README.md

---
base_model: facebook/mbart-large-50-many-to-many-mmt
tags:
  - translation
  - mbart50
  - english
  - telugu
  - hackhedron
  - neural-machine-translation
  - huggingface
license: apache-2.0
datasets:
  - hackhedron
metrics:
  - sacrebleu
model-index:
  - name: mbart50-en-te-hackhedron
    language:
      - en
      - te
    results:
      - task:
          name: Translation
          type: translation
        dataset:
          name: HackHedron English-Telugu Parallel Corpus
          type: hackhedron
          args: en-te
        metrics:
          - name: SacreBLEU
            type: sacrebleu
            value: 66.9240  
---
# 🌐 mBART50 English ↔ Telugu | HackHedron Dataset

This model is fine-tuned from [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the [HackHedron English-Telugu Parallel Corpus](https://huggingface.co/datasets). It supports bidirectional translation between **English ↔ Telugu**.

## 🧠 Model Architecture

- **Base model**: mBART50 (Multilingual BART with 50 languages)
- **Type**: Seq2Seq Transformer
- **Tokenizer**: MBart50TokenizerFast
- **Languages Used**:
  - `en_XX` for English
  - `te_IN` for Telugu

---

## 📚 Dataset

**HackHedron English-Telugu Parallel Corpus**  
- ~390,000 training sentence pairs  
- ~43,000 validation pairs  
- Format:
```json
{
  "english": "Tom started his car and drove away.",
  "telugu": "టామ్ తన కారును స్టార్ట్ చేసి దూరంగా నడిపాడు."
}
````

---

## 📈 Evaluation

| Metric    | Score  |  Loss   |
| --------- | ------ | ------- |
| SacreBLEU | 66.924 |  0.0511 |

> 🧪 Evaluation done using Hugging Face `evaluate` library on validation set.
> 
---

## 💻 How to Use

```python
from transformers import MBartForConditionalGeneration, MBart50TokenizerFast

model = MBartForConditionalGeneration.from_pretrained("koushik-reddy/mbart50-en-te-hackhedron")
tokenizer = MBart50TokenizerFast.from_pretrained("koushik-reddy/mbart50-en-te-hackhedron")

# Set source and target language
tokenizer.src_lang = "en_XX"
tokenizer.tgt_lang = "te_IN"

text = "How are you?"
inputs = tokenizer(text, return_tensors="pt")
generated_tokens = model.generate(**inputs, forced_bos_token_id=tokenizer.lang_code_to_id["te_IN"])
translated = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
print(translated[0])
```

---

## 📦 How to Fine-Tune Further

Use the `Seq2SeqTrainer` from Hugging Face:

```python
from transformers import Seq2SeqTrainer, Seq2SeqTrainingArguments
```

Make sure to properly set `forced_bos_token_id=tokenizer.lang_code_to_id["te_IN"]` during generation.

---

## 🛠️ Training Details

* Optimizer: AdamW
* Learning Rate: 2e-05
* Epochs: 1
* train_batch_size: 8
* eval_batch_size: 8
* seed: 42
* Truncation Length: 128 tokens
* Framework: 🤗 Transformers + Datasets
* Scheduler: Linear
* Mixed Precision: Enabled (fp16)

---

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Bleu    |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|
| 0.0455        | 1.0   | 48808 | 0.0511          | 66.9240 |

---

### Framework versions

- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1

---

## 🏷️ License

This model is licensed under [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0).

---

## 🤝 Acknowledgements

* 🤗 Hugging Face Transformers
* Facebook AI for mBART50
* HackHedron Parallel Corpus Contributors

---

> Created by **Koushik Reddy** – [Hugging Face Profile](https://huggingface.co/Koushim)