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