Meli101/biomedNLP-text-classifier-tf

This model is a fine-tuned version of microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract on an unknown dataset. It achieves the following results on the evaluation set:

• Train Loss: 0.0983
• Validation Loss: 0.2868
• Train Precision: 0.9210
• Train Recall: 0.9212
• Train Accuracy: 0.9211
• Train F1: 0.9210
• Epoch: 2

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': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1535, '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	Validation Loss	Train Precision	Train Recall	Train Accuracy	Train F1	Epoch
0.5233	0.3586	0.8710	0.8670	0.8666	0.8674	0
0.2081	0.2713	0.9096	0.9099	0.9097	0.9097	1
0.0983	0.2868	0.9210	0.9212	0.9211	0.9210	2

Framework versions

• Transformers 4.37.2
• TensorFlow 2.15.0
• Datasets 2.17.1
• Tokenizers 0.15.2