metadata
library_name: transformers
license: mit
base_model: microsoft/deberta-v3-small
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: results
results: []
results
This model is a fine-tuned version of microsoft/deberta-v3-small on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1435
- Accuracy: 0.9485
- Precision: 0.9559
- Recall: 0.9405
- F1: 0.9481
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: 2e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 500
- num_epochs: 6
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.344 | 1.0 | 782 | 0.1528 | 0.9438 | 0.9444 | 0.9430 | 0.9437 |
| 0.1362 | 2.0 | 1564 | 0.1435 | 0.9485 | 0.9559 | 0.9405 | 0.9481 |
| 0.1 | 3.0 | 2346 | 0.1799 | 0.9503 | 0.9533 | 0.9470 | 0.9501 |
| 0.0574 | 4.0 | 3128 | 0.2148 | 0.9516 | 0.9511 | 0.9521 | 0.9516 |
Framework versions
- Transformers 4.50.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1