---
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: nerugm-base-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. -->

# nerugm-base-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: 0.2607
- Precision: 0.8198
- Recall: 0.8946
- F1: 0.8556
- Accuracy: 0.9651

## 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: 16
- eval_batch_size: 64
- 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 | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3182        | 1.0   | 106  | 0.1284          | 0.7463    | 0.8547 | 0.7968 | 0.9572   |
| 0.1137        | 2.0   | 212  | 0.1302          | 0.7230    | 0.8775 | 0.7928 | 0.9562   |
| 0.0683        | 3.0   | 318  | 0.1249          | 0.7833    | 0.8547 | 0.8174 | 0.9606   |
| 0.0454        | 4.0   | 424  | 0.1464          | 0.7711    | 0.8832 | 0.8234 | 0.9591   |
| 0.0325        | 5.0   | 530  | 0.1557          | 0.8010    | 0.8832 | 0.8401 | 0.9641   |
| 0.0211        | 6.0   | 636  | 0.2112          | 0.7915    | 0.8974 | 0.8411 | 0.9599   |
| 0.015         | 7.0   | 742  | 0.1944          | 0.7734    | 0.8946 | 0.8296 | 0.9606   |
| 0.0113        | 8.0   | 848  | 0.2151          | 0.8140    | 0.8974 | 0.8537 | 0.9665   |
| 0.0075        | 9.0   | 954  | 0.1996          | 0.8140    | 0.8974 | 0.8537 | 0.9685   |
| 0.0067        | 10.0  | 1060 | 0.2077          | 0.8470    | 0.8832 | 0.8647 | 0.9685   |
| 0.0039        | 11.0  | 1166 | 0.2609          | 0.7698    | 0.8860 | 0.8238 | 0.9579   |
| 0.0028        | 12.0  | 1272 | 0.2498          | 0.8263    | 0.8946 | 0.8591 | 0.9648   |
| 0.0035        | 13.0  | 1378 | 0.2407          | 0.8179    | 0.8832 | 0.8493 | 0.9643   |
| 0.003         | 14.0  | 1484 | 0.2475          | 0.7919    | 0.8889 | 0.8376 | 0.9631   |
| 0.0016        | 15.0  | 1590 | 0.2552          | 0.7975    | 0.8974 | 0.8445 | 0.9641   |
| 0.0016        | 16.0  | 1696 | 0.2463          | 0.8268    | 0.8974 | 0.8607 | 0.9665   |
| 0.0012        | 17.0  | 1802 | 0.2500          | 0.8324    | 0.8917 | 0.8611 | 0.9665   |
| 0.0009        | 18.0  | 1908 | 0.2629          | 0.8208    | 0.9003 | 0.8587 | 0.9653   |
| 0.0014        | 19.0  | 2014 | 0.2619          | 0.8182    | 0.8974 | 0.8560 | 0.9651   |
| 0.0006        | 20.0  | 2120 | 0.2607          | 0.8198    | 0.8946 | 0.8556 | 0.9651   |


### Framework versions

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