---
language:
- id
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: sentiment-pt-pl20-0
  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-pt-pl20-0

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: 0.2809
- Accuracy: 0.9023
- Precision: 0.8758
- Recall: 0.8983
- F1: 0.8857

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5417        | 1.0   | 122  | 0.4692          | 0.7368   | 0.6763    | 0.6438 | 0.6531 |
| 0.4301        | 2.0   | 244  | 0.4378          | 0.7769   | 0.7547    | 0.8022 | 0.7593 |
| 0.3347        | 3.0   | 366  | 0.3451          | 0.8446   | 0.8158    | 0.8026 | 0.8086 |
| 0.2954        | 4.0   | 488  | 0.3337          | 0.8647   | 0.8377    | 0.8342 | 0.8359 |
| 0.2632        | 5.0   | 610  | 0.3356          | 0.8571   | 0.8248    | 0.8414 | 0.8321 |
| 0.2492        | 6.0   | 732  | 0.3261          | 0.8446   | 0.8110    | 0.8451 | 0.8231 |
| 0.227         | 7.0   | 854  | 0.2978          | 0.8797   | 0.8496    | 0.8749 | 0.8602 |
| 0.2189        | 8.0   | 976  | 0.2742          | 0.8947   | 0.8789    | 0.8630 | 0.8704 |
| 0.2068        | 9.0   | 1098 | 0.2875          | 0.8922   | 0.8673    | 0.8763 | 0.8716 |
| 0.1935        | 10.0  | 1220 | 0.2693          | 0.9073   | 0.8904    | 0.8844 | 0.8873 |
| 0.1729        | 11.0  | 1342 | 0.2715          | 0.9073   | 0.8840    | 0.8969 | 0.8900 |
| 0.1639        | 12.0  | 1464 | 0.2755          | 0.8997   | 0.8733    | 0.8941 | 0.8825 |
| 0.1564        | 13.0  | 1586 | 0.2662          | 0.9023   | 0.8828    | 0.8808 | 0.8818 |
| 0.1495        | 14.0  | 1708 | 0.2973          | 0.8997   | 0.8722    | 0.8991 | 0.8835 |
| 0.1487        | 15.0  | 1830 | 0.2732          | 0.9098   | 0.8865    | 0.9012 | 0.8932 |
| 0.141         | 16.0  | 1952 | 0.2842          | 0.9048   | 0.8784    | 0.9026 | 0.8888 |
| 0.1276        | 17.0  | 2074 | 0.2794          | 0.9048   | 0.8798    | 0.8976 | 0.8878 |
| 0.1383        | 18.0  | 2196 | 0.2787          | 0.9073   | 0.8823    | 0.9019 | 0.8910 |
| 0.1371        | 19.0  | 2318 | 0.2780          | 0.9023   | 0.8758    | 0.8983 | 0.8857 |
| 0.1248        | 20.0  | 2440 | 0.2809          | 0.9023   | 0.8758    | 0.8983 | 0.8857 |


### Framework versions

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