---
language:
- id
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: sentiment-base-0
  results: []
---

<!-- 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. -->

# sentiment-base-0

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:
- Loss: 0.8105
- Accuracy: 0.9023
- Precision: 0.8828
- Recall: 0.8808
- F1: 0.8818

## 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: 5e-05
- train_batch_size: 30
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.4267        | 1.0   | 122  | 0.3586          | 0.8797   | 0.8892    | 0.8149 | 0.8409 |
| 0.2234        | 2.0   | 244  | 0.3668          | 0.8697   | 0.8395    | 0.8853 | 0.8539 |
| 0.126         | 3.0   | 366  | 0.4554          | 0.8922   | 0.8632    | 0.8938 | 0.8756 |
| 0.0886        | 4.0   | 488  | 0.4441          | 0.9073   | 0.8957    | 0.8769 | 0.8855 |
| 0.0611        | 5.0   | 610  | 0.4923          | 0.9048   | 0.8881    | 0.8801 | 0.8839 |
| 0.0366        | 6.0   | 732  | 0.6796          | 0.8997   | 0.8748    | 0.8891 | 0.8814 |
| 0.0358        | 7.0   | 854  | 0.5746          | 0.9048   | 0.8935    | 0.8726 | 0.8821 |
| 0.0272        | 8.0   | 976  | 0.5953          | 0.8947   | 0.8718    | 0.8755 | 0.8737 |
| 0.0231        | 9.0   | 1098 | 0.6506          | 0.8997   | 0.8891    | 0.8641 | 0.8752 |
| 0.0141        | 10.0  | 1220 | 0.6854          | 0.9023   | 0.8814    | 0.8833 | 0.8824 |
| 0.023         | 11.0  | 1342 | 0.7218          | 0.9023   | 0.8814    | 0.8833 | 0.8824 |
| 0.0067        | 12.0  | 1464 | 0.7695          | 0.9023   | 0.8814    | 0.8833 | 0.8824 |
| 0.0064        | 13.0  | 1586 | 0.9004          | 0.8797   | 0.8496    | 0.8749 | 0.8602 |
| 0.0103        | 14.0  | 1708 | 0.7978          | 0.9023   | 0.8792    | 0.8883 | 0.8835 |
| 0.0072        | 15.0  | 1830 | 0.8251          | 0.8997   | 0.8791    | 0.8791 | 0.8791 |
| 0.0054        | 16.0  | 1952 | 0.7715          | 0.9023   | 0.8814    | 0.8833 | 0.8824 |
| 0.0038        | 17.0  | 2074 | 0.7821          | 0.9073   | 0.8920    | 0.8819 | 0.8867 |
| 0.0021        | 18.0  | 2196 | 0.8211          | 0.8972   | 0.8754    | 0.8773 | 0.8764 |
| 0.0022        | 19.0  | 2318 | 0.8162          | 0.8997   | 0.8791    | 0.8791 | 0.8791 |
| 0.0027        | 20.0  | 2440 | 0.8105          | 0.9023   | 0.8828    | 0.8808 | 0.8818 |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.15.2