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: bert-base-uncased_fold_4
results: []
bert-base-uncased_fold_4
This model is a fine-tuned version of bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0359
- Accuracy: 0.9938
- F1: 0.9885
- Precision: 0.9962
- Recall: 0.9808
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: 40
- eval_batch_size: 40
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.0252 | 1.0 | 3784 | 0.0346 | 0.9925 | 0.9860 | 0.9971 | 0.9751 |
| 0.0153 | 2.0 | 7568 | 0.0330 | 0.9935 | 0.9879 | 0.9971 | 0.9789 |
| 0.0129 | 3.0 | 11352 | 0.0359 | 0.9938 | 0.9885 | 0.9962 | 0.9808 |
Framework versions
- Transformers 5.3.0
- Pytorch 2.10.0+cu128
- Datasets 4.6.1
- Tokenizers 0.22.2