metadata

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

NusaBERT-base-NERP

This model is a fine-tuned version of 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