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README.md

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-# 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
-Download the folder from here and the run the following command
-
-```shell
-python3 app.py
-```
-After running this command 
-## 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.
 
+---
+title: Text Translation
+emoji: 👀
+colorFrom: purple
+colorTo: pink
+sdk: gradio
+sdk_version: 3.40.1
+app_file: app.py
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference