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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_translated-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_translated-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: 1.0761
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- Precision: 0.7299
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- Recall: 0.7427
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- F1: 0.7363
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- Accuracy: 0.8415
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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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| 1.4842 | 1.0 | 814 | 0.7712 | 0.5858 | 0.6026 | 0.5941 | 0.7918 |
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| 0.5128 | 2.0 | 1628 | 0.6435 | 0.6469 | 0.6828 | 0.6644 | 0.8119 |
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| 0.3526 | 3.0 | 2442 | 0.7030 | 0.6823 | 0.7045 | 0.6933 | 0.8242 |
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| 0.2142 | 4.0 | 3256 | 0.7695 | 0.7112 | 0.7243 | 0.7177 | 0.8381 |
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| 0.1422 | 5.0 | 4070 | 0.8550 | 0.7203 | 0.7310 | 0.7256 | 0.8399 |
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| 0.1188 | 6.0 | 4884 | 0.9209 | 0.7183 | 0.7333 | 0.7258 | 0.8391 |
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| 0.0915 | 7.0 | 5698 | 0.9892 | 0.7238 | 0.7372 | 0.7305 | 0.8404 |
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| 0.072 | 8.0 | 6512 | 1.0271 | 0.7230 | 0.7364 | 0.7296 | 0.8417 |
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| 0.0626 | 9.0 | 7326 | 1.0608 | 0.7312 | 0.7417 | 0.7364 | 0.8419 |
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| 0.0613 | 10.0 | 8140 | 1.0761 | 0.7299 | 0.7427 | 0.7363 | 0.8415 |
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### Framework versions
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- Transformers 4.27.3
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- Pytorch 1.13.1+cu116
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- Datasets 2.10.1
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- Tokenizers 0.13.2
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