metadata
base_model: FPTAI/vibert-base-cased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: vibert-base-cased-ed
results: []
vibert-base-cased-ed
This model is a fine-tuned version of FPTAI/vibert-base-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0595
- F1 Micro: 0.7034
- F1 Macro: 0.0430
- Accuracy: 0.6374
- Recall Micro: 0.6094
- Precision Micro: 0.8317
- Recall Macro: 0.0392
- Precision Macro: 0.0621
- F1: 0.5913
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 | F1 Micro | F1 Macro | Accuracy | Recall Micro | Precision Micro | Recall Macro | Precision Macro | F1 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.0696 | 1.0 | 1526 | 0.0711 | 0.6892 | 0.0243 | 0.7054 | 0.6737 | 0.7054 | 0.0294 | 0.0207 | 0.5573 |
| 0.055 | 2.0 | 3052 | 0.0622 | 0.6965 | 0.0252 | 0.6345 | 0.6060 | 0.8187 | 0.0265 | 0.0241 | 0.5775 |
| 0.0631 | 3.0 | 4578 | 0.0598 | 0.7054 | 0.0255 | 0.6436 | 0.6147 | 0.8274 | 0.0268 | 0.0243 | 0.5847 |
| 0.0534 | 4.0 | 6104 | 0.0591 | 0.6980 | 0.0260 | 0.6268 | 0.5989 | 0.8362 | 0.0265 | 0.0540 | 0.5809 |
| 0.0296 | 5.0 | 7630 | 0.0595 | 0.7034 | 0.0430 | 0.6374 | 0.6094 | 0.8317 | 0.0392 | 0.0621 | 0.5913 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1