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---
tags:
- generated_from_trainer
datasets:
- indonlu
metrics:
- accuracy
- f1
model-index:
- name: distilled-optimized-indobert-classification
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: indonlu
type: indonlu
args: smsa
metrics:
- name: Accuracy
type: accuracy
value: 0.9
- name: F1
type: f1
value: 0.8994069293432798
---
<!-- 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. -->
# distilled-optimized-indobert-classification
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the indonlu dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7397
- Accuracy: 0.9
- F1: 0.8994
## 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: 4.315104717136378e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 33
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 9
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.128 | 1.0 | 688 | 0.8535 | 0.8913 | 0.8917 |
| 0.1475 | 2.0 | 1376 | 0.9171 | 0.8913 | 0.8913 |
| 0.0997 | 3.0 | 2064 | 0.7799 | 0.8960 | 0.8951 |
| 0.0791 | 4.0 | 2752 | 0.7179 | 0.9032 | 0.9023 |
| 0.0577 | 5.0 | 3440 | 0.6908 | 0.9063 | 0.9055 |
| 0.0406 | 6.0 | 4128 | 0.7613 | 0.8992 | 0.8986 |
| 0.0275 | 7.0 | 4816 | 0.7502 | 0.8992 | 0.8989 |
| 0.023 | 8.0 | 5504 | 0.7408 | 0.8976 | 0.8969 |
| 0.0169 | 9.0 | 6192 | 0.7397 | 0.9 | 0.8994 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.1.0
- Tokenizers 0.12.1
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