Model Card for Fine-tuned Helsinki-NLP/opus-mt-en-hi on IITB English-Hindi Dataset

Model Details

Model Description

This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-hi for English-to-Hindi translation using the IIT Bombay English-Hindi Parallel Corpus. The model is designed to translate English sentences into Hindi with improved accuracy and fluency.

Developed by: shogun-the-great
Model type: Seq2Seq (Sequence-to-Sequence) for Translation
Language(s): English to Hindi
License: Apache-2.0 (or specify your license)
Finetuned from model: Helsinki-NLP/opus-mt-en-hi

Model Sources

Dataset: IITB English-Hindi Dataset

Uses

Direct Use

This model can be directly used for English-to-Hindi translation tasks, such as:

Translating text-based content (e.g., documents, articles) from English to Hindi.
Assisting in bilingual applications requiring English-Hindi translation.
Language learning and cross-lingual understanding.

Out-of-Scope Use

This model may not perform well on:

Specialized domains like medical, legal, or technical text.
Translation of highly idiomatic, ambiguous, or informal sentences.

Bias, Risks, and Limitations

Bias

The model may inherit biases from the IIT Bombay English-Hindi dataset, such as:

Translation bias in cultural, gender, or regional contexts.
Limited coverage of less frequent phrases or idioms.

Risks

Inaccurate translations in critical scenarios (e.g., medical or legal use cases).
Possible loss of nuance or meaning in complex sentences.

Recommendations

Validate translations for critical use cases.
Fine-tune further on domain-specific datasets if required.

How to Get Started with the Model

You can load and use the fine-tuned model directly from the Hugging Face Hub:

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

# Load the tokenizer and model
model_name = "YourUsername/finetuned-opus-mt-en-hi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

# Example usage for translation
text = "Hello, how are you?"
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=128)
translation_ids = model.generate(inputs['input_ids'], max_length=128, num_beams=4, early_stopping=True)

# Decode the translated text
translation = tokenizer.decode(translation_ids[0], skip_special_tokens=True)
print("Translation:", translation)