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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: roberta-finetuned-ner-without-data-sort
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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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# roberta-finetuned-ner-without-data-sort
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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: 0.0420
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- Precision: 0.9914
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- Recall: 0.9909
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- F1: 0.9912
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- Accuracy: 0.9920
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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: 8
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- eval_batch_size: 8
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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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| No log | 1.0 | 213 | 0.1879 | 0.9378 | 0.9414 | 0.9396 | 0.9493 |
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| No log | 2.0 | 426 | 0.1038 | 0.9725 | 0.9750 | 0.9737 | 0.9751 |
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| 0.4424 | 3.0 | 639 | 0.0701 | 0.9861 | 0.9851 | 0.9856 | 0.9863 |
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| 0.4424 | 4.0 | 852 | 0.0637 | 0.9882 | 0.9880 | 0.9881 | 0.9880 |
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| 0.0675 | 5.0 | 1065 | 0.0546 | 0.9851 | 0.9878 | 0.9865 | 0.9879 |
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| 0.0675 | 6.0 | 1278 | 0.0480 | 0.9894 | 0.9904 | 0.9899 | 0.9901 |
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| 0.0675 | 7.0 | 1491 | 0.0473 | 0.9919 | 0.9904 | 0.9912 | 0.9911 |
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| 0.0426 | 8.0 | 1704 | 0.0441 | 0.9921 | 0.9916 | 0.9919 | 0.9921 |
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| 0.0426 | 9.0 | 1917 | 0.0426 | 0.9921 | 0.9916 | 0.9919 | 0.9922 |
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| 0.033 | 10.0 | 2130 | 0.0420 | 0.9914 | 0.9909 | 0.9912 | 0.9920 |
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### Framework versions
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- Transformers 4.17.0
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- Pytorch 1.10.0+cu111
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- Datasets 2.0.0
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- Tokenizers 0.11.6
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