---
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: 0.7536
- Accuracy: 0.9048
- Precision: 0.8798
- Recall: 0.8976
- F1: 0.8878

## 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: 1
- 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.4355        | 1.0   | 122  | 0.3243          | 0.8697   | 0.8538    | 0.8228 | 0.8359 |
| 0.2295        | 2.0   | 244  | 0.3047          | 0.8897   | 0.8625    | 0.8795 | 0.8701 |
| 0.1337        | 3.0   | 366  | 0.3747          | 0.8997   | 0.8778    | 0.8816 | 0.8797 |
| 0.1038        | 4.0   | 488  | 0.4188          | 0.8822   | 0.8518    | 0.8867 | 0.8651 |
| 0.072         | 5.0   | 610  | 0.6271          | 0.8872   | 0.8672    | 0.8577 | 0.8622 |
| 0.0462        | 6.0   | 732  | 0.6129          | 0.8897   | 0.8632    | 0.8770 | 0.8695 |
| 0.0459        | 7.0   | 854  | 0.5891          | 0.8897   | 0.8710    | 0.8595 | 0.8649 |
| 0.0391        | 8.0   | 976  | 0.5973          | 0.8872   | 0.8587    | 0.8802 | 0.8681 |
| 0.0307        | 9.0   | 1098 | 0.7087          | 0.8747   | 0.8441    | 0.8863 | 0.8585 |
| 0.0199        | 10.0  | 1220 | 0.7264          | 0.8972   | 0.8869    | 0.8598 | 0.8717 |
| 0.0105        | 11.0  | 1342 | 0.6738          | 0.8972   | 0.8767    | 0.8748 | 0.8757 |
| 0.0131        | 12.0  | 1464 | 0.7488          | 0.8997   | 0.8733    | 0.8941 | 0.8825 |
| 0.0102        | 13.0  | 1586 | 0.7155          | 0.8972   | 0.8708    | 0.8898 | 0.8793 |
| 0.0061        | 14.0  | 1708 | 0.7196          | 0.9073   | 0.8851    | 0.8944 | 0.8895 |
| 0.0138        | 15.0  | 1830 | 0.7618          | 0.9023   | 0.8773    | 0.8933 | 0.8846 |
| 0.0075        | 16.0  | 1952 | 0.7253          | 0.9048   | 0.8806    | 0.8951 | 0.8873 |
| 0.0063        | 17.0  | 2074 | 0.7560          | 0.9023   | 0.8782    | 0.8908 | 0.8841 |
| 0.0066        | 18.0  | 2196 | 0.7483          | 0.9023   | 0.8758    | 0.8983 | 0.8857 |
| 0.0023        | 19.0  | 2318 | 0.7535          | 0.9023   | 0.8773    | 0.8933 | 0.8846 |
| 0.0021        | 20.0  | 2440 | 0.7536          | 0.9048   | 0.8798    | 0.8976 | 0.8878 |


### Framework versions

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