---
tags:
- generated_from_keras_callback
model-index:
- name: aadhistii/tsel-finetune-indobert-base-p1-2k-formal-v2
  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. -->

# aadhistii/tsel-finetune-indobert-base-p1-2k-formal-v2

This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1116
- Validation Loss: 0.8423
- Train Precision: 0.7473
- Train Recall: 0.7473
- Train F1: 0.7473
- Epoch: 3

## 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': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 940, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32

### Training results

| Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Epoch |
|:----------:|:---------------:|:---------------:|:------------:|:--------:|:-----:|
| 0.7881     | 0.5850          | 0.7660          | 0.7660       | 0.7660   | 0     |
| 0.4468     | 0.6147          | 0.7447          | 0.7447       | 0.7447   | 1     |
| 0.2292     | 0.7075          | 0.7633          | 0.7633       | 0.7633   | 2     |
| 0.1116     | 0.8423          | 0.7473          | 0.7473       | 0.7473   | 3     |


### Framework versions

- Transformers 4.42.3
- TensorFlow 2.15.0
- Datasets 2.20.0
- Tokenizers 0.19.1