Update README.md

README.md

@@ -1,5 +1,4 @@
 ---
-inference: false
 license: mit
 datasets:
 - mwz/ur_para
@@ -8,65 +7,34 @@ language:
 tags:
 - 'paraphrase '
 ---
-<h1 align="center">Urdu Paraphrase Generation Model</h1>
+# Urdu Paraphrasing Model
 
-<p align="center">
-  <b>Fine-tuned model for Urdu paraphrase generation</b>
-</p>
+This repository contains a trained Urdu paraphrasing model based on the BERT-based encoder-decoder architecture. The model has been fine-tuned on the Urdu Paraphrase Dataset and can generate paraphrases for given input sentences in Urdu.
 
 ## Model Description
 
-The Urdu Paraphrase Generation Model is a language model trained on 30k rows dataset of Urdu paraphrases. It is based on the `roberta-urdu-small` architecture, fine-tuned for the specific task of generating high-quality paraphrases.
-
-## Features
-
-- 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.
+The model is built using the Hugging Face Transformers library and is trained on the BERT-base-uncased model. It employs an encoder-decoder architecture where the BERT model serves as the encoder, and another BERT model is used as the decoder. The model is trained to generate paraphrases by reconstructing the input sentences.
 
 ## Usage
 
-### Installation
+To use the trained model for paraphrasing Urdu sentences, you can follow the steps below:
 
-To use the Urdu Paraphrase Generation Model, follow these steps:
-
-1. Install the `transformers` library:
-```bash
-pip install transformers
-```
-2. Load the model and tokenizer in your Python script:
-```
-from transformers import AutoModelForMaskedLM, AutoTokenizer
+1. Install the required dependencies by running the following command:
+2. Load the trained model using the Hugging Face Transformers library:
+```python
+from transformers import EncoderDecoderModel, BertTokenizer
 
 # Load the model and tokenizer
-model = AutoModelForMaskedLM.from_pretrained("urduhack/roberta-urdu-small")
-tokenizer = AutoTokenizer.from_pretrained("urduhack/roberta-urdu-small")
-```
-## Generating Paraphrases
-Use the following code snippet to generate paraphrases with the loaded model and tokenizer:
-```
-# Example sentence
-input_sentence = "تصوراتی طور پر کریم سکمنگ کی دو بنیادی جہتیں ہیں - مصنوعات اور جغرافیہ۔"
-
-# Tokenize the input sentence
-inputs = tokenizer(input_sentence, truncation=True, padding=True, return_tensors="pt")
-input_ids = inputs.input_ids.to(model.device)
-attention_mask = inputs.attention_mask.to(model.device)
-
-# Generate paraphrase
-with torch.no_grad():
-    outputs = model.generate(input_ids, attention_mask=attention_mask, max_length=128)
-
-paraphrase = tokenizer.decode(outputs[0], skip_special_tokens=True)
-print("Paraphrase:", paraphrase)
+model = EncoderDecoderModel.from_pretrained("mwz/UrduParaphraseBERT")
+tokenizer = BertTokenizer.from_pretrained("mwz/UrduParaphraseBERT")
+
+def paraphrase_urdu_sentence(sentence):
+    input_ids = tokenizer.encode(sentence, padding="longest", truncation=True, max_length=512, return_tensors="pt")
+    generated_ids = model.generate(input_ids=input_ids, max_length=128, num_beams=4, no_repeat_ngram_size=2)
+
+    paraphrase = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
+    return paraphrase
+sentence = "ایک مثالی روشنی کا مشہور نقطہ آبادی چھوٹی چھوٹی سڑکوں میں اپنے آپ کو خوشگوار کرسکتی ہے۔"
+paraphrased_sentence = paraphrase_urdu_sentence(sentence)
+print(paraphrased_sentence)
 ```
-
-## Performance
-The model has been fine-tuned on a 30k rows dataset of Urdu paraphrases and achieves impressive performance in generating high-quality paraphrases. Detailed performance metrics, such as accuracy and fluency, are being evaluated and will be updated soon.
-
-## Contributing
-Contributions to the Urdu Paraphrase Generation Model are welcome! If you find any issues or have suggestions for improvements, please open an issue or submit a pull request.
-
-## License
-
-This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.