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--- |
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tags: |
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- generated_from_trainer |
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datasets: |
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- null |
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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: bert-srb-ner-setimes |
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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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metric: |
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name: Accuracy |
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type: accuracy |
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value: 0.9632618605436434 |
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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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# bert-srb-ner-setimes |
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This model was trained from scratch on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1682 |
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- Precision: 0.8153 |
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- Recall: 0.8353 |
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- F1: 0.8251 |
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- Accuracy: 0.9633 |
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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: 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 | 207 | 0.2003 | 0.6991 | 0.7440 | 0.7209 | 0.9415 | |
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| No log | 2.0 | 414 | 0.1597 | 0.7202 | 0.7770 | 0.7475 | 0.9509 | |
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| 0.2342 | 3.0 | 621 | 0.1505 | 0.7619 | 0.8012 | 0.7811 | 0.9558 | |
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| 0.2342 | 4.0 | 828 | 0.1710 | 0.7822 | 0.8020 | 0.7920 | 0.9564 | |
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| 0.082 | 5.0 | 1035 | 0.1434 | 0.7969 | 0.8277 | 0.8120 | 0.9614 | |
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| 0.082 | 6.0 | 1242 | 0.1526 | 0.8001 | 0.8350 | 0.8171 | 0.9625 | |
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| 0.082 | 7.0 | 1449 | 0.1590 | 0.8061 | 0.8384 | 0.8219 | 0.9633 | |
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| 0.0353 | 8.0 | 1656 | 0.1616 | 0.8143 | 0.8296 | 0.8219 | 0.9634 | |
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| 0.0353 | 9.0 | 1863 | 0.1684 | 0.8167 | 0.8367 | 0.8265 | 0.9631 | |
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| 0.017 | 10.0 | 2070 | 0.1682 | 0.8153 | 0.8353 | 0.8251 | 0.9633 | |
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### Framework versions |
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- Transformers 4.9.2 |
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- Pytorch 1.9.0 |
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- Datasets 1.11.0 |
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- Tokenizers 0.10.1 |
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