AnnoMI-simple_speaker_role_id-bert-base-uncased-v1
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: 0.7799
Accuracy: 0.8443
Precision Macro: 0.8448
Recall Macro: 0.8442
F1 Macro: 0.8442
Precision Weighted: 0.8447
Recall Weighted: 0.8443
F1 Weighted: 0.8443
Report: precision recall f1-score support
0 0.83 0.86 0.85 488 1 0.86 0.83 0.84 482accuracy 0.84 970 macro avg 0.84 0.84 0.84 970 weighted avg 0.84 0.84 0.84 970
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: 8
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- 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
- lr_scheduler_warmup_steps: 100
- num_epochs: 20
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision Macro | Recall Macro | F1 Macro | Precision Weighted | Recall Weighted | F1 Weighted | Report |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.3142 | 2.0577 | 500 | 0.4281 | 0.8423 | 0.8438 | 0.8425 | 0.8421 | 0.8439 | 0.8423 | 0.8421 | precision recall f1-score support |
0 0.87 0.81 0.84 488
1 0.82 0.88 0.85 482
accuracy 0.84 970
macro avg 0.84 0.84 0.84 970 weighted avg 0.84 0.84 0.84 970 | | 0.1986 | 4.1155 | 1000 | 0.5841 | 0.8144 | 0.8250 | 0.8150 | 0.8131 | 0.8253 | 0.8144 | 0.8130 | precision recall f1-score support
0 0.89 0.73 0.80 488
1 0.76 0.90 0.83 482
accuracy 0.81 970
macro avg 0.82 0.81 0.81 970 weighted avg 0.83 0.81 0.81 970 | | 0.177 | 6.1732 | 1500 | 0.6025 | 0.8392 | 0.8392 | 0.8392 | 0.8392 | 0.8392 | 0.8392 | 0.8392 | precision recall f1-score support
0 0.84 0.84 0.84 488
1 0.84 0.84 0.84 482
accuracy 0.84 970
macro avg 0.84 0.84 0.84 970 weighted avg 0.84 0.84 0.84 970 | | 0.1595 | 8.2309 | 2000 | 0.5967 | 0.8495 | 0.8495 | 0.8495 | 0.8495 | 0.8495 | 0.8495 | 0.8495 | precision recall f1-score support
0 0.85 0.85 0.85 488
1 0.85 0.84 0.85 482
accuracy 0.85 970
macro avg 0.85 0.85 0.85 970 weighted avg 0.85 0.85 0.85 970 | | 0.1593 | 10.2887 | 2500 | 0.6821 | 0.8433 | 0.8433 | 0.8433 | 0.8433 | 0.8433 | 0.8433 | 0.8433 | precision recall f1-score support
0 0.84 0.85 0.84 488
1 0.85 0.84 0.84 482
accuracy 0.84 970
macro avg 0.84 0.84 0.84 970 weighted avg 0.84 0.84 0.84 970 | | 0.148 | 12.3464 | 3000 | 0.6469 | 0.8423 | 0.8456 | 0.8420 | 0.8418 | 0.8454 | 0.8423 | 0.8418 | precision recall f1-score support
0 0.81 0.89 0.85 488
1 0.88 0.79 0.83 482
accuracy 0.84 970
macro avg 0.85 0.84 0.84 970 weighted avg 0.85 0.84 0.84 970 | | 0.1446 | 14.4041 | 3500 | 0.7799 | 0.8443 | 0.8448 | 0.8442 | 0.8442 | 0.8447 | 0.8443 | 0.8443 | precision recall f1-score support
0 0.83 0.86 0.85 488
1 0.86 0.83 0.84 482
accuracy 0.84 970
macro avg 0.84 0.84 0.84 970 weighted avg 0.84 0.84 0.84 970 |
Framework versions
- Transformers 4.57.6
- Pytorch 2.9.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.2