---
language:
- id
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: sentiment-ia3
  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-ia3

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: 0.4451
- Accuracy: 0.7870
- Precision: 0.7443
- Recall: 0.7243
- F1: 0.7325

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5653        | 1.0   | 122  | 0.5246          | 0.7218   | 0.6540    | 0.6257 | 0.6333 |
| 0.5167        | 2.0   | 244  | 0.5215          | 0.7293   | 0.6805    | 0.6935 | 0.6855 |
| 0.4984        | 3.0   | 366  | 0.4975          | 0.7444   | 0.6916    | 0.6916 | 0.6916 |
| 0.4765        | 4.0   | 488  | 0.4854          | 0.7419   | 0.6837    | 0.6523 | 0.6619 |
| 0.4797        | 5.0   | 610  | 0.4852          | 0.7719   | 0.7270    | 0.7386 | 0.7320 |
| 0.4668        | 6.0   | 732  | 0.4738          | 0.7669   | 0.7190    | 0.7201 | 0.7195 |
| 0.4622        | 7.0   | 854  | 0.4769          | 0.7719   | 0.7261    | 0.7336 | 0.7295 |
| 0.4621        | 8.0   | 976  | 0.4625          | 0.7494   | 0.6949    | 0.6577 | 0.6686 |
| 0.4561        | 9.0   | 1098 | 0.4609          | 0.7769   | 0.7311    | 0.7122 | 0.7199 |
| 0.4519        | 10.0  | 1220 | 0.4608          | 0.7669   | 0.7252    | 0.6676 | 0.6822 |
| 0.4413        | 11.0  | 1342 | 0.4544          | 0.7694   | 0.7215    | 0.6994 | 0.7080 |
| 0.4449        | 12.0  | 1464 | 0.4569          | 0.7845   | 0.7401    | 0.7425 | 0.7413 |
| 0.4506        | 13.0  | 1586 | 0.4527          | 0.7644   | 0.7197    | 0.6683 | 0.6821 |
| 0.4446        | 14.0  | 1708 | 0.4488          | 0.7794   | 0.7379    | 0.6989 | 0.7121 |
| 0.4426        | 15.0  | 1830 | 0.4491          | 0.7870   | 0.7436    | 0.7293 | 0.7355 |
| 0.4409        | 16.0  | 1952 | 0.4465          | 0.7719   | 0.7257    | 0.6961 | 0.7068 |
| 0.4348        | 17.0  | 2074 | 0.4474          | 0.7870   | 0.7436    | 0.7293 | 0.7355 |
| 0.4478        | 18.0  | 2196 | 0.4460          | 0.7845   | 0.7408    | 0.7225 | 0.7302 |
| 0.4382        | 19.0  | 2318 | 0.4448          | 0.7870   | 0.7447    | 0.7218 | 0.7310 |
| 0.4313        | 20.0  | 2440 | 0.4451          | 0.7870   | 0.7443    | 0.7243 | 0.7325 |


### Framework versions

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