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---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
model-index:
- name: XLM-R-BASE-Finetune-step2-finetune-and-eval-may31-volcanic-moon-1-D-05-31-T-03-38
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-R-BASE-Finetune-step2-finetune-and-eval-may31-volcanic-moon-1-D-05-31-T-03-38
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: 0.3977
- Precision 0: 0.8721
- Precision 1: 0.8029
- Recall 0: 0.8633
- Recall 1: 0.8148
- F1 0: 0.8677
- F1 1: 0.8088
- Precision Weighted: 0.8440
- Recall Weighted: 0.8436
- F1 Weighted: 0.8438
- F1 Macro: 0.8382
## 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: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision 0 | Precision 1 | Recall 0 | Recall 1 | F1 0 | F1 1 | Precision Weighted | Recall Weighted | F1 Weighted | F1 Macro |
|:-------------:|:-----:|:----:|:---------------:|:-----------:|:-----------:|:--------:|:--------:|:------:|:------:|:------------------:|:---------------:|:-----------:|:--------:|
| 0.5501 | 1.0 | 469 | 0.4524 | 0.7699 | 0.8995 | 0.9556 | 0.5823 | 0.8528 | 0.7069 | 0.8226 | 0.804 | 0.7936 | 0.7799 |
| 0.3869 | 2.0 | 938 | 0.4545 | 0.8995 | 0.7229 | 0.7710 | 0.8739 | 0.8303 | 0.7913 | 0.8278 | 0.8128 | 0.8145 | 0.8108 |
| 0.3825 | 3.0 | 1407 | 0.3678 | 0.8429 | 0.8191 | 0.8855 | 0.7586 | 0.8637 | 0.7877 | 0.8333 | 0.834 | 0.8329 | 0.8257 |
| 0.2683 | 4.0 | 1876 | 0.3977 | 0.8721 | 0.8029 | 0.8633 | 0.8148 | 0.8677 | 0.8088 | 0.8440 | 0.8436 | 0.8438 | 0.8382 |
| 0.2297 | 5.0 | 2345 | 0.5155 | 0.8711 | 0.7937 | 0.8552 | 0.8148 | 0.8631 | 0.8041 | 0.8396 | 0.8388 | 0.8391 | 0.8336 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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