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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- conll2003 |
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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: test |
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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: conll2003 |
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type: conll2003 |
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args: conll2003 |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.7696078431372549 |
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- name: Recall |
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type: recall |
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value: 0.839572192513369 |
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- name: F1 |
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type: f1 |
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value: 0.8030690537084398 |
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- name: Accuracy |
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type: accuracy |
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value: 0.8847040737893928 |
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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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# test |
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This model is a fine-tuned version of [hfl/chinese-bert-wwm-ext](https://huggingface.co/hfl/chinese-bert-wwm-ext) on the conll2003 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.7372 |
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- Precision: 0.7696 |
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- Recall: 0.8396 |
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- F1: 0.8031 |
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- Accuracy: 0.8847 |
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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: 100 |
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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 | 2 | 1.9496 | 0.0 | 0.0 | 0.0 | 0.4889 | |
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| No log | 2.0 | 4 | 1.6137 | 0.0 | 0.0 | 0.0 | 0.4919 | |
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| No log | 3.0 | 6 | 1.3906 | 0.0 | 0.0 | 0.0 | 0.5650 | |
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| No log | 4.0 | 8 | 1.2273 | 0.0652 | 0.0481 | 0.0554 | 0.6856 | |
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| No log | 5.0 | 10 | 1.0565 | 0.2051 | 0.1711 | 0.1866 | 0.7125 | |
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| No log | 6.0 | 12 | 0.9150 | 0.5094 | 0.4332 | 0.4682 | 0.7540 | |
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| No log | 7.0 | 14 | 0.8051 | 0.5988 | 0.5187 | 0.5559 | 0.7679 | |
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| No log | 8.0 | 16 | 0.7151 | 0.6707 | 0.5989 | 0.6328 | 0.7763 | |
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| No log | 9.0 | 18 | 0.6334 | 0.6685 | 0.6364 | 0.6521 | 0.8086 | |
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| No log | 10.0 | 20 | 0.5693 | 0.6957 | 0.6845 | 0.6900 | 0.8201 | |
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| No log | 11.0 | 22 | 0.5192 | 0.7166 | 0.7166 | 0.7166 | 0.8363 | |
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| No log | 12.0 | 24 | 0.4736 | 0.7135 | 0.7326 | 0.7230 | 0.8524 | |
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| No log | 13.0 | 26 | 0.4448 | 0.6938 | 0.7754 | 0.7323 | 0.8555 | |
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| No log | 14.0 | 28 | 0.4280 | 0.7177 | 0.8021 | 0.7576 | 0.8586 | |
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| No log | 15.0 | 30 | 0.4179 | 0.7588 | 0.8075 | 0.7824 | 0.8663 | |
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| No log | 16.0 | 32 | 0.4214 | 0.7356 | 0.8182 | 0.7747 | 0.8593 | |
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| No log | 17.0 | 34 | 0.4070 | 0.7391 | 0.8182 | 0.7766 | 0.8616 | |
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| No log | 18.0 | 36 | 0.4112 | 0.7586 | 0.8235 | 0.7897 | 0.8724 | |
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| No log | 19.0 | 38 | 0.4530 | 0.7330 | 0.8075 | 0.7684 | 0.8693 | |
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| No log | 20.0 | 40 | 0.4719 | 0.7766 | 0.8182 | 0.7969 | 0.8732 | |
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| No log | 21.0 | 42 | 0.4886 | 0.7260 | 0.8075 | 0.7646 | 0.8632 | |
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| No log | 22.0 | 44 | 0.5007 | 0.7217 | 0.8182 | 0.7669 | 0.8701 | |
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| No log | 23.0 | 46 | 0.5169 | 0.7321 | 0.8182 | 0.7727 | 0.8762 | |
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| No log | 24.0 | 48 | 0.5531 | 0.7238 | 0.8128 | 0.7657 | 0.8724 | |
