metadata
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: results
results: []
results
This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.7799
- Accuracy: 0.391
- F1: 0.2680
- Precision: 0.4970
- Recall: 0.391
- Mse: 11.53
- Mae: 2.086
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: 32
- 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: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Mse | Mae |
|---|---|---|---|---|---|---|---|---|---|
| 2.0415 | 1.0 | 157 | 1.7799 | 0.391 | 0.2680 | 0.4970 | 0.391 | 11.53 | 2.086 |
| 1.6256 | 2.0 | 314 | 1.6337 | 0.42 | 0.3217 | 0.4871 | 0.42 | 8.176 | 1.694 |
| 1.4552 | 3.0 | 471 | 1.6218 | 0.432 | 0.3467 | 0.4754 | 0.432 | 6.967 | 1.529 |
| 1.2905 | 4.0 | 628 | 1.6459 | 0.437 | 0.3596 | 0.3645 | 0.437 | 6.628 | 1.494 |
| 1.2211 | 5.0 | 785 | 1.6601 | 0.432 | 0.3684 | 0.3584 | 0.432 | 6.314 | 1.458 |
Framework versions
- Transformers 4.46.3
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.20.3