metadata
license: mit
base_model: indobenchmark/indobert-base-p2
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: absa_model
results: []
absa_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: 0.6475
- Accuracy: 0.8392
- F1: 0.8409
- Precision: 0.8444
- Recall: 0.8431
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: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 72 | 0.6475 | 0.8392 | 0.8409 | 0.8444 | 0.8431 |
| No log | 2.0 | 144 | 0.8639 | 0.8252 | 0.8156 | 0.8261 | 0.8177 |
| No log | 3.0 | 216 | 0.9170 | 0.7832 | 0.7800 | 0.8112 | 0.7676 |
| No log | 4.0 | 288 | 0.8206 | 0.8322 | 0.8359 | 0.8346 | 0.8405 |
| No log | 5.0 | 360 | 0.8318 | 0.8392 | 0.8417 | 0.8434 | 0.8404 |
| No log | 6.0 | 432 | 0.9578 | 0.8252 | 0.8255 | 0.8243 | 0.8325 |
| 0.0684 | 7.0 | 504 | 0.9713 | 0.8112 | 0.8027 | 0.8143 | 0.7967 |
| 0.0684 | 8.0 | 576 | 0.9850 | 0.8252 | 0.8137 | 0.8236 | 0.8089 |
| 0.0684 | 9.0 | 648 | 0.9955 | 0.8392 | 0.8258 | 0.8347 | 0.8203 |
| 0.0684 | 10.0 | 720 | 0.9964 | 0.8392 | 0.8258 | 0.8347 | 0.8203 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0