Model Card for MBart English to Urdu
This model is designed to translate text from English to Urdu using the mBART architecture, fine-tuned with the BitFit method.
Model Details
Model Description
This model leverages the mBART (Multilingual BART) architecture to perform English-to-Urdu translation. The model was fine-tuned on a custom English-Urdu dataset, and it uses efficient fine-tuning techniques such as BitFit to ensure high-quality translations.
- Developed by: Mudasir692
- Model type: mBART
- Language(s) (NLP): English, Urdu
- License: MIT
- Finetuned from model: mBART50
Model Sources
Uses
Direct Use
This model is intended for direct use in English-to-Urdu machine translation tasks.
Downstream Use
The model can be fine-tuned further for specific tasks in machine translation or other NLP tasks involving multilingual data. You can use adapters or prefix tuning.
Out-of-Scope Use
The model is not suitable for domains where high accuracy is critical or where domain-specific training data is required. Misuse in sensitive domains like legal or medical translation should be avoided.
Bias, Risks, and Limitations
The model may inherit biases from the dataset, including socio-political biases and cultural influences in the training data. Testing on domain-specific data is recommended before using it in production.
Recommendations
Users should be made aware of the potential biases in the translations and consider domain-specific testing before deploying the model in real-world applications.
How to Get Started with the Model
To get started with the model, use the following code snippet to load the model and tokenizer, input English sentences, and generate translations to Urdu.
1from transformers import MBartForConditionalGeneration, MBart50TokenizerFast
2
3# Load the tokenizer and model
4tokenizer = MBart50TokenizerFast.from_pretrained("Mudasir692/mbart-eng-ur")
5model = MBartForConditionalGeneration.from_pretrained("Mudasir692/mbart-eng-ur")
6
7# Example input text (English)
8input_text = "This is an example sentence."
9
10# Tokenize the input text
11inputs = tokenizer(input_text, return_tensors="pt")
12
13# Generate the translation
14translated = model.generate(**inputs)
15
16# Decode the translation and print the result
17output_text = tokenizer.decode(translated[0], skip_special_tokens=True)
18print("Translated Text (Urdu):", output_text)
