---
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: indobert_artha
  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. -->

# indobert_artha

This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co./indolem/indobert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6470
- Balanced accuracy: 0.4809

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Balanced accuracy |
|:-------------:|:-----:|:----:|:---------------:|:-----------------:|
| 1.4443        | 1.0   | 92   | 1.2032          | 0.125             |
| 1.112         | 2.0   | 184  | 0.9012          | 0.3006            |
| 0.8258        | 3.0   | 276  | 0.8611          | 0.4565            |
| 0.6286        | 4.0   | 368  | 0.7711          | 0.4397            |
| 0.4205        | 5.0   | 460  | 0.8665          | 0.4900            |
| 0.3025        | 6.0   | 552  | 0.9085          | 0.4572            |
| 0.1904        | 7.0   | 644  | 1.1407          | 0.4584            |
| 0.1387        | 8.0   | 736  | 1.2191          | 0.4682            |
| 0.1294        | 9.0   | 828  | 1.3164          | 0.4470            |
| 0.097         | 10.0  | 920  | 1.4438          | 0.4245            |
| 0.0843        | 11.0  | 1012 | 1.3584          | 0.4603            |
| 0.0829        | 12.0  | 1104 | 1.3619          | 0.4442            |
| 0.0667        | 13.0  | 1196 | 1.4805          | 0.4536            |
| 0.0596        | 14.0  | 1288 | 1.6224          | 0.4917            |
| 0.0538        | 15.0  | 1380 | 1.6581          | 0.4253            |
| 0.0488        | 16.0  | 1472 | 1.6128          | 0.4982            |
| 0.0442        | 17.0  | 1564 | 1.8136          | 0.4951            |
| 0.0426        | 18.0  | 1656 | 1.6496          | 0.4859            |
| 0.0384        | 19.0  | 1748 | 1.6517          | 0.4702            |
| 0.0311        | 20.0  | 1840 | 1.6183          | 0.4901            |
| 0.0288        | 21.0  | 1932 | 1.7072          | 0.4647            |
| 0.0283        | 22.0  | 2024 | 1.6827          | 0.4653            |
| 0.0244        | 23.0  | 2116 | 1.6211          | 0.4777            |
| 0.0264        | 24.0  | 2208 | 1.6428          | 0.4719            |
| 0.0202        | 25.0  | 2300 | 1.6462          | 0.4907            |
| 0.0198        | 26.0  | 2392 | 1.6719          | 0.4841            |
| 0.024         | 27.0  | 2484 | 1.6376          | 0.4957            |
| 0.0205        | 28.0  | 2576 | 1.6477          | 0.4775            |
| 0.0162        | 29.0  | 2668 | 1.6459          | 0.4909            |
| 0.0165        | 30.0  | 2760 | 1.6470          | 0.4809            |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1