NLLB-200 Fine-Tuned for Colloquial Telugu
Model Description
This model is a fine-tuned version of the NLLB-200 (Distilled 600M) designed for translating English sentences into colloquial Telugu. It has been optimized to better capture informal and conversational nuances.
Model Details
- Model Name: anithasoma/nllb-finetuned-telugu
- Base Model: facebook/nllb-200-distilled-600M
- Fine-Tuned By: anithasoma
- Languages: English → Telugu (colloquial)
- Framework: Transformers (🤗 Hugging Face)
🚀 Run the Model on Google Colab
Click the badge above to launch the model in Google Colab!
Training Details
- Dataset: anithasoma/refined_en_te
- Training Environment: Google Colab with NVIDIA GPU.
- Fine-Tuning Method: LoRA + PEFT (Parameter Efficient Fine-Tuning)
- Epochs: Adjusted based on validation loss.
- Metrics: BLEU Score, SacreBLEU Score Perplexity, Human Evaluation.
Evaluation Metrics
The model was evaluated using the BLEU and SacreBLEU metrics:
- BLEU Score: 43.12
- SacreBLEU Score: 43.12
How to Use
You can use this model in Python with the transformers library:
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("anithasoma/nllb-finetuned-telugu")
model = AutoModelForSeq2SeqLM.from_pretrained("anithasoma/nllb-finetuned-telugu")
def translate(text):
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs)
return tokenizer.decode(outputs[0], skip_special_tokens=True)
print(translate("Hello, how are you?"))
Model Card
Intended Use
This model is intended for generating colloquial Telugu translations from English text, improving conversational AI, and enhancing informal communication applications.
Limitations
- May not perform well on formal or domain-specific text.
- Can sometimes produce literal rather than context-aware translations.
License
This model is licensed under the Apache 2.0 License.
Contributors
Developed by anithasoma as part of the SAWiT AI Hackathon.
For feedback or collaboration, reach out via Hugging Face! 🚀