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: 0.0617
- F1 Micro: 0.7029
- F1 Macro: 0.0254
- Accuracy: 0.6459
- Recall Micro: 0.6169
- Precision Micro: 0.8169
- Recall Macro: 0.0269
- Precision Macro: 0.0240
- F1: 0.5817
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: 1e-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 |
| 0.0577 | 2.0 | 3052 | 0.0640 | 0.6943 | 0.0251 | 0.6398 | 0.6111 | 0.8038 | 0.0267 | 0.0236 | 0.5742 |
| 0.0674 | 3.0 | 4578 | 0.0613 | 0.6949 | 0.0252 | 0.6257 | 0.5976 | 0.8300 | 0.0261 | 0.0244 | 0.5778 |
| 0.0576 | 4.0 | 6104 | 0.0610 | 0.7006 | 0.0254 | 0.6358 | 0.6073 | 0.8278 | 0.0265 | 0.0243 | 0.5814 |
| 0.0387 | 5.0 | 7630 | 0.0617 | 0.7029 | 0.0254 | 0.6459 | 0.6169 | 0.8169 | 0.0269 | 0.0240 | 0.5817 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1