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IC_segundo

Text Classification
Transformers
TensorFlow
bert
generated_from_keras_callback
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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