---
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: 0.3013
- Accuracy: 0.8922
- Precision: 0.8694
- Recall: 0.8712
- F1: 0.8703

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5538        | 1.0   | 122  | 0.4789          | 0.7193   | 0.6517    | 0.6289 | 0.6359 |
| 0.4356        | 2.0   | 244  | 0.4088          | 0.7845   | 0.7518    | 0.7900 | 0.7610 |
| 0.3417        | 3.0   | 366  | 0.3369          | 0.8571   | 0.8365    | 0.8089 | 0.8206 |
| 0.2904        | 4.0   | 488  | 0.3267          | 0.8672   | 0.8423    | 0.8335 | 0.8377 |
| 0.263         | 5.0   | 610  | 0.3210          | 0.8672   | 0.8356    | 0.8585 | 0.8453 |
| 0.2463        | 6.0   | 732  | 0.3551          | 0.8421   | 0.8093    | 0.8483 | 0.8220 |
| 0.2303        | 7.0   | 854  | 0.3028          | 0.8722   | 0.8409    | 0.8696 | 0.8524 |
| 0.2208        | 8.0   | 976  | 0.2673          | 0.8897   | 0.8695    | 0.8620 | 0.8656 |
| 0.1994        | 9.0   | 1098 | 0.2715          | 0.8897   | 0.8649    | 0.8720 | 0.8683 |
| 0.1836        | 10.0  | 1220 | 0.2595          | 0.9098   | 0.8999    | 0.8787 | 0.8883 |
| 0.1706        | 11.0  | 1342 | 0.2833          | 0.8922   | 0.8650    | 0.8838 | 0.8734 |
| 0.1623        | 12.0  | 1464 | 0.2993          | 0.8872   | 0.8599    | 0.8752 | 0.8669 |
| 0.1478        | 13.0  | 1586 | 0.2864          | 0.8972   | 0.8849    | 0.8623 | 0.8724 |
| 0.1467        | 14.0  | 1708 | 0.2805          | 0.8972   | 0.8754    | 0.8773 | 0.8764 |
| 0.132         | 15.0  | 1830 | 0.2869          | 0.8997   | 0.8748    | 0.8891 | 0.8814 |
| 0.125         | 16.0  | 1952 | 0.3052          | 0.8972   | 0.8723    | 0.8848 | 0.8781 |
| 0.1183        | 17.0  | 2074 | 0.2968          | 0.8897   | 0.8649    | 0.8720 | 0.8683 |
| 0.1185        | 18.0  | 2196 | 0.3033          | 0.8922   | 0.8673    | 0.8763 | 0.8716 |
| 0.1132        | 19.0  | 2318 | 0.3063          | 0.8897   | 0.8640    | 0.8745 | 0.8689 |
| 0.1195        | 20.0  | 2440 | 0.3013          | 0.8922   | 0.8694    | 0.8712 | 0.8703 |


### Framework versions

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