---
language:
- id
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: sentiment-pt-pl50-2
  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-pl50-2

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: 0.3235
- Accuracy: 0.8747
- Precision: 0.8537
- Recall: 0.8388
- F1: 0.8457

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5384        | 1.0   | 122  | 0.4919          | 0.7368   | 0.6770    | 0.6538 | 0.6617 |
| 0.4212        | 2.0   | 244  | 0.4175          | 0.8246   | 0.7930    | 0.8359 | 0.8048 |
| 0.3413        | 3.0   | 366  | 0.3403          | 0.8446   | 0.8257    | 0.7851 | 0.8009 |
| 0.2888        | 4.0   | 488  | 0.3278          | 0.8471   | 0.8159    | 0.8143 | 0.8151 |
| 0.2577        | 5.0   | 610  | 0.3103          | 0.8596   | 0.8325    | 0.8257 | 0.8290 |
| 0.2495        | 6.0   | 732  | 0.3074          | 0.8672   | 0.8436    | 0.8310 | 0.8369 |
| 0.2391        | 7.0   | 854  | 0.3005          | 0.8672   | 0.8402    | 0.8385 | 0.8394 |
| 0.2177        | 8.0   | 976  | 0.2979          | 0.8697   | 0.8449    | 0.8378 | 0.8412 |
| 0.2102        | 9.0   | 1098 | 0.2961          | 0.8797   | 0.8549    | 0.8549 | 0.8549 |
| 0.2029        | 10.0  | 1220 | 0.3043          | 0.8697   | 0.8579    | 0.8178 | 0.8340 |
| 0.1829        | 11.0  | 1342 | 0.3059          | 0.8797   | 0.8572    | 0.8499 | 0.8534 |
| 0.184         | 12.0  | 1464 | 0.3002          | 0.8772   | 0.8609    | 0.8356 | 0.8467 |
| 0.1802        | 13.0  | 1586 | 0.2954          | 0.8897   | 0.8710    | 0.8595 | 0.8649 |
| 0.1684        | 14.0  | 1708 | 0.3008          | 0.8872   | 0.8634    | 0.8652 | 0.8643 |
| 0.1627        | 15.0  | 1830 | 0.3067          | 0.8872   | 0.8672    | 0.8577 | 0.8622 |
| 0.1581        | 16.0  | 1952 | 0.3107          | 0.8772   | 0.8514    | 0.8531 | 0.8522 |
| 0.1468        | 17.0  | 2074 | 0.3229          | 0.8772   | 0.8576    | 0.8406 | 0.8484 |
| 0.1433        | 18.0  | 2196 | 0.3247          | 0.8747   | 0.8537    | 0.8388 | 0.8457 |
| 0.1538        | 19.0  | 2318 | 0.3246          | 0.8747   | 0.8537    | 0.8388 | 0.8457 |
| 0.1412        | 20.0  | 2440 | 0.3235          | 0.8747   | 0.8537    | 0.8388 | 0.8457 |


### Framework versions

- Transformers 4.40.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1