metadata
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: gustavokpc/IC_segundo
results: []
gustavokpc/IC_segundo
This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 0.0559
- Train Accuracy: 0.9805
- Train F1 M: 0.5583
- Train Precision M: 0.4028
- Train Recall M: 0.9686
- Validation Loss: 0.2533
- Validation Accuracy: 0.9327
- Validation F1 M: 0.5605
- Validation Precision M: 0.4028
- Validation Recall M: 0.9674
- Epoch: 4
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:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 3790, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
Training results
| Train Loss | Train Accuracy | Train F1 M | Train Precision M | Train Recall M | Validation Loss | Validation Accuracy | Validation F1 M | Validation Precision M | Validation Recall M | Epoch |
|---|---|---|---|---|---|---|---|---|---|---|
| 0.3576 | 0.8399 | 0.4604 | 0.3607 | 0.7042 | 0.2825 | 0.8997 | 0.5635 | 0.4127 | 0.9300 | 0 |
| 0.2012 | 0.9274 | 0.5204 | 0.3849 | 0.8616 | 0.2103 | 0.9175 | 0.5451 | 0.3970 | 0.9095 | 1 |
| 0.1312 | 0.9511 | 0.5451 | 0.3969 | 0.9273 | 0.2125 | 0.9307 | 0.5571 | 0.4017 | 0.9523 | 2 |
| 0.0871 | 0.9690 | 0.5547 | 0.4007 | 0.9557 | 0.2417 | 0.9301 | 0.5565 | 0.4013 | 0.9547 | 3 |
| 0.0559 | 0.9805 | 0.5583 | 0.4028 | 0.9686 | 0.2533 | 0.9327 | 0.5605 | 0.4028 | 0.9674 | 4 |
Framework versions
- Transformers 4.34.1
- TensorFlow 2.14.0
- Datasets 2.14.5
- Tokenizers 0.14.1