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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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- wikiann |
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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 |
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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: wikiann |
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type: wikiann |
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args: sr |
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metric: |
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name: Accuracy |
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type: accuracy |
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value: 0.9546696220907545 |
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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 |
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This model was trained from scratch on the wikiann dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3561 |
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- Precision: 0.8909 |
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- Recall: 0.9082 |
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- F1: 0.8995 |
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- Accuracy: 0.9547 |
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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: 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: 20 |
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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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| 0.3907 | 1.0 | 625 | 0.2316 | 0.8255 | 0.8314 | 0.8285 | 0.9259 | |
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| 0.2091 | 2.0 | 1250 | 0.1920 | 0.8598 | 0.8731 | 0.8664 | 0.9420 | |
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| 0.1562 | 3.0 | 1875 | 0.1833 | 0.8608 | 0.8820 | 0.8713 | 0.9441 | |
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| 0.0919 | 4.0 | 2500 | 0.1985 | 0.8712 | 0.8886 | 0.8798 | 0.9476 | |
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| 0.0625 | 5.0 | 3125 | 0.2195 | 0.8762 | 0.8923 | 0.8842 | 0.9485 | |
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| 0.0545 | 6.0 | 3750 | 0.2320 | 0.8706 | 0.9004 | 0.8852 | 0.9495 | |
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| 0.0403 | 7.0 | 4375 | 0.2459 | 0.8817 | 0.8957 | 0.8887 | 0.9505 | |
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| 0.0269 | 8.0 | 5000 | 0.2603 | 0.8813 | 0.9021 | 0.8916 | 0.9516 | |
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| 0.0193 | 9.0 | 5625 | 0.2916 | 0.8812 | 0.8949 | 0.8880 | 0.9500 | |
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| 0.0162 | 10.0 | 6250 | 0.2938 | 0.8814 | 0.9025 | 0.8918 | 0.9520 | |
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| 0.0134 | 11.0 | 6875 | 0.3330 | 0.8809 | 0.8961 | 0.8885 | 0.9497 | |
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| 0.0076 | 12.0 | 7500 | 0.3141 | 0.8840 | 0.9025 | 0.8932 | 0.9524 | |
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| 0.0069 | 13.0 | 8125 | 0.3292 | 0.8819 | 0.9065 | 0.8940 | 0.9535 | |
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| 0.0053 | 14.0 | 8750 | 0.3454 | 0.8844 | 0.9018 | 0.8930 | 0.9523 | |
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| 0.0038 | 15.0 | 9375 | 0.3519 | 0.8912 | 0.9061 | 0.8986 | 0.9539 | |
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| 0.0034 | 16.0 | 10000 | 0.3437 | 0.8894 | 0.9038 | 0.8965 | 0.9539 | |
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| 0.0024 | 17.0 | 10625 | 0.3518 | 0.8896 | 0.9072 | 0.8983 | 0.9543 | |
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| 0.0018 | 18.0 | 11250 | 0.3572 | 0.8877 | 0.9072 | 0.8973 | 0.9543 | |
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| 0.0015 | 19.0 | 11875 | 0.3554 | 0.8910 | 0.9081 | 0.8994 | 0.9549 | |
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| 0.0011 | 20.0 | 12500 | 0.3561 | 0.8909 | 0.9082 | 0.8995 | 0.9547 | |
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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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