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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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datasets: |
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- wnut_17 |
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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-WNUT-ner |
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results: |
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- task: |
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name: Token Classification |
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type: token-classification |
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dataset: |
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name: wnut_17 |
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type: wnut_17 |
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config: wnut_17 |
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split: test |
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args: wnut_17 |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.6251511487303507 |
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- name: Recall |
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type: recall |
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value: 0.47914735866543096 |
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- name: F1 |
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type: f1 |
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value: 0.5424973767051418 |
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- name: Accuracy |
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type: accuracy |
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value: 0.952295460374455 |
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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-WNUT-ner |
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This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the wnut_17 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3376 |
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- Precision: 0.6252 |
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- Recall: 0.4791 |
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- F1: 0.5425 |
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- Accuracy: 0.9523 |
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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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| No log | 1.0 | 213 | 0.2787 | 0.5650 | 0.3383 | 0.4232 | 0.9418 | |
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| No log | 2.0 | 426 | 0.2535 | 0.6225 | 0.4004 | 0.4873 | 0.9485 | |
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| 0.177 | 3.0 | 639 | 0.2773 | 0.6594 | 0.3911 | 0.4910 | 0.9497 | |
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| 0.177 | 4.0 | 852 | 0.2651 | 0.6098 | 0.4708 | 0.5314 | 0.9526 | |
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| 0.0551 | 5.0 | 1065 | 0.3076 | 0.6026 | 0.4652 | 0.5251 | 0.9514 | |
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| 0.0551 | 6.0 | 1278 | 0.3031 | 0.6343 | 0.4662 | 0.5374 | 0.9531 | |
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| 0.0551 | 7.0 | 1491 | 0.3319 | 0.6336 | 0.4680 | 0.5384 | 0.9523 | |
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| 0.0276 | 8.0 | 1704 | 0.3430 | 0.6508 | 0.4560 | 0.5362 | 0.9526 | |
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| 0.0276 | 9.0 | 1917 | 0.3342 | 0.6138 | 0.4773 | 0.5370 | 0.9521 | |
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| 0.0157 | 10.0 | 2130 | 0.3376 | 0.6252 | 0.4791 | 0.5425 | 0.9523 | |
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### Framework versions |
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- Transformers 4.26.0 |
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- Pytorch 1.13.1+cu117 |
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- Datasets 2.9.0 |
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- Tokenizers 0.13.2 |
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