Upload project.md

project.md

@@ -0,0 +1,87 @@
+# English to Hindi Text Translation using Transformers
+
+This project showcases a simple text translation model that translates English text to Hindi using the Hugging Face Transformers library. The model utilizes pre-trained sequence-to-sequence architecture for accurate and efficient translation.
+
+## Table of Contents
+
+- [Project Overview](#project-overview)
+- [Installation](#installation)
+- [Usage](#usage)
+- [Model Training and Dataset](#model-training-and-dataset)
+- [Model Testing and Deployment](#model-testing-and-deployment)
+- [User Interface](#user-interface)
+- [Challenges Faced](#challenges-faced)
+- [Contributions](#contributions)
+
+## Project Overview
+
+Text translation is an essential task in natural language processing, and this project aims to provide a practical example of building and deploying a translation model. The project covers the following aspects:
+
+- Data preprocessing: Tokenization and dataset preparation.
+- Model training: Training a sequence-to-sequence model for English-to-Hindi translation.
+- Model testing: Translating text using the trained model.
+- User interface: Creating a user-friendly interface for text translation.
+
+## Installation
+
+To run this project, you'll need the following dependencies:
+
+- Python 3.x
+- TensorFlow
+- Hugging Face Transformers
+- Datasets library
+- Gradio
+
+You can install the required libraries using the following shell command:
+
+```shell
+pip install datasets transformers[sentencepiece] tensorflow gradio -q
+```
+
+## Usage
+Checkout the app [here](https://huggingface.co/spaces/Lohith9923/En-Hi-Translation) where you need to give english sentences or text in input textbox and output is translated text or sentence in Hindi. 
+
+## Model Training and Dataset
+For training the text translation model.
+You can checkout the pre-trained model from [here](https://colab.research.google.com/corgiredirector?site=https%3A%2F%2Fhuggingface.co%2FHelsinki-NLP%2Fopus-mt-en-hi) and Dataset from [here](https://huggingface.co/datasets/cfilt/iitb-english-hindi/viewer/cfilt--iitb-english-hindi).
+- First Download the pre-trained model using **transformers** library in python.
+- Load the Dataset **cfilt/iitb-english-hindi** using **Datasets** library in python.
+- Initialized the model, tokenizer, and preprocessing function.
+- Tokenized the dataset and prepared the training and validation data.
+- Compiled the model with the optimizer(**Adam**) with required parameters.
+- Trained the model for the desired number of epochs.
+
+## Model Testing and Deployment
+To test the trained model and deploy a user interface:
+
+- Saved the trained model at a preferred location.
+- Loaded the model from the location and tokenizer for testing.
+- Translated sample input text using the model.
+- Deployed a Gradio interface for user-friendly translation.
+
+## User Interface
+
+The Gradio interface provides an interactive way to translate English text to Hindi. To use the interface:
+
+- Run the project and navigate to the specified URL.
+- Enter English text in the input box.
+- Checkout the translated Hindi text in the output box.
+
+## Challenges Faced
+
+- Surfed through lot of resources in google and other platforms for best dataset for my project.
+- Spent a lot of time gathering the correct resources for understanding about transformers, LLM's and gradio.
+
+## Contributions
+Contributions to this project are welcome! Here are some ways you can contribute:
+
+- Improve the model's translation quality and performance.
+- Enhance the user interface for a better user experience.
+- Add support for more languages and translation directions.
+
+To contribute, follow these steps:
+
+- Fork this repository.
+- Create a new branch for your feature or bug fix.
+- Commit your changes and push them to your fork.
+- Open a pull request with a detailed description of your changes.