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---
language:
- id
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: nerui-base-3
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-3
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: 0.0518
- Location Precision: 0.9222
- Location Recall: 0.9651
- Location F1: 0.9432
- Location Number: 86
- Organization Precision: 0.9333
- Organization Recall: 0.9438
- Organization F1: 0.9385
- Organization Number: 178
- Person Precision: 0.9843
- Person Recall: 0.9766
- Person F1: 0.9804
- Person Number: 128
- Overall Precision: 0.9471
- Overall Recall: 0.9592
- Overall F1: 0.9531
- Overall Accuracy: 0.9889
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------------------:|:---------------:|:-----------:|:---------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:----------------:|:-------------:|:---------:|:-------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.241 | 1.0 | 96 | 0.0559 | 0.8039 | 0.9535 | 0.8723 | 86 | 0.9086 | 0.8933 | 0.9008 | 178 | 0.9538 | 0.9688 | 0.9612 | 128 | 0.8968 | 0.9311 | 0.9136 | 0.9833 |
| 0.0545 | 2.0 | 192 | 0.0551 | 0.8454 | 0.9535 | 0.8962 | 86 | 0.9011 | 0.9213 | 0.9111 | 178 | 0.9841 | 0.9688 | 0.9764 | 128 | 0.9136 | 0.9439 | 0.9285 | 0.9827 |
| 0.0286 | 3.0 | 288 | 0.0485 | 0.8586 | 0.9884 | 0.9189 | 86 | 0.9480 | 0.9213 | 0.9345 | 178 | 0.9841 | 0.9688 | 0.9764 | 128 | 0.9372 | 0.9515 | 0.9443 | 0.9870 |
| 0.0151 | 4.0 | 384 | 0.0570 | 0.9121 | 0.9651 | 0.9379 | 86 | 0.9375 | 0.9270 | 0.9322 | 178 | 0.9766 | 0.9766 | 0.9766 | 128 | 0.9443 | 0.9515 | 0.9479 | 0.9873 |
| 0.0088 | 5.0 | 480 | 0.0518 | 0.9222 | 0.9651 | 0.9432 | 86 | 0.9333 | 0.9438 | 0.9385 | 178 | 0.9843 | 0.9766 | 0.9804 | 128 | 0.9471 | 0.9592 | 0.9531 | 0.9889 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.15.2
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