metadata
license: mit
base_model: indobenchmark/indobert-base-p2
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: koneksi_model
results: []
koneksi_model
This model is a fine-tuned version of indobenchmark/indobert-base-p2 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.0917
- Accuracy: 0.75
- F1: 0.7493
- Precision: 0.7497
- Recall: 0.7491
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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| No log | 0.52 | 50 | 0.5014 | 0.7812 | 0.7810 | 0.7865 | 0.7839 |
| No log | 1.04 | 100 | 0.5135 | 0.7708 | 0.7708 | 0.7732 | 0.7726 |
| No log | 1.56 | 150 | 0.5564 | 0.7552 | 0.7503 | 0.7924 | 0.7624 |
| No log | 2.08 | 200 | 0.5628 | 0.7604 | 0.7572 | 0.7659 | 0.7570 |
| No log | 2.6 | 250 | 0.8524 | 0.7083 | 0.7037 | 0.7132 | 0.7043 |
| No log | 3.12 | 300 | 0.6830 | 0.7448 | 0.7432 | 0.7456 | 0.7428 |
| No log | 3.65 | 350 | 0.9662 | 0.7292 | 0.7262 | 0.7321 | 0.7261 |
| No log | 4.17 | 400 | 0.9936 | 0.7656 | 0.7656 | 0.7659 | 0.7663 |
| No log | 4.69 | 450 | 1.0558 | 0.7604 | 0.7603 | 0.7604 | 0.7609 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0