---
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: wdevinsp/indobert-base-uncased-finetuned-digestive-qna
  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. -->

# wdevinsp/indobert-base-uncased-finetuned-digestive-qna

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: 0.0391
- Train End Logits Accuracy: 0.9814
- Train Start Logits Accuracy: 0.9814
- Epoch: 37

## 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': 3700, '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 | Train End Logits Accuracy | Train Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:-----:|
| 3.1872     | 0.3564                    | 0.3733                      | 0     |
| 2.1076     | 0.4189                    | 0.4088                      | 1     |
| 1.6782     | 0.4443                    | 0.5507                      | 2     |
| 1.1711     | 0.5507                    | 0.7111                      | 3     |
| 0.6231     | 0.7720                    | 0.8429                      | 4     |
| 0.4296     | 0.8463                    | 0.8902                      | 5     |
| 0.2544     | 0.9189                    | 0.9409                      | 6     |
| 0.1853     | 0.9358                    | 0.9510                      | 7     |
| 0.1411     | 0.9544                    | 0.9578                      | 8     |
| 0.1215     | 0.9493                    | 0.9662                      | 9     |
| 0.1141     | 0.9645                    | 0.9628                      | 10    |
| 0.1195     | 0.9561                    | 0.9696                      | 11    |
| 0.0851     | 0.9662                    | 0.9645                      | 12    |
| 0.1255     | 0.9527                    | 0.9493                      | 13    |
| 0.0897     | 0.9595                    | 0.9730                      | 14    |
| 0.0860     | 0.9578                    | 0.9696                      | 15    |
| 0.0710     | 0.9595                    | 0.9713                      | 16    |
| 0.0669     | 0.9628                    | 0.9713                      | 17    |
| 0.0634     | 0.9797                    | 0.9730                      | 18    |
| 0.0765     | 0.9662                    | 0.9814                      | 19    |
| 0.0732     | 0.9679                    | 0.9730                      | 20    |
| 0.0586     | 0.9696                    | 0.9713                      | 21    |
| 0.0572     | 0.9679                    | 0.9764                      | 22    |
| 0.0518     | 0.9747                    | 0.9764                      | 23    |
| 0.0500     | 0.9713                    | 0.9764                      | 24    |
| 0.0464     | 0.9696                    | 0.9713                      | 25    |
| 0.0470     | 0.9814                    | 0.9730                      | 26    |
| 0.0575     | 0.9797                    | 0.9764                      | 27    |
| 0.0660     | 0.9679                    | 0.9696                      | 28    |
| 0.0576     | 0.9713                    | 0.9696                      | 29    |
| 0.0515     | 0.9696                    | 0.9747                      | 30    |
| 0.0547     | 0.9780                    | 0.9696                      | 31    |
| 0.0430     | 0.9730                    | 0.9780                      | 32    |
| 0.0408     | 0.9814                    | 0.9747                      | 33    |
| 0.0475     | 0.9747                    | 0.9747                      | 34    |
| 0.0536     | 0.9764                    | 0.9797                      | 35    |
| 0.0494     | 0.9696                    | 0.9730                      | 36    |
| 0.0391     | 0.9814                    | 0.9814                      | 37    |


### Framework versions

- Transformers 4.41.2
- TensorFlow 2.15.0
- Datasets 2.20.0
- Tokenizers 0.19.1