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update model card README.md
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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-en
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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-en
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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: 0.1015
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- Precision: 0.9723
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- Recall: 0.9726
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- F1: 0.9725
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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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| 1.2866 | 1.0 | 814 | 0.3188 | 0.8661 | 0.8672 | 0.8666 | 0.9250 |
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| 0.1956 | 2.0 | 1628 | 0.1299 | 0.9409 | 0.9471 | 0.9440 | 0.9736 |
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| 0.1063 | 3.0 | 2442 | 0.1196 | 0.9537 | 0.9607 | 0.9572 | 0.9810 |
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| 0.0558 | 4.0 | 3256 | 0.0789 | 0.9661 | 0.9697 | 0.9679 | 0.9854 |
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| 0.0367 | 5.0 | 4070 | 0.0824 | 0.9685 | 0.9690 | 0.9687 | 0.9848 |
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| 0.031 | 6.0 | 4884 | 0.0887 | 0.9712 | 0.9728 | 0.9720 | 0.9859 |
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| 0.0233 | 7.0 | 5698 | 0.0829 | 0.9736 | 0.9744 | 0.9740 | 0.9872 |
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| 0.0139 | 8.0 | 6512 | 0.0879 | 0.9743 | 0.9747 | 0.9745 | 0.9876 |
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| 0.007 | 9.0 | 7326 | 0.0978 | 0.9740 | 0.9734 | 0.9737 | 0.9870 |
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| 0.0076 | 10.0 | 8140 | 0.1015 | 0.9723 | 0.9726 | 0.9725 | 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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