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# Kikia26/FineTunePubMedBertWithTensorflowKeras2

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: 0.0693
- Validation Loss: 0.3774
- Train Precision: 0.6399
- Train Recall: 0.7384
- Train F1: 0.6856
- Train Accuracy: 0.9030
- Epoch: 19

## 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 |
|:----------:|:---------------:|:---------------:|:------------:|:--------:|:--------------:|:-----:|
| 1.5823     | 0.9047          | 0.0             | 0.0          | 0.0      | 0.7808         | 0     |
| 0.9053     | 0.6998          | 0.5303          | 0.0738       | 0.1296   | 0.8106         | 1     |
| 0.6980     | 0.5341          | 0.7038          | 0.3861       | 0.4986   | 0.8591         | 2     |
| 0.5206     | 0.4613          | 0.6213          | 0.5295       | 0.5718   | 0.8753         | 3     |
| 0.4110     | 0.4201          | 0.6292          | 0.5549       | 0.5897   | 0.8836         | 4     |
| 0.3260     | 0.3918          | 0.6306          | 0.5907       | 0.6100   | 0.8937         | 5     |
| 0.2682     | 0.3682          | 0.5989          | 0.6709       | 0.6328   | 0.8985         | 6     |
| 0.2240     | 0.3445          | 0.6355          | 0.6730       | 0.6537   | 0.9041         | 7     |
| 0.1891     | 0.3593          | 0.5736          | 0.7152       | 0.6366   | 0.8913         | 8     |
| 0.1672     | 0.3609          | 0.5721          | 0.7278       | 0.6407   | 0.8908         | 9     |
| 0.1456     | 0.3594          | 0.5940          | 0.7131       | 0.6481   | 0.8969         | 10    |
| 0.1310     | 0.3519          | 0.6437          | 0.7089       | 0.6747   | 0.9073         | 11    |
| 0.1103     | 0.3531          | 0.6322          | 0.7215       | 0.6739   | 0.9030         | 12    |
| 0.1014     | 0.3814          | 0.6065          | 0.7511       | 0.6711   | 0.8964         | 13    |
| 0.0945     | 0.3668          | 0.6494          | 0.7384       | 0.6910   | 0.9049         | 14    |
| 0.0880     | 0.3704          | 0.6510          | 0.7321       | 0.6892   | 0.9038         | 15    |
| 0.0836     | 0.3762          | 0.6377          | 0.7426       | 0.6862   | 0.9001         | 16    |
| 0.0709     | 0.3765          | 0.6354          | 0.7426       | 0.6848   | 0.9020         | 17    |
| 0.0755     | 0.3791          | 0.6347          | 0.7405       | 0.6835   | 0.9022         | 18    |
| 0.0693     | 0.3774          | 0.6399          | 0.7384       | 0.6856   | 0.9030         | 19    |


### Framework versions

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

{"license": "mit", "tags": ["generated_from_keras_callback"], "base_model": "microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext", "model-index": [{"name": "Kikia26/FineTunePubMedBertWithTensorflowKeras2", "results": []}]}
transformers tf bert token-classification generated_from_keras_callback base_model:microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext license:mit autotrain_compatible endpoints_compatible region:us