|
--- |
|
license: mit |
|
base_model: indolem/indobert-base-uncased |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- accuracy |
|
model-index: |
|
- name: Zidan_model_output_new |
|
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. --> |
|
|
|
# Zidan_model_output_new |
|
|
|
This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co./indolem/indobert-base-uncased) on an unknown dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.7244 |
|
- Accuracy: 0.8545 |
|
|
|
## 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: 1e-05 |
|
- train_batch_size: 8 |
|
- eval_batch_size: 16 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 12 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:| |
|
| No log | 1.0 | 124 | 0.8299 | 0.6364 | |
|
| No log | 2.0 | 248 | 0.4376 | 0.8182 | |
|
| No log | 3.0 | 372 | 0.4606 | 0.8364 | |
|
| No log | 4.0 | 496 | 0.4147 | 0.8182 | |
|
| 0.6148 | 5.0 | 620 | 0.3365 | 0.8727 | |
|
| 0.6148 | 6.0 | 744 | 0.3996 | 0.8545 | |
|
| 0.6148 | 7.0 | 868 | 0.5302 | 0.8364 | |
|
| 0.6148 | 8.0 | 992 | 0.5224 | 0.8545 | |
|
| 0.1989 | 9.0 | 1116 | 0.5880 | 0.8727 | |
|
| 0.1989 | 10.0 | 1240 | 0.6525 | 0.8545 | |
|
| 0.1989 | 11.0 | 1364 | 0.5338 | 0.8909 | |
|
| 0.1989 | 12.0 | 1488 | 0.7244 | 0.8545 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.41.2 |
|
- Pytorch 2.3.0+cu121 |
|
- Datasets 2.20.0 |
|
- Tokenizers 0.19.1 |
|
|