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| No log | 25.0 | 50 | 0.5895 | 0.7311 | 0.8289 | 0.7769 | 0.8655 | |
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| No log | 26.0 | 52 | 0.5482 | 0.7330 | 0.8075 | 0.7684 | 0.8778 | |
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| No log | 27.0 | 54 | 0.5361 | 0.7488 | 0.8128 | 0.7795 | 0.8832 | |
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| No log | 28.0 | 56 | 0.5378 | 0.7427 | 0.8182 | 0.7786 | 0.8847 | |
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| No log | 29.0 | 58 | 0.5543 | 0.7371 | 0.8396 | 0.7850 | 0.8824 | |
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| No log | 30.0 | 60 | 0.5564 | 0.7585 | 0.8396 | 0.7970 | 0.8839 | |
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| No log | 31.0 | 62 | 0.5829 | 0.7235 | 0.8396 | 0.7772 | 0.8724 | |
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| No log | 32.0 | 64 | 0.5974 | 0.7269 | 0.8396 | 0.7792 | 0.8716 | |
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| No log | 33.0 | 66 | 0.5750 | 0.7610 | 0.8342 | 0.7959 | 0.8839 | |
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| No log | 34.0 | 68 | 0.5887 | 0.7723 | 0.8342 | 0.8021 | 0.8878 | |
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| No log | 35.0 | 70 | 0.6219 | 0.7441 | 0.8396 | 0.7889 | 0.8747 | |
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| No log | 36.0 | 72 | 0.6676 | 0.7269 | 0.8396 | 0.7792 | 0.8632 | |
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| No log | 37.0 | 74 | 0.6517 | 0.7452 | 0.8289 | 0.7848 | 0.8693 | |
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| No log | 38.0 | 76 | 0.6346 | 0.7828 | 0.8289 | 0.8052 | 0.8862 | |
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| No log | 39.0 | 78 | 0.6239 | 0.7839 | 0.8342 | 0.8083 | 0.8855 | |
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| No log | 40.0 | 80 | 0.6360 | 0.7277 | 0.8289 | 0.775 | 0.8762 | |
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| No log | 41.0 | 82 | 0.6645 | 0.7336 | 0.8396 | 0.7830 | 0.8701 | |
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| No log | 42.0 | 84 | 0.6611 | 0.7406 | 0.8396 | 0.7870 | 0.8747 | |
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| No log | 43.0 | 86 | 0.6707 | 0.7488 | 0.8289 | 0.7868 | 0.8762 | |
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| No log | 44.0 | 88 | 0.6901 | 0.7277 | 0.8289 | 0.775 | 0.8709 | |
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| No log | 45.0 | 90 | 0.6911 | 0.7393 | 0.8342 | 0.7839 | 0.8709 | |
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| No log | 46.0 | 92 | 0.6540 | 0.7761 | 0.8342 | 0.8041 | 0.8878 | |
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| No log | 47.0 | 94 | 0.6381 | 0.7761 | 0.8342 | 0.8041 | 0.8916 | |
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| No log | 48.0 | 96 | 0.6285 | 0.7745 | 0.8449 | 0.8082 | 0.8885 | |
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| No log | 49.0 | 98 | 0.6449 | 0.7692 | 0.8556 | 0.8101 | 0.8862 | |
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| No log | 50.0 | 100 | 0.6809 | 0.7442 | 0.8556 | 0.7960 | 0.8732 | |
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| No log | 51.0 | 102 | 0.6898 | 0.7395 | 0.8503 | 0.7910 | 0.8716 | |
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| No log | 52.0 | 104 | 0.6897 | 0.75 | 0.8503 | 0.7970 | 0.8762 | |
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| No log | 53.0 | 106 | 0.6714 | 0.7656 | 0.8556 | 0.8081 | 0.8855 | |
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| No log | 54.0 | 108 | 0.6612 | 0.7692 | 0.8556 | 0.8101 | 0.8855 | |
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| No log | 55.0 | 110 | 0.6583 | 0.7692 | 0.8556 | 0.8101 | 0.8855 | |
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| No log | 56.0 | 112 | 0.6648 | 0.7692 | 0.8556 | 0.8101 | 0.8855 | |
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| No log | 57.0 | 114 | 0.6757 | 0.7656 | 0.8556 | 0.8081 | 0.8832 | |
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| No log | 58.0 | 116 | 0.6803 | 0.7656 | 0.8556 | 0.8081 | 0.8839 | |
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| No log | 59.0 | 118 | 0.6834 | 0.7692 | 0.8556 | 0.8101 | 0.8862 | |
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| No log | 60.0 | 120 | 0.6889 | 0.7833 | 0.8503 | 0.8154 | 0.8878 | |
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| No log | 61.0 | 122 | 0.6963 | 0.7772 | 0.8396 | 0.8072 | 0.8862 | |
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| No log | 62.0 | 124 | 0.7057 | 0.7772 | 0.8396 | 0.8072 | 0.8862 | |
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| No log | 63.0 | 126 | 0.7212 | 0.7910 | 0.8503 | 0.8196 | 0.8862 | |
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| No log | 64.0 | 128 | 0.7334 | 0.7833 | 0.8503 | 0.8154 | 0.8824 | |
