transformers safetensors xlm-roberta text-classification generated_from_trainer base_model:xlm-roberta-large license:mit autotrain_compatible endpoints_compatible region:us
---
license: mit
base_model: xlm-roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: xlm-roberta-large-twitter-indonesia-sarcastic
  results: []
---

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# xlm-roberta-large-twitter-indonesia-sarcastic

This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4322
- Accuracy: 0.8885
- F1: 0.7692
- Precision: 0.7937
- Recall: 0.7463

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.5862        | 1.0   | 59   | 0.5304          | 0.75     | 0.0    | 0.0       | 0.0    |
| 0.5168        | 2.0   | 118  | 0.4897          | 0.75     | 0.0    | 0.0       | 0.0    |
| 0.4771        | 3.0   | 177  | 0.4535          | 0.7948   | 0.3373 | 0.875     | 0.2090 |
| 0.4101        | 4.0   | 236  | 0.4235          | 0.7910   | 0.6585 | 0.5567    | 0.8060 |
| 0.3225        | 5.0   | 295  | 0.4733          | 0.8507   | 0.5918 | 0.9355    | 0.4328 |
| 0.2246        | 6.0   | 354  | 0.3362          | 0.8694   | 0.7009 | 0.82      | 0.6119 |
| 0.166         | 7.0   | 413  | 0.3672          | 0.8769   | 0.7227 | 0.8269    | 0.6418 |
| 0.0989        | 8.0   | 472  | 0.3835          | 0.8769   | 0.7626 | 0.7361    | 0.7910 |
| 0.0797        | 9.0   | 531  | 0.4379          | 0.8993   | 0.7939 | 0.8125    | 0.7761 |
| 0.08          | 10.0  | 590  | 0.7677          | 0.8545   | 0.7451 | 0.6628    | 0.8507 |
| 0.0505        | 11.0  | 649  | 0.7316          | 0.8806   | 0.7288 | 0.8431    | 0.6418 |
| 0.073         | 12.0  | 708  | 0.4796          | 0.9104   | 0.8182 | 0.8308    | 0.8060 |
| 0.05          | 13.0  | 767  | 0.8469          | 0.8694   | 0.7059 | 0.8077    | 0.6269 |
| 0.0583        | 14.0  | 826  | 0.7266          | 0.8918   | 0.7563 | 0.8654    | 0.6716 |
| 0.0275        | 15.0  | 885  | 0.8974          | 0.8918   | 0.7387 | 0.9318    | 0.6119 |


### Framework versions

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