translation en hi dataset:c4 license:apache-2.0 region:us
---
datasets:
- c4
language:
- en
- hi
license: apache-2.0
tags:
- translation
---

# English-to-Hindi Translation Model

## Table of Contents
1. [Introduction](#introduction)
2. [Model Overview](#model-overview)
3. [Installation](#installation)
4. [Quick Start](#quick-start)
5. [Detailed Usage](#detailed-usage)
6. [Customization and Training](#customization-and-training)
7. [Contributing](#contributing)
8. [Citation](#citation)
9. [License](#license)
10. [Contact](#contact)
11. [Acknowledgements](#acknowledgements)

<a name="introduction"></a>
## Introduction
This repository houses an advanced English-to-Hindi translation model leveraging Google's mT5-small powerful transformers. It is designed for accurate, context-aware translations, utilizing cutting-edge NLP techniques.

<a name="model-overview"></a>
## Model Overview
- **Base Model**: Adapted from Google's mT5-small models
- **Language Pair**: English to Hindi
- **Training Data**: Large-scale, diverse dataset ensuring nuanced translation
- **Features**: Customized tokenizer, handling of long sentences, and attention mechanisms for context preservation

<a name="installation"></a>
## Installation
```bash
pip install transformers tensorflow pandas dataset tqdm
```

<a name="quick-start"></a>
```sh
git lfs install
git clone https://huggingface.co/nashit93/EnglishToHindiTranslationLongContext
cd EnglishToHindiTranslationLongContext
```

## Quick Start
```python
#!/usr/bin/env python
# coding: utf-8

import tensorflow as tf
import os
from transformers import TFMT5ForConditionalGeneration, MT5Tokenizer
import pandas as pd
from datasets import Dataset
from tqdm import tqdm
# Set up tokenizer
tokenizer = MT5Tokenizer.from_pretrained("google/mt5-small")




# Function to load the model from the latest checkpoint
def load_model(checkpoint_dir):
    latest_checkpoint = None
    if os.path.exists(checkpoint_dir):
        checkpoints = [os.path.join(checkpoint_dir, d) for d in os.listdir(checkpoint_dir)]
        checkpoints = [d for d in checkpoints if os.path.isdir(d)]
        if checkpoints:
            latest_checkpoint = max(checkpoints, key=os.path.getmtime)

    if latest_checkpoint:
        print("Loading model from:", latest_checkpoint)
        return TFMT5ForConditionalGeneration.from_pretrained(latest_checkpoint)
    else:
        print("No checkpoint found, loading default model")
        return TFMT5ForConditionalGeneration.from_pretrained("google/mt5-small")

# Load the model
model = load_model('model_checkpoints-small-on-1mill-dp')

# Function to prepare text for prediction
def prepare_text(text, tokenizer, max_length=200):
    inputs = tokenizer.encode(text, return_tensors="tf", max_length=max_length, truncation=True)
    return inputs

# Function to generate prediction with adjustable settings
def generate_prediction(text, model, tokenizer, max_length=2000, num_beams=5):
    input_ids = prepare_text(text, tokenizer, max_length=max_length)
    output_ids = model.generate(
        input_ids,
        max_length=max_length,
        num_beams=num_beams,
        no_repeat_ngram_size=2,
        early_stopping=True
    )
    return tokenizer.decode(output_ids[0], skip_special_tokens=True)


# Example Prediction
sample_text = "Hi , how are you?"
prediction = generate_prediction(sample_text, model, tokenizer)
print("Prediction:", prediction)

```

<a name="detailed-usage"></a>
## Detailed Usage
Explore advanced functionalities, including batch processing, handling different text lengths, and model fine-tuning.

<a name="contributing"></a>
## Contributing

We warmly welcome contributions from the community to improve and enhance the English-to-Hindi translation model. If you're looking to contribute, here's how you can get started:

## How to Contribute

### Reporting Bugs and Issues
- **Identify and Report**: If you find a bug or an issue, report it by creating a new issue on the GitHub repository.
- **Details**: Provide as much detail as possible about the bug/issue, including steps to reproduce it, error messages, and the environment in which it occurred.

### Suggesting Enhancements
- **Feature Requests**: Ideas for new features or enhancements to existing ones are always appreciated.
- **Use Cases**: Describe the use case for your feature, helping us understand the context and benefit.

### Code Contributions
1. **Fork the Repository**: Start by forking the repository to your GitHub account.
2. **Clone Locally**: Clone your forked repository to your local machine.
3. **Create a Branch**: Create a branch for your feature or fix. Use a clear branch name that describes the purpose of your contribution.
4. **Commit Changes**: Make your changes in your branch and commit them with clear, detailed messages.
5. **Write Tests**: If applicable, write tests to ensure the robustness and reliability of your contribution.
6. **Lint and Test Your Code**: Make sure your code adheres to the project's style guidelines and passes all tests.
7. **Pull Request**: Submit a pull request to the main repository for review. Ensure you describe your changes and their impact.

### Documentation
- **Improvements**: Improvements to documentation, including README, code comments, and docstrings, are always valuable.
- **Clarity and Accuracy**: Ensure the documentation is clear, accurate, and helps users understand and use the model effectively.

### Review Process
After submitting your pull request:
- **Code Review**: Maintainers will review your code and may request changes or provide feedback.
- **Discussion**: Engage in discussions and revisions until your contribution meets the necessary standards.

### Community Guidelines
- **Respect and Inclusivity**: All contributions should respect community guidelines of inclusivity and mutual respect.
- **Code of Conduct**: Adhere to the project's code of conduct to ensure a welcoming and open environment for everyone.

### Staying Updated
- **Syncing Fork**: Keep your fork synced with the main repository to stay updated with changes and avoid merge conflicts.

## Need Help?
If you need help or have questions about contributing, feel free to reach out @ nashit93@gmail.com

Your contributions, big or small, make a significant impact on the project and the community. We look forward to your ideas, bug reports, feature requests, and code contributions! 🌟

<a name="citation"></a>
## Citation
If you use this model in your research, please cite it.

<a name="license"></a>
## License
This project is licensed under the Apache License 2.0 - see the `LICENSE` file for details.

<a name="contact"></a>
## Contact
Reach out to the maintainers at nashit93@gmail.com for any questions or collaboration inquiries.

<a name="acknowledgements"></a>
## Acknowledgements
- Hugging Face and Google team for their transformer models
- Dataset providers and contributors to the open-source NLP community

