metadata
license: mit
base_model: microsoft/deberta-v3-small
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: results
results: []
results
This model is a fine-tuned version of microsoft/deberta-v3-small on the KMC dataset. It achieves the following results on the evaluation set:
- Loss: 0.0556
- Accuracy: 0.8722
- F1: 0.8849
- Precision: 0.8950
- Recall: 0.8750
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.0894 | 1.0 | 1633 | 0.0817 | 0.7508 | 0.8072 | 0.8774 | 0.7474 |
| 0.059 | 2.0 | 3266 | 0.0620 | 0.8264 | 0.8520 | 0.8815 | 0.8243 |
| 0.0429 | 3.0 | 4899 | 0.0531 | 0.8563 | 0.8751 | 0.8947 | 0.8563 |
| 0.032 | 4.0 | 6532 | 0.0547 | 0.8704 | 0.8838 | 0.8976 | 0.8705 |
| 0.0253 | 5.0 | 8165 | 0.0556 | 0.8722 | 0.8849 | 0.8950 | 0.8750 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1