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---
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: []
---
<!-- 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-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
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