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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-pl
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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-pl
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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: 0.1009
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- Precision: 0.9579
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- Recall: 0.9512
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- F1: 0.9546
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- Accuracy: 0.9860
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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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| 1.1608 | 1.0 | 798 | 0.2575 | 0.8881 | 0.8916 | 0.8898 | 0.9532 |
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| 0.1561 | 2.0 | 1596 | 0.1188 | 0.9459 | 0.9389 | 0.9424 | 0.9806 |
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| 0.0979 | 3.0 | 2394 | 0.1060 | 0.9507 | 0.9486 | 0.9497 | 0.9838 |
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| 0.0579 | 4.0 | 3192 | 0.0916 | 0.9573 | 0.9475 | 0.9524 | 0.9851 |
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| 0.0507 | 5.0 | 3990 | 0.1109 | 0.9527 | 0.9506 | 0.9516 | 0.9839 |
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| 0.0344 | 6.0 | 4788 | 0.0987 | 0.9575 | 0.9488 | 0.9531 | 0.9855 |
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| 0.0266 | 7.0 | 5586 | 0.1010 | 0.9584 | 0.9501 | 0.9542 | 0.9854 |
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| 0.0211 | 8.0 | 6384 | 0.1051 | 0.9575 | 0.9498 | 0.9536 | 0.9855 |
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| 0.0168 | 9.0 | 7182 | 0.1009 | 0.9577 | 0.9516 | 0.9546 | 0.9861 |
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| 0.016 | 10.0 | 7980 | 0.1009 | 0.9579 | 0.9512 | 0.9546 | 0.9860 |
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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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