---
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: 0.8336
- Accuracy: 0.8972
- Precision: 0.8708
- Recall: 0.8898
- 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.3942        | 1.0   | 122  | 0.3128          | 0.8797   | 0.8858    | 0.8174 | 0.8419 |
| 0.2168        | 2.0   | 244  | 0.3044          | 0.8897   | 0.8659    | 0.8695 | 0.8676 |
| 0.1372        | 3.0   | 366  | 0.5318          | 0.8897   | 0.8852    | 0.8420 | 0.8595 |
| 0.0957        | 4.0   | 488  | 0.4765          | 0.8947   | 0.8676    | 0.8880 | 0.8766 |
| 0.0674        | 5.0   | 610  | 0.5523          | 0.8872   | 0.8577    | 0.9027 | 0.8729 |
| 0.0535        | 6.0   | 732  | 0.5159          | 0.9073   | 0.8888    | 0.8869 | 0.8879 |
| 0.027         | 7.0   | 854  | 0.5941          | 0.8872   | 0.8634    | 0.8652 | 0.8643 |
| 0.0223        | 8.0   | 976  | 0.7166          | 0.8797   | 0.8549    | 0.8549 | 0.8549 |
| 0.0145        | 9.0   | 1098 | 0.7023          | 0.9023   | 0.8802    | 0.8858 | 0.8830 |
| 0.0106        | 10.0  | 1220 | 0.6993          | 0.9048   | 0.8881    | 0.8801 | 0.8839 |
| 0.0093        | 11.0  | 1342 | 0.8274          | 0.8947   | 0.8789    | 0.8630 | 0.8704 |
| 0.0086        | 12.0  | 1464 | 0.7972          | 0.8972   | 0.8796    | 0.8698 | 0.8745 |
| 0.0106        | 13.0  | 1586 | 0.7592          | 0.8972   | 0.8715    | 0.8873 | 0.8787 |
| 0.0072        | 14.0  | 1708 | 0.7834          | 0.8997   | 0.8748    | 0.8891 | 0.8814 |
| 0.0098        | 15.0  | 1830 | 0.8049          | 0.8997   | 0.8767    | 0.8841 | 0.8803 |
| 0.0058        | 16.0  | 1952 | 0.7671          | 0.8997   | 0.8767    | 0.8841 | 0.8803 |
| 0.0035        | 17.0  | 2074 | 0.8085          | 0.9023   | 0.8758    | 0.8983 | 0.8857 |
| 0.0052        | 18.0  | 2196 | 0.7721          | 0.8997   | 0.8757    | 0.8866 | 0.8808 |
| 0.0028        | 19.0  | 2318 | 0.8359          | 0.8972   | 0.8708    | 0.8898 | 0.8793 |
| 0.0033        | 20.0  | 2440 | 0.8336          | 0.8972   | 0.8708    | 0.8898 | 0.8793 |


### Framework versions

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