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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: xlm-roberta-base-ncbi_disease
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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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# xlm-roberta-base-ncbi_disease
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This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0915
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- Precision: 0.8273
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- Recall: 0.8763
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- F1: 0.8511
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- Accuracy: 0.9866
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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: 32
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- eval_batch_size: 32
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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: 15
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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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| No log | 1.0 | 169 | 0.0682 | 0.7049 | 0.7763 | 0.7389 | 0.9784 |
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| No log | 2.0 | 338 | 0.0575 | 0.7558 | 0.8592 | 0.8042 | 0.9832 |
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| 0.0889 | 3.0 | 507 | 0.0558 | 0.8092 | 0.8592 | 0.8334 | 0.9859 |
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| 0.0889 | 4.0 | 676 | 0.0595 | 0.8316 | 0.8579 | 0.8446 | 0.9858 |
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| 0.0889 | 5.0 | 845 | 0.0665 | 0.7998 | 0.8566 | 0.8272 | 0.9850 |
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| 0.0191 | 6.0 | 1014 | 0.0796 | 0.8229 | 0.85 | 0.8362 | 0.9862 |
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| 0.0191 | 7.0 | 1183 | 0.0783 | 0.8193 | 0.8474 | 0.8331 | 0.9860 |
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| 0.0191 | 8.0 | 1352 | 0.0792 | 0.8257 | 0.8539 | 0.8396 | 0.9864 |
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| 0.0079 | 9.0 | 1521 | 0.0847 | 0.8154 | 0.8658 | 0.8398 | 0.9851 |
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| 0.0079 | 10.0 | 1690 | 0.0855 | 0.8160 | 0.875 | 0.8444 | 0.9857 |
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| 0.0079 | 11.0 | 1859 | 0.0868 | 0.8081 | 0.8645 | 0.8353 | 0.9864 |
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| 0.0037 | 12.0 | 2028 | 0.0912 | 0.8036 | 0.8776 | 0.8390 | 0.9853 |
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| 0.0037 | 13.0 | 2197 | 0.0907 | 0.8323 | 0.8684 | 0.8500 | 0.9868 |
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| 0.0037 | 14.0 | 2366 | 0.0899 | 0.8192 | 0.8763 | 0.8468 | 0.9865 |
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| 0.0023 | 15.0 | 2535 | 0.0915 | 0.8273 | 0.8763 | 0.8511 | 0.9866 |
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### Framework versions
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- Transformers 4.26.1
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- Pytorch 1.13.1+cu116
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- Datasets 2.9.0
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- Tokenizers 0.13.2
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