---
language:
- id
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: sentiment-seq_bn-1
  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-1

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: 0.3078
- Accuracy: 0.8797
- Precision: 0.8572
- Recall: 0.8499
- F1: 0.8534

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5593        | 1.0   | 122  | 0.5129          | 0.7318   | 0.6697    | 0.6453 | 0.6532 |
| 0.481         | 2.0   | 244  | 0.4831          | 0.7343   | 0.6993    | 0.7295 | 0.7054 |
| 0.4234        | 3.0   | 366  | 0.3974          | 0.8221   | 0.7926    | 0.7616 | 0.7740 |
| 0.3701        | 4.0   | 488  | 0.3780          | 0.8396   | 0.8128    | 0.7890 | 0.7992 |
| 0.3499        | 5.0   | 610  | 0.3612          | 0.8471   | 0.8135    | 0.8268 | 0.8195 |
| 0.3165        | 6.0   | 732  | 0.3760          | 0.8271   | 0.7953    | 0.8377 | 0.8072 |
| 0.2968        | 7.0   | 854  | 0.3342          | 0.8697   | 0.8438    | 0.8403 | 0.8420 |
| 0.2812        | 8.0   | 976  | 0.3311          | 0.8672   | 0.8463    | 0.8260 | 0.8351 |
| 0.2682        | 9.0   | 1098 | 0.3269          | 0.8722   | 0.8463    | 0.8446 | 0.8454 |
| 0.2596        | 10.0  | 1220 | 0.3145          | 0.8797   | 0.8560    | 0.8524 | 0.8541 |
| 0.2464        | 11.0  | 1342 | 0.3138          | 0.8697   | 0.8503    | 0.8278 | 0.8377 |
| 0.2415        | 12.0  | 1464 | 0.3126          | 0.8847   | 0.8697    | 0.8459 | 0.8565 |
| 0.2354        | 13.0  | 1586 | 0.3136          | 0.8822   | 0.8694    | 0.8392 | 0.8521 |
| 0.2303        | 14.0  | 1708 | 0.3172          | 0.8747   | 0.8463    | 0.8563 | 0.8510 |
| 0.2172        | 15.0  | 1830 | 0.3120          | 0.8822   | 0.8656    | 0.8442 | 0.8537 |
| 0.2159        | 16.0  | 1952 | 0.3116          | 0.8622   | 0.8319    | 0.8400 | 0.8357 |
| 0.2192        | 17.0  | 2074 | 0.3123          | 0.8847   | 0.8717    | 0.8434 | 0.8557 |
| 0.2124        | 18.0  | 2196 | 0.3150          | 0.8647   | 0.8340    | 0.8467 | 0.8399 |
| 0.2077        | 19.0  | 2318 | 0.3084          | 0.8797   | 0.8585    | 0.8474 | 0.8526 |
| 0.205         | 20.0  | 2440 | 0.3078          | 0.8797   | 0.8572    | 0.8499 | 0.8534 |


### Framework versions

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