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---
language:
- id
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: nerui-base-0
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-0
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: 0.0540
- Location Precision: 0.8544
- Location Recall: 0.9362
- Location F1: 0.8934
- Location Number: 94
- Organization Precision: 0.9119
- Organization Recall: 0.8683
- Organization F1: 0.8896
- Organization Number: 167
- Person Precision: 0.9926
- Person Recall: 0.9781
- Person F1: 0.9853
- Person Number: 137
- Overall Precision: 0.9244
- Overall Recall: 0.9221
- Overall F1: 0.9233
- Overall Accuracy: 0.9851
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------------------:|:---------------:|:-----------:|:---------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:----------------:|:-------------:|:---------:|:-------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.2529 | 1.0 | 96 | 0.0478 | 0.9438 | 0.8936 | 0.9180 | 94 | 0.8556 | 0.9222 | 0.8876 | 167 | 0.9776 | 0.9562 | 0.9668 | 137 | 0.9156 | 0.9271 | 0.9213 | 0.9851 |
| 0.0617 | 2.0 | 192 | 0.0545 | 0.87 | 0.9255 | 0.8969 | 94 | 0.88 | 0.9222 | 0.9006 | 167 | 0.9571 | 0.9781 | 0.9675 | 137 | 0.9036 | 0.9422 | 0.9225 | 0.9815 |
| 0.0309 | 3.0 | 288 | 0.0539 | 0.8447 | 0.9255 | 0.8832 | 94 | 0.8868 | 0.8443 | 0.8650 | 167 | 0.9708 | 0.9708 | 0.9708 | 137 | 0.9048 | 0.9070 | 0.9059 | 0.9829 |
| 0.0178 | 4.0 | 384 | 0.0556 | 0.8878 | 0.9255 | 0.9062 | 94 | 0.8941 | 0.9102 | 0.9021 | 167 | 0.9853 | 0.9781 | 0.9817 | 137 | 0.9233 | 0.9372 | 0.9302 | 0.9845 |
| 0.0103 | 5.0 | 480 | 0.0540 | 0.8544 | 0.9362 | 0.8934 | 94 | 0.9119 | 0.8683 | 0.8896 | 167 | 0.9926 | 0.9781 | 0.9853 | 137 | 0.9244 | 0.9221 | 0.9233 | 0.9851 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.15.2
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