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---
license: mit
base_model: LazarusNLP/NusaBERT-base
tags:
- generated_from_trainer
datasets:
- indonlu
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: NusaBERT-base-NERP
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: indonlu
type: indonlu
config: nerp
split: validation
args: nerp
metrics:
- name: Precision
type: precision
value: 0.8060507833603457
- name: Recall
type: recall
value: 0.8405633802816901
- name: F1
type: f1
value: 0.8229453943739657
- name: Accuracy
type: accuracy
value: 0.9634085213032582
---
<!-- 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. -->
# NusaBERT-base-NERP
This model is a fine-tuned version of [LazarusNLP/NusaBERT-base](https://huggingface.co/LazarusNLP/NusaBERT-base) on the indonlu dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1254
- Precision: 0.8061
- Recall: 0.8406
- F1: 0.8229
- Accuracy: 0.9634
## 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: 2e-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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 420 | 0.1444 | 0.7415 | 0.8272 | 0.7820 | 0.9543 |
| 0.2385 | 2.0 | 840 | 0.1276 | 0.7879 | 0.8187 | 0.8030 | 0.9586 |
| 0.1143 | 3.0 | 1260 | 0.1260 | 0.7815 | 0.8510 | 0.8148 | 0.9597 |
| 0.0903 | 4.0 | 1680 | 0.1305 | 0.7836 | 0.8516 | 0.8162 | 0.9596 |
| 0.07 | 5.0 | 2100 | 0.1342 | 0.8158 | 0.8255 | 0.8206 | 0.9605 |
| 0.0582 | 6.0 | 2520 | 0.1343 | 0.8172 | 0.8408 | 0.8288 | 0.9606 |
| 0.0582 | 7.0 | 2940 | 0.1440 | 0.7936 | 0.8476 | 0.8197 | 0.9594 |
| 0.0521 | 8.0 | 3360 | 0.1447 | 0.8069 | 0.8453 | 0.8257 | 0.9605 |
| 0.0446 | 9.0 | 3780 | 0.1512 | 0.7996 | 0.8453 | 0.8218 | 0.9599 |
| 0.0417 | 10.0 | 4200 | 0.1524 | 0.8078 | 0.8453 | 0.8261 | 0.9606 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0+cu118
- Datasets 2.17.1
- Tokenizers 0.15.1
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