metadata
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned-bert-mrpc
results: []
finetuned-bert-mrpc
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: 0.4725
- Accuracy: 0.8382
- F1: 0.8881
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: 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
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.5236 | 0.4348 | 50 | 0.4882 | 0.7647 | 0.8452 |
| 0.4277 | 0.8696 | 100 | 0.3845 | 0.8431 | 0.8940 |
| 0.2727 | 1.3043 | 150 | 0.3824 | 0.8529 | 0.8980 |
| 0.2285 | 1.7391 | 200 | 0.3646 | 0.8358 | 0.8831 |
| 0.1305 | 2.1739 | 250 | 0.3605 | 0.8652 | 0.9002 |
| 0.1193 | 2.6087 | 300 | 0.4725 | 0.8382 | 0.8881 |
Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.2