transformers tf bert token-classification generated_from_keras_callback dataset:jnlpba/jnlpba base_model:google-bert/bert-base-cased base_model:finetune:google-bert/bert-base-cased license:apache-2.0 autotrain_compatible endpoints_compatible region:us
---
base_model: bert-base-cased
datasets:
- jnlpba/jnlpba
library_name: transformers
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: apriadiazriel/bert-cased-jnlpba
  results: []
---

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# apriadiazriel/bert-cased-jnlpba

This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the [JNLPBA](https://huggingface.co/datasets/jnlpba/jnlpba) dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0851
- Validation Loss: 0.2221
- Precision: 0.6744
- Recall: 0.7808
- F1: 0.7237
- Accuracy: 0.9371
- Epoch: 5

## 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': 2e-05, 'decay_steps': 5795, '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 | Precision | Recall | F1 | Accuracy | Epoch |
|:----------:|:---------------:|:---------------:|:------------:|:--------:|:--------------:|:-----:|
| 0.2424     | 0.1998          | 0.6507          | 0.7606       | 0.7014   | 0.9322         | 0     |
| 0.1426     | 0.1975          | 0.6613          | 0.7832       | 0.7171   | 0.9364         | 1     |
| 0.1166     | 0.2051          | 0.6527          | 0.7847       | 0.7127   | 0.9353         | 2     |
| 0.0984     | 0.2108          | 0.6750          | 0.7811       | 0.7242   | 0.9378         | 3     |
| 0.0851     | 0.2221          | 0.6744          | 0.7808       | 0.7237   | 0.9371         | 4     |


### Framework versions

- Transformers 4.48.3
- TensorFlow 2.18.0
- Datasets 3.3.2
- Tokenizers 0.21.0