---
library_name: transformers
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: damand2061/pfsa-id-indobert-lem
  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-indobert-lem

This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co./indolem/indobert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1353
- Validation Loss: 0.2440
- Validation F1: 0.8119
- Validation Accuracy: 0.9295
- 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: {'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': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16

### Training results

| Train Loss | Validation Loss | Validation F1 | Validation Accuracy | Epoch |
|:----------:|:---------------:|:-------------:|:-------------------:|:-----:|
| 0.4338     | 0.2589          | 0.6515        | 0.9170              | 0     |
| 0.2529     | 0.2283          | 0.7705        | 0.9276              | 1     |
| 0.2046     | 0.2272          | 0.7979        | 0.9293              | 2     |
| 0.1622     | 0.2312          | 0.8089        | 0.9303              | 3     |
| 0.1353     | 0.2440          | 0.8119        | 0.9295              | 4     |


### Framework versions

- Transformers 4.44.2
- TensorFlow 2.17.0
- Datasets 2.21.0
- Tokenizers 0.19.1