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---
license: mit
base_model: indobenchmark/indobert-base-p2
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: general_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# general_model
This model is a fine-tuned version of [indobenchmark/indobert-base-p2](https://huggingface.co/indobenchmark/indobert-base-p2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2535
- Accuracy: 0.9132
- F1: 0.9412
- Precision: 0.9286
- Recall: 0.9542
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.3084 | 1.0 | 795 | 0.2535 | 0.9132 | 0.9412 | 0.9286 | 0.9542 |
| 0.2129 | 2.0 | 1590 | 0.2975 | 0.9056 | 0.9369 | 0.9131 | 0.9620 |
| 0.1516 | 3.0 | 2385 | 0.3605 | 0.9043 | 0.9346 | 0.9314 | 0.9378 |
| 0.095 | 4.0 | 3180 | 0.5394 | 0.8943 | 0.9301 | 0.8973 | 0.9655 |
| 0.076 | 5.0 | 3975 | 0.5923 | 0.8955 | 0.9292 | 0.9182 | 0.9404 |
| 0.0399 | 6.0 | 4770 | 0.5995 | 0.8899 | 0.9247 | 0.9212 | 0.9283 |
| 0.0288 | 7.0 | 5565 | 0.7001 | 0.8930 | 0.9261 | 0.9326 | 0.9197 |
| 0.0178 | 8.0 | 6360 | 0.7846 | 0.8930 | 0.9285 | 0.9049 | 0.9534 |
| 0.0083 | 9.0 | 7155 | 0.7989 | 0.8943 | 0.9288 | 0.9125 | 0.9456 |
| 0.0063 | 10.0 | 7950 | 0.8204 | 0.8924 | 0.9276 | 0.9102 | 0.9456 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
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