SujathaL/english-telugu-colloquial-translator

Model Overview: This model is a fine-tuned version of unsloth/tinyllama-chat-bnb-4bit, designed for translating English text into Telugu while preserving colloquial expressions. The model helps generate natural and contextually relevant translations for everyday conversations.

Model Description: Base Model: unsloth/tinyllama-chat-bnb-4bit Objective: English to Telugu translation with a focus on colloquial speech. Fine-tuning Dataset: Custom dataset (details provided below). Framework: PyTorch with Hugging Face transformers and PEFT. Quantization: 4-bit for efficient inference.

Intended Uses & Limitations Intended Uses ✅ Translation of English text to Telugu while maintaining natural conversational flow. ✅ Useful for chatbots, customer support, and informal communication. ✅ Can assist language learners in understanding Telugu colloquialisms.

Limitations ⚠️ May not perform well on highly technical or formal translations. ⚠️ Might struggle with complex sentence structures or idiomatic expressions. ⚠️ Requires further fine-tuning for domain-specific translations.

Training and Evaluation Data: The model was fine-tuned on a custom dataset containing parallel English-Telugu colloquial sentences. The dataset includes:

-Conversations from social media platforms. -Informal discussions and day-to-day dialogues. -Commonly used Telugu expressions and their English equivalents.

Training Procedure: Training Hyperparameters:

Hyperparameter Value

Learning Rate 0.0003 Training Batch Size 4 Evaluation Batch Size 4 Gradient Accumulation Steps 2 Total Train Batch Size 8 Optimizer AdamW LR Scheduler Linear Warmup Steps 2 Epochs 10 Mixed Precision Training Native AMP

Training Results: The model achieved the following performance on the evaluation set:

Training Loss Epoch Step Validation Loss 8.9699 0.2 2 5.5126 4.0118 2.0 20 5.4684 1.1042 5.2 52 5.1425 0.9898 10.0 100 5.2763

Model Performance and Evaluation: The model was evaluated using a validation dataset, and the loss metric was used to measure performance.

-Lower loss values indicate better performance. -The model shows improvement over training epochs, stabilizing around a loss of 5.27.

Framework Versions:

PEFT: 0.14.0 Transformers: 4.49.0 PyTorch: 2.6.0+cu124 Datasets: 3.2.0 Tokenizers: 0.21.0

How to Use the Model: You can use this model with Hugging Face’s transformers library model_name = "SujathaL/english-telugu-colloquial-translator"

Limitations & Future Improvements:

Possible Enhancements: Fine-tune on a larger, more diverse dataset. Implement reinforcement learning for better colloquial accuracy. Test on real-world conversational datasets.

Known Issues: May require post-processing for fluency. Some idioms may not translate accurately.

Contributors Sujatha Lakkoju (Model Developer) Dataset Sources (ChatGPT)

License: This model is released under the MIT License.

Contact: If you have any queries, feel free to reach out at [email protected]