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-reddit-indonesia-sarcastic
  results: []
---

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# xlm-roberta-large-reddit-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: 0.4768
- Accuracy: 0.8120
- F1: 0.6274
- Precision: 0.6217
- Recall: 0.6331

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.5177        | 1.0   | 309  | 0.4619          | 0.7867   | 0.4801 | 0.6150    | 0.3938 |
| 0.4158        | 2.0   | 618  | 0.4048          | 0.8143   | 0.5705 | 0.6770    | 0.4929 |
| 0.3535        | 3.0   | 927  | 0.4726          | 0.8051   | 0.4742 | 0.7294    | 0.3513 |
| 0.2983        | 4.0   | 1236 | 0.5060          | 0.8065   | 0.5806 | 0.6342    | 0.5354 |
| 0.2439        | 5.0   | 1545 | 0.4598          | 0.8143   | 0.6203 | 0.6350    | 0.6062 |
| 0.198         | 6.0   | 1854 | 0.5417          | 0.8058   | 0.5595 | 0.6468    | 0.4929 |
| 0.1655        | 7.0   | 2163 | 0.6252          | 0.8072   | 0.575  | 0.6411    | 0.5212 |
| 0.1242        | 8.0   | 2472 | 0.8431          | 0.8122   | 0.6051 | 0.6384    | 0.5751 |


### Framework versions

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