| --- |
| library_name: transformers |
| language: |
| - nep |
| - hi |
| - sa |
| - mr |
| base_model: RoBERTa |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| - f1 |
| - precision |
| - recall |
| model-index: |
| - name: RoBERTa-devangari-script-classification |
| results: [] |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # RoBERTa-devangari-script-classification |
|
|
| This model is a fine-tuned version of [RoBERTa](https://huggingface.co/RoBERTa) on the Custom Devangari Datasets dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.0329 |
| - Accuracy: 0.9935 |
| - F1: 0.9935 |
| - Precision: 0.9935 |
| - Recall: 0.9935 |
|
|
| ## Model description |
|
|
| This model is a fine-tuned version of RoBERTa, optimized for multiclass text classification on datasets written in |
| Devanagari script across multiple languages, including Nepali, Marathi, Sanskrit, Bhojpuri, and Hindi. By leveraging the |
| robust RoBERTa architecture, this model has been fine-tuned to recognize intricate patterns and contextual |
| cues within Devanagari text, achieving high accuracy and F1 scores for multiclass classification tasks. |
|
|
| ## Intended uses & limitations |
|
|
| #### Intended Uses: |
|
|
| - Multiclass text classification for Nepali, Marathi, Sanskrit, Bhojpuri, and Hindi, written in Devanagari script. |
| - Suitable for sentiment analysis, topic categorization, and public opinion monitoring. |
|
|
| #### Limitations: |
|
|
| - Limited to Devanagari script; accuracy may drop on other scripts. |
| - Fine-tuned for multiclass classification; may not generalize well to other tasks or binary classifications. |
| - Language-specific nuances not present in the dataset may impact performance on certain dialects. |
|
|
| ## Training procedure |
|
|
| ### Training hyperparameters |
|
|
| The following hyperparameters were used during training: |
| - learning_rate: 3e-05 |
| - train_batch_size: 16 |
| - eval_batch_size: 16 |
| - seed: 42 |
| - gradient_accumulation_steps: 2 |
| - total_train_batch_size: 32 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - lr_scheduler_warmup_steps: 500 |
| - num_epochs: 3 |
| - mixed_precision_training: Native AMP |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
| |:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
| | 0.2337 | 1.0 | 1638 | 0.0603 | 0.9874 | 0.9874 | 0.9875 | 0.9874 | |
| | 0.0513 | 2.0 | 3277 | 0.0387 | 0.9919 | 0.9919 | 0.9919 | 0.9919 | |
| | 0.0252 | 3.0 | 4914 | 0.0329 | 0.9935 | 0.9935 | 0.9935 | 0.9935 | |
|
|
| ### Framework versions |
|
|
| - Transformers 4.44.2 |
| - Pytorch 2.4.1+cu121 |
| - Datasets 3.0.2 |
| - Tokenizers 0.19.1 |
