---
language:
- id
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: nerui-base-2
  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. -->

# nerui-base-2

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: 0.0571
- Location Precision: 0.8812
- Location Recall: 0.9570
- Location F1: 0.9175
- Location Number: 93
- Organization Precision: 0.9130
- Organization Recall: 0.8855
- Organization F1: 0.8991
- Organization Number: 166
- Person Precision: 0.9786
- Person Recall: 0.9648
- Person F1: 0.9716
- Person Number: 142
- Overall Precision: 0.9279
- Overall Recall: 0.9302
- Overall F1: 0.9290
- Overall Accuracy: 0.9857

## 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: 5.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Location Precision | Location Recall | Location F1 | Location Number | Organization Precision | Organization Recall | Organization F1 | Organization Number | Person Precision | Person Recall | Person F1 | Person Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------------------:|:---------------:|:-----------:|:---------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:----------------:|:-------------:|:---------:|:-------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.2624        | 1.0   | 96   | 0.0635          | 0.7857             | 0.9462          | 0.8585      | 93              | 0.8545                 | 0.8494              | 0.8520          | 166                 | 0.9858           | 0.9789        | 0.9823    | 142           | 0.8804            | 0.9177         | 0.8987     | 0.9802           |
| 0.054         | 2.0   | 192  | 0.0530          | 0.8318             | 0.9570          | 0.89        | 93              | 0.8580                 | 0.9096              | 0.8830          | 166                 | 0.9787           | 0.9718        | 0.9753    | 142           | 0.8915            | 0.9426         | 0.9164     | 0.9841           |
| 0.0268        | 3.0   | 288  | 0.0673          | 0.8257             | 0.9677          | 0.8911      | 93              | 0.8869                 | 0.8976              | 0.8922          | 166                 | 0.9857           | 0.9718        | 0.9787    | 142           | 0.9041            | 0.9401         | 0.9218     | 0.9833           |
| 0.0159        | 4.0   | 384  | 0.0546          | 0.9167             | 0.9462          | 0.9312      | 93              | 0.8743                 | 0.9217              | 0.8974          | 166                 | 0.9786           | 0.9648        | 0.9716    | 142           | 0.9197            | 0.9426         | 0.9310     | 0.9868           |
| 0.0108        | 5.0   | 480  | 0.0571          | 0.8812             | 0.9570          | 0.9175      | 93              | 0.9130                 | 0.8855              | 0.8991          | 166                 | 0.9786           | 0.9648        | 0.9716    | 142           | 0.9279            | 0.9302         | 0.9290     | 0.9857           |


### Framework versions

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