| --- |
| license: apache-2.0 |
| datasets: |
| - anithasoma/refined_en_te |
| language: |
| - en |
| - te |
| metrics: |
| - bleu |
| - sacrebleu |
| base_model: |
| - facebook/nllb-200-distilled-600M |
| pipeline_tag: translation |
| library_name: transformers |
| tags: |
| - text-generation |
| - translation |
| - fine-tuned-model |
| - colloquial-language |
| - telugu |
| - machine-translation |
| --- |
| # NLLB-200 Fine-Tuned for Colloquial Telugu |
|
|
| ## Model Description |
| This model is a fine-tuned version of the [NLLB-200 (Distilled 600M)](https://huggingface.co/facebook/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](https://huggingface.co/anithasoma) |
| - **Languages:** English → Telugu (colloquial) |
| - **Framework:** Transformers (🤗 Hugging Face) |
| - |
| ## 🚀 Run the Model on Google Colab |
|
|
| [](https://colab.research.google.com/drive/1CiuywF2xzdzFH7jvQ7UIrBo4tI9FI9Nf?usp=sharing) |
|
|
| 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: |
|
|
| ```python |
| 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](https://www.apache.org/licenses/LICENSE-2.0). |
|
|
| ## Contributors |
| Developed by **[anithasoma](https://huggingface.co/anithasoma)** as part of the SAWiT AI Hackathon. |
|
|
| --- |
| *For feedback or collaboration, reach out via Hugging Face!* 🚀 |