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| No log | 65.0 | 130 | 0.7398 | 0.7833 | 0.8503 | 0.8154 | 0.8801 | |
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| No log | 66.0 | 132 | 0.7400 | 0.7833 | 0.8503 | 0.8154 | 0.8809 | |
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| No log | 67.0 | 134 | 0.7345 | 0.7783 | 0.8449 | 0.8103 | 0.8855 | |
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| No log | 68.0 | 136 | 0.7270 | 0.79 | 0.8449 | 0.8165 | 0.8870 | |
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| No log | 69.0 | 138 | 0.7245 | 0.7839 | 0.8342 | 0.8083 | 0.8862 | |
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| No log | 70.0 | 140 | 0.7260 | 0.7868 | 0.8289 | 0.8073 | 0.8847 | |
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| No log | 71.0 | 142 | 0.7275 | 0.7817 | 0.8235 | 0.8021 | 0.8839 | |
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| No log | 72.0 | 144 | 0.7283 | 0.7778 | 0.8235 | 0.8000 | 0.8832 | |
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| No log | 73.0 | 146 | 0.7296 | 0.78 | 0.8342 | 0.8062 | 0.8847 | |
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| No log | 74.0 | 148 | 0.7344 | 0.7734 | 0.8396 | 0.8051 | 0.8832 | |
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| No log | 75.0 | 150 | 0.7314 | 0.7745 | 0.8449 | 0.8082 | 0.8824 | |
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| No log | 76.0 | 152 | 0.7299 | 0.7794 | 0.8503 | 0.8133 | 0.8832 | |
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| No log | 77.0 | 154 | 0.7282 | 0.7794 | 0.8503 | 0.8133 | 0.8839 | |
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| No log | 78.0 | 156 | 0.7252 | 0.7783 | 0.8449 | 0.8103 | 0.8839 | |
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| No log | 79.0 | 158 | 0.7216 | 0.7756 | 0.8503 | 0.8112 | 0.8855 | |
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| No log | 80.0 | 160 | 0.7194 | 0.7756 | 0.8503 | 0.8112 | 0.8870 | |
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| No log | 81.0 | 162 | 0.7191 | 0.7756 | 0.8503 | 0.8112 | 0.8878 | |
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| No log | 82.0 | 164 | 0.7201 | 0.7696 | 0.8396 | 0.8031 | 0.8862 | |
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| No log | 83.0 | 166 | 0.7211 | 0.7696 | 0.8396 | 0.8031 | 0.8862 | |
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| No log | 84.0 | 168 | 0.7222 | 0.7696 | 0.8396 | 0.8031 | 0.8862 | |
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| No log | 85.0 | 170 | 0.7220 | 0.7696 | 0.8396 | 0.8031 | 0.8862 | |
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| No log | 86.0 | 172 | 0.7239 | 0.7734 | 0.8396 | 0.8051 | 0.8870 | |
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| No log | 87.0 | 174 | 0.7291 | 0.7772 | 0.8396 | 0.8072 | 0.8847 | |
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| No log | 88.0 | 176 | 0.7344 | 0.7745 | 0.8449 | 0.8082 | 0.8824 | |
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| No log | 89.0 | 178 | 0.7373 | 0.7745 | 0.8449 | 0.8082 | 0.8824 | |
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| No log | 90.0 | 180 | 0.7391 | 0.7707 | 0.8449 | 0.8061 | 0.8832 | |
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| No log | 91.0 | 182 | 0.7403 | 0.7745 | 0.8449 | 0.8082 | 0.8824 | |
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| No log | 92.0 | 184 | 0.7412 | 0.7745 | 0.8449 | 0.8082 | 0.8832 | |
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| No log | 93.0 | 186 | 0.7417 | 0.7707 | 0.8449 | 0.8061 | 0.8832 | |
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| No log | 94.0 | 188 | 0.7402 | 0.7745 | 0.8449 | 0.8082 | 0.8839 | |
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| No log | 95.0 | 190 | 0.7389 | 0.7745 | 0.8449 | 0.8082 | 0.8847 | |
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| No log | 96.0 | 192 | 0.7381 | 0.7696 | 0.8396 | 0.8031 | 0.8839 | |
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| No log | 97.0 | 194 | 0.7377 | 0.7696 | 0.8396 | 0.8031 | 0.8847 | |
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| No log | 98.0 | 196 | 0.7374 | 0.7696 | 0.8396 | 0.8031 | 0.8847 | |
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| No log | 99.0 | 198 | 0.7372 | 0.7696 | 0.8396 | 0.8031 | 0.8847 | |
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| No log | 100.0 | 200 | 0.7372 | 0.7696 | 0.8396 | 0.8031 | 0.8847 | |
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### Framework versions |
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- Transformers 4.18.0 |
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- Pytorch 1.11.0+cu113 |
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- Datasets 2.1.0 |
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- Tokenizers 0.12.1 |
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