gustavokpc/IC_primeiro
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: 0.0532
- Train Accuracy: 0.9812
- Train F1 M: 0.5544
- Train Precision M: 0.4027
- Train Recall M: 0.9558
- Validation Loss: 0.2580
- Validation Accuracy: 0.9175
- Validation F1 M: 0.5588
- Validation Precision M: 0.4059
- Validation Recall M: 0.9423
- 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': 2e-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 |
---|---|---|---|---|---|---|---|---|---|---|
0.3533 | 0.8498 | 0.4723 | 0.4085 | 0.6530 | 0.2424 | 0.9037 | 0.5060 | 0.3909 | 0.7591 | 0 |
0.1974 | 0.9259 | 0.5184 | 0.3930 | 0.8161 | 0.1978 | 0.9202 | 0.5425 | 0.4014 | 0.8778 | 1 |
0.1242 | 0.9551 | 0.5382 | 0.3974 | 0.8918 | 0.1970 | 0.9248 | 0.5583 | 0.4106 | 0.9195 | 2 |
0.0823 | 0.9705 | 0.5511 | 0.4024 | 0.9370 | 0.2550 | 0.9116 | 0.5567 | 0.4057 | 0.9330 | 3 |
0.0532 | 0.9812 | 0.5544 | 0.4027 | 0.9558 | 0.2580 | 0.9175 | 0.5588 | 0.4059 | 0.9423 | 4 |
Framework versions
- Transformers 4.34.1
- TensorFlow 2.14.0
- Datasets 2.14.5
- Tokenizers 0.14.1
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