README.md

metadata

license: apache-2.0
base_model: bert-large-uncased
tags:
  - generated_from_keras_callback
model-index:
  - name: gustavokpc/IC_11
    results: []

gustavokpc/IC_11

This model is a fine-tuned version of bert-large-uncased on an unknown dataset. It achieves the following results on the evaluation set:

• Train Loss: 0.1200
• Train Accuracy: 0.9569
• Train F1 M: 0.5414
• Train Precision M: 0.3981
• Train Recall M: 0.9034
• Validation Loss: 0.2290
• Validation Accuracy: 0.9202
• Validation F1 M: 0.5513
• Validation Precision M: 0.4022
• Validation Recall M: 0.9261
• Epoch: 6

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-06, 'decay_steps': 5306, '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
0.4434	0.8014	0.4236	0.3661	0.5865	0.2743	0.8872	0.4964	0.3787	0.7644	0
0.2804	0.8901	0.4898	0.3778	0.7488	0.2824	0.8879	0.5567	0.4181	0.8782	1
0.2254	0.9128	0.5069	0.3838	0.8028	0.2388	0.9090	0.5468	0.4009	0.9053	2
0.1873	0.9303	0.5203	0.3889	0.8490	0.2200	0.9149	0.5561	0.4075	0.9235	3
0.1614	0.9404	0.5316	0.3944	0.8756	0.2188	0.9235	0.5566	0.4080	0.9242	4
0.1380	0.9497	0.5359	0.3956	0.8898	0.2205	0.9228	0.5506	0.4024	0.9213	5
0.1200	0.9569	0.5414	0.3981	0.9034	0.2290	0.9202	0.5513	0.4022	0.9261	6

Framework versions

• Transformers 4.34.1
• TensorFlow 2.14.0
• Datasets 2.14.5
• Tokenizers 0.14.1