---
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: indobert-base-uncased-twitter-indonesia-sarcastic
  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. -->

# indobert-base-uncased-twitter-indonesia-sarcastic

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: 0.4202
- Accuracy: 0.8030
- F1: 0.6467
- Precision: 0.5843
- Recall: 0.7239

## 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: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 100.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.5531        | 1.0   | 59   | 0.4977          | 0.7724   | 0.4078 | 0.5833    | 0.3134 |
| 0.4992        | 2.0   | 118  | 0.4785          | 0.7724   | 0.3441 | 0.6154    | 0.2388 |
| 0.44          | 3.0   | 177  | 0.4819          | 0.7799   | 0.3656 | 0.6538    | 0.2537 |
| 0.3815        | 4.0   | 236  | 0.4524          | 0.8097   | 0.6623 | 0.5952    | 0.7463 |
| 0.3104        | 5.0   | 295  | 0.4547          | 0.8172   | 0.5421 | 0.725     | 0.4328 |
| 0.2592        | 6.0   | 354  | 0.4058          | 0.8172   | 0.5664 | 0.6957    | 0.4776 |
| 0.2083        | 7.0   | 413  | 0.4358          | 0.8060   | 0.5738 | 0.6364    | 0.5224 |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0