---
library_name: transformers
tags: [translation, hinglish, LoRA, NLP]
---

# Model Card for English to Hinglish Translation Model

## Model Details

### Model Description

This is a fine-tuned **T5-small** model for translating English sentences into Hinglish (a mix of Hindi and English written in Latin script). The model was trained using **LoRA (Low-Rank Adaptation)** to optimize training efficiency.

- **Developed by:** Team AI-Pradarshan(Rashmi Rai, Ayesha, Bitasta)
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [Your Hugging Face Username]
- **Model type:** Sequence-to-Sequence Language Model
- **Language(s) (NLP):** English, Hinglish
- **License:** MIT
- **Finetuned from model [optional]:** google-t5/t5-small

### Model Sources [optional]

- **Repository:** [https://huggingface.co/rairashmi/hinglish_translation_lora](https://huggingface.co/rairashmi/hinglish_translation_lora)
- **Dataset:** [rairashmi/en-to-hinglish-dataset](https://huggingface.co/datasets/rairashmi/en-to-hinglish-dataset)

## Uses

### Direct Use

This model can be used to translate English sentences into Hinglish text directly via Hugging Face Transformers.

### Downstream Use [optional]

The model can be fine-tuned further or integrated into conversational AI systems and chatbots.

### Out-of-Scope Use

- This model is not designed for real-time conversational applications.
- It may not perform well on non-standard or highly domain-specific English text.

## Bias, Risks, and Limitations

- The dataset used may contain inherent biases in Hinglish translation styles.
- Accuracy may vary for different dialects and sentence structures.

### Recommendations

Users should be aware of translation inconsistencies and verify translations for critical applications.

## How to Get Started with the Model

```python
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

model_name = "rairashmi/hinglish_translation_lora"
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

def translate_english_to_hinglish(text):
    inputs = tokenizer(f"translate English to Hinglish: {text}", return_tensors="pt", padding=True, truncation=True)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

sentence = "How are you?"
translation = translate_english_to_hinglish(sentence)
print(f"🔹 English: {sentence}")
print(f"🟢 Hinglish: {translation}")
```

## Training Details

### Training Data

The model was trained on the **rairashmi/en-to-hinglish-dataset**, a parallel corpus of English-Hinglish text pairs.

### Training Procedure

#### Preprocessing [optional]

- Tokenized using the **T5 tokenizer**
- Padding and truncation applied with a max length of 128

#### Training Hyperparameters

- **Learning Rate:** 2e-5
- **Batch Size:** 8
- **Epochs:** 2
- **Mixed Precision:** FP16

#### Speeds, Sizes, Times [optional]

- Training took approximately **X hours** on an **A100 GPU**
- Model size: **T5-Small with LoRA adapters**

## Evaluation

### Testing Data, Factors & Metrics

#### Testing Data

- Evaluated on a held-out validation split of the dataset.

#### Factors

- Evaluated across different sentence lengths and complexities.

#### Metrics

- **BLEU Score:** X.XX (Evaluated using `sacrebleu`)

### Results

- The model achieves **X.XX BLEU Score** on the test set.

## Model Examination [optional]

[More Information Needed]

## Environmental Impact

- **Hardware Type:** A100 GPU
- **Hours used:** X
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]

## Technical Specifications [optional]

### Model Architecture and Objective

- The model is based on **T5-small** architecture fine-tuned for machine translation.

### Compute Infrastructure

#### Hardware

- Training was performed on a **single A100 GPU**

#### Software

- Transformers, Datasets, PEFT, Accelerate, Evaluate, Torch

## Citation [optional]

**BibTeX:**
```bibtex
@misc{hinglish_translation,
  author = {Your Name},
  title = {English to Hinglish Translation Model},
  year = {2025},
  url = {https://huggingface.co/rairashmi/hinglish_translation_lora}
}
```

## Glossary [optional]

- **Hinglish**: A mix of Hindi and English written in Latin script.

## More Information [optional]

For further details, check out the **[Hugging Face Model Page](https://huggingface.co/rairashmi/hinglish_translation_lora)**.

## Model Card Authors [optional]

- [Your Name or Organization]

## Model Card Contact

For any issues or questions, contact **[Your Contact Information]**.