metadata

license: mit
base_model: indolem/indobert-base-uncased
tags:
  - generated_from_trainer
datasets:
  - id_nergrit_corpus
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: my_nergrit_model
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: id_nergrit_corpus
          type: id_nergrit_corpus
          config: ner
          split: validation
          args: ner
        metrics:
          - name: Precision
            type: precision
            value: 0.811461318051576
          - name: Recall
            type: recall
            value: 0.8397580358201874
          - name: F1
            type: f1
            value: 0.8253672184658428
          - name: Accuracy
            type: accuracy
            value: 0.947162775616083

my_nergrit_model

This model is a fine-tuned version of indolem/indobert-base-uncased on the id_nergrit_corpus dataset. It achieves the following results on the evaluation set:

Loss: 0.1786
Precision: 0.8115
Recall: 0.8398
F1: 0.8254
Accuracy: 0.9472

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: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.5063	1.0	784	0.1926	0.7911	0.8243	0.8074	0.9418
0.164	2.0	1568	0.1786	0.8115	0.8398	0.8254	0.9472

Framework versions

Transformers 4.33.2
Pytorch 2.0.1+cu118
Datasets 2.14.5
Tokenizers 0.13.3