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---
library_name: transformers
license: apache-2.0
base_model: LazarusNLP/NusaBERT-base
tags:
- generated_from_keras_callback
model-index:
- name: damand2061/pfsa-id-NusaBERT
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. -->
# damand2061/pfsa-id-NusaBERT
This model is a fine-tuned version of [LazarusNLP/NusaBERT-base](https://huggingface.co./LazarusNLP/NusaBERT-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1672
- Validation Loss: 0.2417
- Validation F1: 0.7748
- Validation Accuracy: 0.9257
- Epoch: 4
## 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: {'inner_optimizer': {'module': 'transformers.optimization_tf', 'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 1e-05, 'decay_steps': 10440, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.8999999761581421, 'beta_2': 0.9990000128746033, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}, 'registered_name': 'AdamWeightDecay'}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Validation F1 | Validation Accuracy | Epoch |
|:----------:|:---------------:|:-------------:|:-------------------:|:-----:|
| 0.4655 | 0.2722 | 0.6325 | 0.9143 | 0 |
| 0.2608 | 0.2483 | 0.7049 | 0.9203 | 1 |
| 0.2179 | 0.2391 | 0.7479 | 0.9242 | 2 |
| 0.1888 | 0.2393 | 0.7669 | 0.9245 | 3 |
| 0.1672 | 0.2417 | 0.7748 | 0.9257 | 4 |
### Framework versions
- Transformers 4.44.2
- TensorFlow 2.17.0
- Datasets 2.21.0
- Tokenizers 0.19.1
|