metadata
metrics:
- f1
model-index:
- name: binary_every_exp
results: []
binary_every_exp
This model is a fine-tuned version of monologg/kobigbird-bert-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0081
- Precision: 1.0
- Recall: 1.0
- F1: 1.0
- Accuracy: 1.0
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: 5e-05
- train_batch_size: 32
- eval_batch_size: 16
- 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 | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 19 | 0.0709 | 0.96 | 1.0 | 0.9796 | 0.9857 |
| No log | 2.0 | 38 | 0.0647 | 1.0 | 0.9583 | 0.9787 | 0.9857 |
| No log | 3.0 | 57 | 0.0095 | 1.0 | 1.0 | 1.0 | 1.0 |
| No log | 4.0 | 76 | 0.0166 | 1.0 | 0.9583 | 0.9787 | 0.9857 |
| No log | 5.0 | 95 | 0.0081 | 1.0 | 1.0 | 1.0 | 1.0 |
Framework versions
- Transformers 4.42.3
- Pytorch 2.3.0+cu121
- Datasets 2.18.0
- Tokenizers 0.19.1