---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: xlm-roberta-base-reddit-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. -->

# xlm-roberta-base-reddit-indonesia-sarcastic

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5093
- Accuracy: 0.8031
- F1: 0.5690
- Precision: 0.6284
- Recall: 0.5198

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.5174        | 1.0   | 309  | 0.4618          | 0.7725   | 0.4641 | 0.5650    | 0.3938 |
| 0.4462        | 2.0   | 618  | 0.4407          | 0.7994   | 0.5428 | 0.6316    | 0.4759 |
| 0.3952        | 3.0   | 927  | 0.4690          | 0.8037   | 0.4991 | 0.69      | 0.3909 |
| 0.3525        | 4.0   | 1236 | 0.4905          | 0.8079   | 0.5152 | 0.6990    | 0.4079 |
| 0.3102        | 5.0   | 1545 | 0.4741          | 0.8122   | 0.5917 | 0.6486    | 0.5439 |
| 0.2645        | 6.0   | 1854 | 0.4964          | 0.8101   | 0.5976 | 0.6358    | 0.5637 |
| 0.2168        | 7.0   | 2163 | 0.5216          | 0.8079   | 0.5824 | 0.6385    | 0.5354 |
| 0.1759        | 8.0   | 2472 | 0.6826          | 0.8044   | 0.5818 | 0.6254    | 0.5439 |


### Framework versions

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