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--- |
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inference: false |
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license: mit |
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datasets: |
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- mwz/ur_para |
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language: |
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- ur |
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tags: |
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- 'paraphrase ' |
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--- |
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<h1 align="center">Urdu Paraphrase Generation Model</h1> |
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<p align="center"> |
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<b>Fine-tuned model for Urdu paraphrase generation</b> |
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</p> |
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## Model Description |
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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. |
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## Features |
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- Generate accurate and contextually relevant paraphrases in Urdu. |
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- Maintain linguistic nuances and syntactic structures of the original input. |
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- Handle a variety of input sentence lengths and complexities. |
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## Usage |
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### Installation |
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To use the Urdu Paraphrase Generation Model, follow these steps: |
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1. Install the `transformers` library: |
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```bash |
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pip install transformers |
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``` |
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2. Load the model and tokenizer in your Python script: |
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``` |
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from transformers import AutoModelForMaskedLM, AutoTokenizer |
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# Load the model and tokenizer |
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model = AutoModelForMaskedLM.from_pretrained("urduhack/roberta-urdu-small") |
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tokenizer = AutoTokenizer.from_pretrained("urduhack/roberta-urdu-small") |
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``` |
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## Generating Paraphrases |
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Use the following code snippet to generate paraphrases with the loaded model and tokenizer: |
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``` |
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# Example sentence |
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input_sentence = "تصوراتی طور پر کریم سکمنگ کی دو بنیادی جہتیں ہیں - مصنوعات اور جغرافیہ۔" |
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# Tokenize the input sentence |
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inputs = tokenizer(input_sentence, truncation=True, padding=True, return_tensors="pt") |
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input_ids = inputs.input_ids.to(model.device) |
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attention_mask = inputs.attention_mask.to(model.device) |
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# Generate paraphrase |
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with torch.no_grad(): |
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outputs = model.generate(input_ids, attention_mask=attention_mask, max_length=128) |
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paraphrase = tokenizer.decode(outputs[0], skip_special_tokens=True) |
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print("Paraphrase:", paraphrase) |
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``` |
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## Performance |
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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. |
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## Contributing |
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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. |
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## License |
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This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details. |
