation pairs.
translation_models/: Directory to save fine-tuned models.
scripts/: Helper functions for preprocessing, training, and evaluation.

Setup

Clone the repository:
git clone https://github.com/username/Translation-Models-En-Gl.git
Install dependencies:
pip install transformers datasets sentencepiece torch sacrebleu rouge-score
Ensure a GPU is available for training (e.g., Google Colab or local machine with CUDA).

Usage

Fine-tuning

Use the training.ipynb notebook to fine-tune T5-small or Helsinki-NLP models.
The trained models are saved in the translation_models directory.

Evaluation

Run the evaluation.ipynb notebook to generate outputs and compute metrics.
Modify the instruction and input text to test different scenarios.

Example

python

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("Swethamani/Translation-Models-En-G")
model = AutoModelForSeq2SeqLM.from_pretrained("Swethamani/Translation-Models-En-G")

# Translate English to Galician
input_text = "Translate this text into Galician."
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Results

T5-small

Strengths: Provides flexibility and decent translation quality.
Limitations: May require more fine-tuning for nuanced sentences.

Helsinki-NLP

Strengths: Offers superior translation quality for complex or domain-specific texts.
Limitations: Lacks generalization for other NLP tasks.

Evaluation

Metrics

Semantic Similarity: Using SentenceTransformer embeddings to compare input and output.
BLEU and ROUGE Scores: For assessing translation quality.
Human Evaluation: Scored on relevance, fluency, coherence, engagement, and creativity.

Uses

Direct Use

Translating English instructions into Galician.
Generating Galician outputs based on English input prompts.

Results Comparison

View the training loss and evaluation scores for both models.
Compare BLEU, ROUGE, and human evaluation scores to determine performance.

Acknowledgements

Libraries Used: Hugging Face transformers, SentenceTransformer, datasets, sacrebleu, and rouge-score.
Dataset: Custom-curated English-Galician translations.