RoBERTa-devangari-script-classification

This model is a fine-tuned version of 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