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FineTunePubMedBertWithTensorflowKeras3

Token Classification
Transformers
TensorFlow
bert
generated_from_keras_callback
Inference Endpoints
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FineTunePubMedBertWithTensorflowKeras3 / README.md
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metadata
license: mit
base_model: microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext
tags:
  - generated_from_keras_callback
model-index:
  - name: Kikia26/FineTunePubMedBertWithTensorflowKeras3
    results: []

Kikia26/FineTunePubMedBertWithTensorflowKeras3

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

  • Train Loss: 0.0981
  • Validation Loss: 0.3764
  • Train Precision: 0.6444
  • Train Recall: 0.7342
  • Train F1: 0.6864
  • Train Accuracy: 0.9014
  • Epoch: 12

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': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 200, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
  • training_precision: float32

Training results

Train Loss Validation Loss Train Precision Train Recall Train F1 Train Accuracy Epoch
1.4820 0.8904 0.0 0.0 0.0 0.7808 0
0.8734 0.6681 0.6159 0.1793 0.2778 0.8274 1
0.6618 0.5098 0.6180 0.4641 0.5301 0.8673 2
0.4675 0.4214 0.6199 0.5781 0.5983 0.8841 3
0.3731 0.3833 0.5849 0.6540 0.6175 0.8910 4
0.2830 0.3550 0.6019 0.6730 0.6355 0.8958 5
0.2357 0.3555 0.6137 0.7004 0.6542 0.9025 6
0.2042 0.3500 0.6325 0.6646 0.6481 0.9004 7
0.1721 0.3511 0.5891 0.7046 0.6417 0.8964 8
0.1516 0.3692 0.6264 0.7004 0.6614 0.9017 9
0.1281 0.3477 0.6508 0.7194 0.6834 0.9046 10
0.1058 0.3701 0.6232 0.7257 0.6706 0.9012 11
0.0981 0.3764 0.6444 0.7342 0.6864 0.9014 12

Framework versions

  • Transformers 4.35.2
  • TensorFlow 2.14.0
  • Datasets 2.15.0
  • Tokenizers 0.15.0