metadata
library_name: transformers
license: apache-2.0
base_model: google-bert/bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: trainer_output
results: []
trainer_output
This model is a fine-tuned version of google-bert/bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1952
- Accuracy: 0.9533
- F1: 0.9531
- Precision: 0.9536
- Recall: 0.9533
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 OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 573
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.2164 | 1.0 | 1147 | 0.2145 | 0.9150 | 0.9141 | 0.9159 | 0.9150 |
| 0.1446 | 2.0 | 2294 | 0.1533 | 0.9407 | 0.9400 | 0.9428 | 0.9407 |
| 0.113 | 3.0 | 3441 | 0.1595 | 0.9448 | 0.9443 | 0.9465 | 0.9448 |
| 0.065 | 4.0 | 4588 | 0.1783 | 0.9492 | 0.9492 | 0.9492 | 0.9492 |
| 0.0522 | 5.0 | 5735 | 0.2001 | 0.9507 | 0.9505 | 0.9509 | 0.9507 |
Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 2.14.4
- Tokenizers 0.21.1