---
language:
- id
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: sentiment-unipelt-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-unipelt-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: 0.2811
- Accuracy: 0.9023
- Precision: 0.8773
- Recall: 0.8933
- F1: 0.8846

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5459        | 1.0   | 122  | 0.4639          | 0.7469   | 0.6922    | 0.6459 | 0.6573 |
| 0.4335        | 2.0   | 244  | 0.4108          | 0.7845   | 0.7552    | 0.7975 | 0.7634 |
| 0.3375        | 3.0   | 366  | 0.3283          | 0.8596   | 0.8347    | 0.8207 | 0.8272 |
| 0.2801        | 4.0   | 488  | 0.3202          | 0.8596   | 0.8278    | 0.8432 | 0.8347 |
| 0.2572        | 5.0   | 610  | 0.3109          | 0.8747   | 0.8438    | 0.8713 | 0.8550 |
| 0.2339        | 6.0   | 732  | 0.3074          | 0.8672   | 0.8353    | 0.8660 | 0.8473 |
| 0.2249        | 7.0   | 854  | 0.2915          | 0.8672   | 0.8353    | 0.8660 | 0.8473 |
| 0.193         | 8.0   | 976  | 0.2540          | 0.8972   | 0.8781    | 0.8723 | 0.8751 |
| 0.1899        | 9.0   | 1098 | 0.2636          | 0.8822   | 0.8526    | 0.8767 | 0.8628 |
| 0.1801        | 10.0  | 1220 | 0.2371          | 0.9073   | 0.8840    | 0.8969 | 0.8900 |
| 0.157         | 11.0  | 1342 | 0.2567          | 0.8997   | 0.8733    | 0.8941 | 0.8825 |
| 0.1553        | 12.0  | 1464 | 0.2593          | 0.8972   | 0.8708    | 0.8898 | 0.8793 |
| 0.1381        | 13.0  | 1586 | 0.2490          | 0.9173   | 0.9010    | 0.8990 | 0.9000 |
| 0.1476        | 14.0  | 1708 | 0.2701          | 0.8997   | 0.8740    | 0.8916 | 0.8819 |
| 0.1447        | 15.0  | 1830 | 0.2611          | 0.9123   | 0.8899    | 0.9029 | 0.8960 |
| 0.1336        | 16.0  | 1952 | 0.3100          | 0.8997   | 0.8718    | 0.9016 | 0.8840 |
| 0.1192        | 17.0  | 2074 | 0.2935          | 0.8972   | 0.8696    | 0.8948 | 0.8803 |
| 0.1247        | 18.0  | 2196 | 0.2869          | 0.9023   | 0.8765    | 0.8958 | 0.8851 |
| 0.117         | 19.0  | 2318 | 0.2761          | 0.9023   | 0.8773    | 0.8933 | 0.8846 |
| 0.1092        | 20.0  | 2440 | 0.2811          | 0.9023   | 0.8773    | 0.8933 | 0.8846 |


### Framework versions

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