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
Dataset: 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

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:

@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.

Model Card Authors [optional]

[Your Name or Organization]

Model Card Contact

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