metadata
license: mit
base_model: indobenchmark/indobert-base-p1
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: indobert-finetuned-aspect-happiness-index
results: []
pipeline_tag: text-classification
language:
- id
widget:
- text: Aku senang kuliah di Undip
example_title: Aspect Detection
indobert-finetuned-aspect-happiness-index
This model is a fine-tuned version of indobenchmark/indobert-base-p1 on an own private dataset. It achieves the following results on the evaluation set:
- Loss: 0.1476
- Accuracy: 0.9732
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:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 270 | 0.1291 | 0.9648 |
| 0.301 | 2.0 | 540 | 0.1708 | 0.9593 |
| 0.301 | 3.0 | 810 | 0.1350 | 0.9685 |
| 0.0655 | 4.0 | 1080 | 0.1734 | 0.9648 |
| 0.0655 | 5.0 | 1350 | 0.1323 | 0.9713 |
| 0.023 | 6.0 | 1620 | 0.1551 | 0.9676 |
| 0.023 | 7.0 | 1890 | 0.1558 | 0.9704 |
| 0.0137 | 8.0 | 2160 | 0.1531 | 0.9732 |
| 0.0137 | 9.0 | 2430 | 0.1493 | 0.9722 |
| 0.0056 | 10.0 | 2700 | 0.1476 | 0.9732 |
Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3