Update README.md

README.md

@@ -6,52 +6,30 @@ language:
 - ur
 tags:
 - 'paraphrase '
-pipeline_tag: text2text-generation
 ---
-# Urdu Paraphrasing Model
+<h1 align="center">Urdu Paraphrase Generation Model</h1>
 
-This repository contains a pretrained model for Urdu paraphrasing. The model is based on the BERT architecture and has been fine-tuned on a large dataset of Urdu paraphrases.
+<p align="center">
+  <b>Fine-tuned model for Urdu paraphrase generation</b>
+</p>
 
 ## Model Description
 
-The pretrained model is based on the BERT architecture, specifically designed for paraphrasing tasks in the Urdu language. It has been trained using a large corpus of Urdu text to generate high-quality paraphrases.
+The Urdu Paraphrase Generation Model is a state-of-the-art language model trained on a large dataset of Urdu paraphrases. It is based on the `roberta-urdu-small` architecture, fine-tuned for the specific task of generating high-quality paraphrases.
 
-## Model Details
+## Features
 
-- Model Name: Urdu-Paraphrasing-BERT
-- Base Model: BERT
-- Architecture: Transformer
-- Language: Urdu
-- Dataset: Urdu Paraphrasing Dataset mwz/ur_para
+- Generate accurate and contextually relevant paraphrases in Urdu.
+- Maintain linguistic nuances and syntactic structures of the original input.
+- Handle a variety of input sentence lengths and complexities.
 
-## How to Use
+## Usage
 
-You can use this pretrained model for generating paraphrases for Urdu text. Here's an example of how to use the model:
+### Installation
 
-```python
-from transformers import pipeline
+To use the Urdu Paraphrase Generation Model, follow these steps:
 
-# Load the model
-model = pipeline("text2text-generation", model="path_to_pretrained_model")
-
-# Generate paraphrases
-input_text = "Urdu input text for paraphrasing."
-paraphrases = model(input_text, max_length=128, num_return_sequences=3)
-
-# Print the generated paraphrases
-print("Original Input Text:", input_text)
-print("Generated Paraphrases:")
-for paraphrase in paraphrases:
-    print(paraphrase["generated_text"])
-```
-## Training
-The model was trained using the Hugging Face transformers library. The training process involved fine-tuning the base BERT model on the Urdu Paraphrasing Dataset.
-
-## Evaluation
-The model's performance was evaluated on a separate validation set using metrics such as BLEU, ROUGE, and perplexity. However, please note that the evaluation results may vary depending on the specific use case.
-
-## Acknowledgments
-- The pretrained model is based on the BERT architecture developed by Google Research.
-
-## License
-This model and the associated code are licensed under the MIT License.
+1. Install the `transformers` library:
+'''bash
+pip install transformers
+'''