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---
library_name: transformers
license: mit
base_model: FacebookAI/xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: xlm-roberta-base-hau-finetuned-augmentation-LUNAR
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-hau-finetuned-augmentation-LUNAR
This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3543
- F1: 0.6620
- Roc Auc: 0.7803
- Accuracy: 0.4983
## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| 0.4603 | 1.0 | 144 | 0.4533 | 0.0933 | 0.5307 | 0.1916 |
| 0.4368 | 2.0 | 288 | 0.4219 | 0.1953 | 0.5812 | 0.2003 |
| 0.3826 | 3.0 | 432 | 0.3880 | 0.3288 | 0.6189 | 0.2787 |
| 0.3535 | 4.0 | 576 | 0.3562 | 0.5208 | 0.6918 | 0.3833 |
| 0.3018 | 5.0 | 720 | 0.3511 | 0.5697 | 0.7308 | 0.4007 |
| 0.2543 | 6.0 | 864 | 0.3593 | 0.6030 | 0.7492 | 0.4338 |
| 0.2549 | 7.0 | 1008 | 0.3463 | 0.6063 | 0.7436 | 0.4460 |
| 0.2183 | 8.0 | 1152 | 0.3425 | 0.6112 | 0.7484 | 0.4477 |
| 0.195 | 9.0 | 1296 | 0.3502 | 0.6004 | 0.7432 | 0.4355 |
| 0.1842 | 10.0 | 1440 | 0.3358 | 0.6223 | 0.7506 | 0.4686 |
| 0.151 | 11.0 | 1584 | 0.3451 | 0.6245 | 0.7578 | 0.4669 |
| 0.1385 | 12.0 | 1728 | 0.3383 | 0.6313 | 0.7584 | 0.4669 |
| 0.1286 | 13.0 | 1872 | 0.3492 | 0.6445 | 0.7692 | 0.4739 |
| 0.115 | 14.0 | 2016 | 0.3502 | 0.6553 | 0.7753 | 0.4913 |
| 0.1031 | 15.0 | 2160 | 0.3516 | 0.6529 | 0.7771 | 0.4826 |
| 0.1068 | 16.0 | 2304 | 0.3529 | 0.6448 | 0.7685 | 0.4808 |
| 0.0818 | 17.0 | 2448 | 0.3522 | 0.6542 | 0.7741 | 0.4895 |
| 0.0906 | 18.0 | 2592 | 0.3543 | 0.6620 | 0.7803 | 0.4983 |
| 0.0878 | 19.0 | 2736 | 0.3541 | 0.6595 | 0.7780 | 0.4948 |
| 0.0872 | 20.0 | 2880 | 0.3545 | 0.6584 | 0.7777 | 0.4948 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
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