---
language:
- id
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: sentiment-unipelt-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-unipelt-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: 0.3288
- Accuracy: 0.8872
- Precision: 0.8606
- Recall: 0.8727
- F1: 0.8662

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5528        | 1.0   | 122  | 0.5051          | 0.7168   | 0.6521    | 0.6396 | 0.6444 |
| 0.4431        | 2.0   | 244  | 0.4333          | 0.7970   | 0.7711    | 0.8189 | 0.7789 |
| 0.3491        | 3.0   | 366  | 0.3269          | 0.8672   | 0.8579    | 0.8110 | 0.8293 |
| 0.2886        | 4.0   | 488  | 0.2995          | 0.8722   | 0.8498    | 0.8371 | 0.8430 |
| 0.2671        | 5.0   | 610  | 0.2967          | 0.8697   | 0.8428    | 0.8428 | 0.8428 |
| 0.2481        | 6.0   | 732  | 0.3346          | 0.8546   | 0.8230    | 0.8647 | 0.8365 |
| 0.2339        | 7.0   | 854  | 0.2965          | 0.8672   | 0.8372    | 0.8485 | 0.8425 |
| 0.217         | 8.0   | 976  | 0.2929          | 0.8772   | 0.8576    | 0.8406 | 0.8484 |
| 0.1984        | 9.0   | 1098 | 0.2778          | 0.8797   | 0.8572    | 0.8499 | 0.8534 |
| 0.1937        | 10.0  | 1220 | 0.2905          | 0.8772   | 0.8628    | 0.8331 | 0.8458 |
| 0.1734        | 11.0  | 1342 | 0.2919          | 0.8872   | 0.8624    | 0.8677 | 0.8650 |
| 0.1561        | 12.0  | 1464 | 0.3126          | 0.8797   | 0.8539    | 0.8574 | 0.8556 |
| 0.1579        | 13.0  | 1586 | 0.3165          | 0.8822   | 0.8610    | 0.8517 | 0.8561 |
| 0.1459        | 14.0  | 1708 | 0.3108          | 0.8972   | 0.8715    | 0.8873 | 0.8787 |
| 0.1399        | 15.0  | 1830 | 0.3192          | 0.8922   | 0.8657    | 0.8813 | 0.8728 |
| 0.1377        | 16.0  | 1952 | 0.3200          | 0.8847   | 0.8567    | 0.8734 | 0.8642 |
| 0.1259        | 17.0  | 2074 | 0.3303          | 0.8847   | 0.8589    | 0.8659 | 0.8623 |
| 0.1293        | 18.0  | 2196 | 0.3265          | 0.8922   | 0.8657    | 0.8813 | 0.8728 |
| 0.1187        | 19.0  | 2318 | 0.3305          | 0.8847   | 0.8589    | 0.8659 | 0.8623 |
| 0.1302        | 20.0  | 2440 | 0.3288          | 0.8872   | 0.8606    | 0.8727 | 0.8662 |


### Framework versions

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