metadata
license: mit
tags:
- generated_from_trainer
datasets:
- indonlu
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: indobert-finetuned-pos
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: indonlu
type: indonlu
config: posp
split: train
args: posp
metrics:
- name: Precision
type: precision
value: 0.9477284686897035
- name: Recall
type: recall
value: 0.9477284686897035
- name: F1
type: f1
value: 0.9477284686897035
- name: Accuracy
type: accuracy
value: 0.9477284686897035
indobert-finetuned-pos
This model is a fine-tuned version of indobenchmark/indobert-base-p2 on the indonlu dataset. It achieves the following results on the evaluation set:
- Loss: 0.1762
- Precision: 0.9477
- Recall: 0.9477
- F1: 0.9477
- Accuracy: 0.9477
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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 420 | 0.2238 | 0.9278 | 0.9278 | 0.9278 | 0.9278 |
| 0.3621 | 2.0 | 840 | 0.1806 | 0.9437 | 0.9437 | 0.9437 | 0.9437 |
| 0.1504 | 3.0 | 1260 | 0.1762 | 0.9477 | 0.9477 | 0.9477 | 0.9477 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2