|
--- |
|
license: mit |
|
base_model: indobenchmark/indobert-large-p1 |
|
tags: |
|
- generated_from_keras_callback |
|
model-index: |
|
- name: aditnnda/gacoan_reviewer |
|
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. --> |
|
|
|
# aditnnda/gacoan_reviewer |
|
|
|
This model is a fine-tuned version of [indobenchmark/indobert-large-p1](https://huggingface.co/indobenchmark/indobert-large-p1) on an unknown dataset. |
|
It achieves the following results on the evaluation set: |
|
- Train Loss: 0.0001 |
|
- Validation Loss: 0.4435 |
|
- Train Accuracy: 0.9386 |
|
- Epoch: 24 |
|
|
|
## 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': 3550, '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 Accuracy | Epoch | |
|
|:----------:|:---------------:|:--------------:|:-----:| |
|
| 0.2553 | 0.1732 | 0.9331 | 0 | |
|
| 0.0938 | 0.1571 | 0.9400 | 1 | |
|
| 0.0310 | 0.2345 | 0.9386 | 2 | |
|
| 0.0138 | 0.3288 | 0.9358 | 3 | |
|
| 0.0140 | 0.3345 | 0.9177 | 4 | |
|
| 0.0033 | 0.3502 | 0.9386 | 5 | |
|
| 0.0118 | 0.3387 | 0.9344 | 6 | |
|
| 0.0269 | 0.4487 | 0.9024 | 7 | |
|
| 0.0188 | 0.3228 | 0.9331 | 8 | |
|
| 0.0017 | 0.3581 | 0.9372 | 9 | |
|
| 0.0020 | 0.4125 | 0.9233 | 10 | |
|
| 0.0021 | 0.4143 | 0.9247 | 11 | |
|
| 0.0011 | 0.4353 | 0.9303 | 12 | |
|
| 0.0002 | 0.4285 | 0.9344 | 13 | |
|
| 0.0005 | 0.4350 | 0.9344 | 14 | |
|
| 0.0002 | 0.4340 | 0.9344 | 15 | |
|
| 0.0002 | 0.4026 | 0.9400 | 16 | |
|
| 0.0001 | 0.4123 | 0.9414 | 17 | |
|
| 0.0001 | 0.4228 | 0.9414 | 18 | |
|
| 0.0001 | 0.4294 | 0.9386 | 19 | |
|
| 0.0001 | 0.4385 | 0.9386 | 20 | |
|
| 0.0001 | 0.4411 | 0.9386 | 21 | |
|
| 0.0001 | 0.4423 | 0.9386 | 22 | |
|
| 0.0001 | 0.4431 | 0.9386 | 23 | |
|
| 0.0001 | 0.4435 | 0.9386 | 24 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.35.2 |
|
- TensorFlow 2.15.0 |
|
- Datasets 2.16.0 |
|
- Tokenizers 0.15.0 |
|
|
