---
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: sentiment-seq_bn-4
  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-seq_bn-4

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: 0.2917
- Accuracy: 0.8847
- Precision: 0.8648
- Recall: 0.8534
- F1: 0.8588

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5556        | 1.0   | 122  | 0.5148          | 0.7343   | 0.6852    | 0.6970 | 0.6899 |
| 0.476         | 2.0   | 244  | 0.4507          | 0.7870   | 0.7577    | 0.6918 | 0.7095 |
| 0.4238        | 3.0   | 366  | 0.4003          | 0.8195   | 0.7958    | 0.7473 | 0.7644 |
| 0.3735        | 4.0   | 488  | 0.3799          | 0.8371   | 0.8089    | 0.7872 | 0.7966 |
| 0.3548        | 5.0   | 610  | 0.3634          | 0.8471   | 0.8153    | 0.8168 | 0.8160 |
| 0.3213        | 6.0   | 732  | 0.3584          | 0.8421   | 0.8077    | 0.8208 | 0.8136 |
| 0.3085        | 7.0   | 854  | 0.3318          | 0.8622   | 0.8448    | 0.8125 | 0.8259 |
| 0.2981        | 8.0   | 976  | 0.3429          | 0.8672   | 0.8722    | 0.7985 | 0.8238 |
| 0.2788        | 9.0   | 1098 | 0.3304          | 0.8797   | 0.8795    | 0.8224 | 0.8439 |
| 0.259         | 10.0  | 1220 | 0.3076          | 0.8772   | 0.8535    | 0.8481 | 0.8507 |
| 0.2587        | 11.0  | 1342 | 0.3025          | 0.8747   | 0.8471    | 0.8538 | 0.8503 |
| 0.2391        | 12.0  | 1464 | 0.2990          | 0.8847   | 0.8697    | 0.8459 | 0.8565 |
| 0.2443        | 13.0  | 1586 | 0.2919          | 0.8797   | 0.8600    | 0.8449 | 0.8518 |
| 0.237         | 14.0  | 1708 | 0.3040          | 0.8772   | 0.8483    | 0.8631 | 0.8550 |
| 0.2176        | 15.0  | 1830 | 0.2937          | 0.8897   | 0.8649    | 0.8720 | 0.8683 |
| 0.2202        | 16.0  | 1952 | 0.2920          | 0.8822   | 0.8610    | 0.8517 | 0.8561 |
| 0.2203        | 17.0  | 2074 | 0.2923          | 0.8822   | 0.8585    | 0.8567 | 0.8575 |
| 0.2204        | 18.0  | 2196 | 0.2927          | 0.8847   | 0.8621    | 0.8584 | 0.8602 |
| 0.2124        | 19.0  | 2318 | 0.2920          | 0.8872   | 0.8672    | 0.8577 | 0.8622 |
| 0.2108        | 20.0  | 2440 | 0.2917          | 0.8847   | 0.8648    | 0.8534 | 0.8588 |


### Framework versions

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