---
language:
- id
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: sentiment-base-3
  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-3

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: 0.7050
- Accuracy: 0.9023
- Precision: 0.8893
- Recall: 0.8708
- F1: 0.8793

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.3971        | 1.0   | 122  | 0.2704          | 0.8822   | 0.8715    | 0.8367 | 0.8513 |
| 0.214         | 2.0   | 244  | 0.2381          | 0.9198   | 0.9033    | 0.9033 | 0.9033 |
| 0.1301        | 3.0   | 366  | 0.3832          | 0.8797   | 0.8795    | 0.8224 | 0.8439 |
| 0.0904        | 4.0   | 488  | 0.3873          | 0.8947   | 0.8662    | 0.8955 | 0.8782 |
| 0.0587        | 5.0   | 610  | 0.4034          | 0.9148   | 0.9022    | 0.8897 | 0.8956 |
| 0.0496        | 6.0   | 732  | 0.5249          | 0.8922   | 0.8632    | 0.8938 | 0.8756 |
| 0.0362        | 7.0   | 854  | 0.5330          | 0.9073   | 0.8999    | 0.8719 | 0.8842 |
| 0.0223        | 8.0   | 976  | 0.6168          | 0.8972   | 0.8743    | 0.8798 | 0.8770 |
| 0.0191        | 9.0   | 1098 | 0.7104          | 0.8947   | 0.8870    | 0.8530 | 0.8675 |
| 0.0147        | 10.0  | 1220 | 0.6125          | 0.8972   | 0.8732    | 0.8823 | 0.8776 |
| 0.0121        | 11.0  | 1342 | 0.6883          | 0.9048   | 0.8790    | 0.9001 | 0.8883 |
| 0.0118        | 12.0  | 1464 | 0.6760          | 0.8972   | 0.8723    | 0.8848 | 0.8781 |
| 0.0034        | 13.0  | 1586 | 0.7163          | 0.9048   | 0.8851    | 0.8851 | 0.8851 |
| 0.0064        | 14.0  | 1708 | 0.7180          | 0.9073   | 0.8875    | 0.8894 | 0.8884 |
| 0.008         | 15.0  | 1830 | 0.6915          | 0.9048   | 0.8978    | 0.8676 | 0.8808 |
| 0.0052        | 16.0  | 1952 | 0.6778          | 0.9073   | 0.8875    | 0.8894 | 0.8884 |
| 0.0066        | 17.0  | 2074 | 0.6993          | 0.9073   | 0.8875    | 0.8894 | 0.8884 |
| 0.0053        | 18.0  | 2196 | 0.6966          | 0.9048   | 0.8956    | 0.8701 | 0.8814 |
| 0.0022        | 19.0  | 2318 | 0.7112          | 0.8997   | 0.8852    | 0.8691 | 0.8765 |
| 0.0042        | 20.0  | 2440 | 0.7050          | 0.9023   | 0.8893    | 0.8708 | 0.8793 |


### Framework versions

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