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README.md
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---
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license: mit
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tags:
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- generated_from_trainer
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metrics:
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- precision
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- recall
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- f1
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- accuracy
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model-index:
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- name: fedcsis-slot_baseline-xlm_r-es
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# fedcsis-slot_baseline-xlm_r-es
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This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0521
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- Precision: 0.9728
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- Recall: 0.9711
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- F1: 0.9720
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- Accuracy: 0.9914
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 2e-05
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- train_batch_size: 16
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- eval_batch_size: 16
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- num_epochs: 10
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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| 0.7183 | 1.0 | 941 | 0.1287 | 0.9389 | 0.9429 | 0.9409 | 0.9802 |
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| 0.0792 | 2.0 | 1882 | 0.0698 | 0.9551 | 0.9609 | 0.9580 | 0.9876 |
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| 0.0502 | 3.0 | 2823 | 0.0586 | 0.9623 | 0.9624 | 0.9624 | 0.9886 |
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| 0.0312 | 4.0 | 3764 | 0.0511 | 0.9697 | 0.9668 | 0.9682 | 0.9904 |
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| 0.0229 | 5.0 | 4705 | 0.0494 | 0.9715 | 0.9687 | 0.9701 | 0.9913 |
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| 0.021 | 6.0 | 5646 | 0.0447 | 0.9697 | 0.9680 | 0.9689 | 0.9911 |
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| 0.0139 | 7.0 | 6587 | 0.0512 | 0.9715 | 0.9691 | 0.9703 | 0.9915 |
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| 0.0126 | 8.0 | 7528 | 0.0507 | 0.9713 | 0.9699 | 0.9706 | 0.9913 |
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| 0.01 | 9.0 | 8469 | 0.0500 | 0.9720 | 0.9702 | 0.9711 | 0.9913 |
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| 0.0072 | 10.0 | 9410 | 0.0521 | 0.9728 | 0.9711 | 0.9720 | 0.9914 |
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### Framework versions
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- Transformers 4.27.4
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- Pytorch 1.13.1+cu116
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- Datasets 2.11.0
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- Tokenizers 0.13.2
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