| | --- |
| | 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 |
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|
| | 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 |
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|
| | #### 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 |
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|
| | - Evaluated on a held-out validation split of the dataset. |
| |
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| | #### Factors |
| |
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| | - Evaluated across different sentence lengths and complexities. |
| |
|
| | #### Metrics |
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|
| | - **BLEU Score:** X.XX (Evaluated using `sacrebleu`) |
| |
|
| | ### Results |
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|
| | - 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 |
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|
| | - 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]**. |
