---
license: mit
base_model: microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext
tags:
- generated_from_keras_callback
model-index:
- name: Kikia26/FineTunePubMedBertWithTensorflowKeras3
  results: []
---

<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->

# Kikia26/FineTunePubMedBertWithTensorflowKeras3

This model is a fine-tuned version of [microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext](https://huggingface.co/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