---
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
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# indobert-finetuned-aspect-happiness-index

This model is a fine-tuned version of [indobenchmark/indobert-base-p1](https://huggingface.co/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