metadata
library_name: transformers
language:
- np
base_model: RoBERTa
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: RoBERTa-devangari-script-classification
results: []
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
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
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 | 0.9997 | 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 | 2.9991 | 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