---
language:
- id
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: sentiment-base-1
  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-1

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: 0.7908
- Accuracy: 0.9023
- Precision: 0.8875
- Recall: 0.8733
- F1: 0.8799

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.3889        | 1.0   | 122  | 0.4200          | 0.8045   | 0.8255    | 0.6867 | 0.7110 |
| 0.2335        | 2.0   | 244  | 0.3136          | 0.8922   | 0.8644    | 0.8863 | 0.8739 |
| 0.1411        | 3.0   | 366  | 0.3569          | 0.8972   | 0.8781    | 0.8723 | 0.8751 |
| 0.1078        | 4.0   | 488  | 0.3537          | 0.9148   | 0.8923    | 0.9072 | 0.8992 |
| 0.0822        | 5.0   | 610  | 0.5069          | 0.8797   | 0.8795    | 0.8224 | 0.8439 |
| 0.0529        | 6.0   | 732  | 0.4262          | 0.9073   | 0.8862    | 0.8919 | 0.8890 |
| 0.0365        | 7.0   | 854  | 0.5586          | 0.8972   | 0.8743    | 0.8798 | 0.8770 |
| 0.033         | 8.0   | 976  | 0.5012          | 0.8947   | 0.8870    | 0.8530 | 0.8675 |
| 0.0248        | 9.0   | 1098 | 0.5833          | 0.8922   | 0.8873    | 0.8462 | 0.8631 |
| 0.0123        | 10.0  | 1220 | 0.6611          | 0.9023   | 0.8858    | 0.8758 | 0.8806 |
| 0.0088        | 11.0  | 1342 | 0.6936          | 0.8947   | 0.8847    | 0.8555 | 0.8682 |
| 0.0074        | 12.0  | 1464 | 0.6790          | 0.9023   | 0.8858    | 0.8758 | 0.8806 |
| 0.0141        | 13.0  | 1586 | 0.6981          | 0.8972   | 0.8830    | 0.8648 | 0.8731 |
| 0.0034        | 14.0  | 1708 | 0.7145          | 0.8972   | 0.8781    | 0.8723 | 0.8751 |
| 0.0059        | 15.0  | 1830 | 0.7304          | 0.8997   | 0.8871    | 0.8666 | 0.8759 |
| 0.0056        | 16.0  | 1952 | 0.7518          | 0.8997   | 0.8778    | 0.8816 | 0.8797 |
| 0.0039        | 17.0  | 2074 | 0.7390          | 0.9023   | 0.8893    | 0.8708 | 0.8793 |
| 0.004         | 18.0  | 2196 | 0.7641          | 0.9023   | 0.8875    | 0.8733 | 0.8799 |
| 0.007         | 19.0  | 2318 | 0.7848          | 0.9023   | 0.8875    | 0.8733 | 0.8799 |
| 0.0042        | 20.0  | 2440 | 0.7908          | 0.9023   | 0.8875    | 0.8733 | 0.8799 |


### Framework versions

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