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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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- 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: hmBERT-CoNLL-cp3 |
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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: 0.9121408403919614 |
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- name: Recall |
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type: recall |
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value: 0.9242679232581622 |
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- name: F1 |
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type: f1 |
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value: 0.9181643400484828 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9862154900510105 |
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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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# hmBERT-CoNLL-cp3 |
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This model is a fine-tuned version of [dbmdz/bert-base-historic-multilingual-cased](https://huggingface.co/dbmdz/bert-base-historic-multilingual-cased) on the conll2003 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0572 |
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- Precision: 0.9121 |
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- Recall: 0.9243 |
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- F1: 0.9182 |
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- Accuracy: 0.9862 |
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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: 5e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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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: 3 |
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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 | 0.06 | 25 | 0.4115 | 0.3643 | 0.3728 | 0.3685 | 0.9007 | |
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| No log | 0.11 | 50 | 0.2243 | 0.6393 | 0.6908 | 0.6641 | 0.9460 | |
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| No log | 0.17 | 75 | 0.1617 | 0.7319 | 0.7637 | 0.7475 | 0.9580 | |
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| No log | 0.23 | 100 | 0.1544 | 0.7282 | 0.7637 | 0.7455 | 0.9585 | |
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| No log | 0.28 | 125 | 0.1341 | 0.7595 | 0.8117 | 0.7847 | 0.9644 | |
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| No log | 0.34 | 150 | 0.1221 | 0.7980 | 0.8251 | 0.8114 | 0.9693 | |
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| No log | 0.4 | 175 | 0.1013 | 0.7968 | 0.8344 | 0.8152 | 0.9719 | |
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| No log | 0.46 | 200 | 0.1076 | 0.8265 | 0.8403 | 0.8333 | 0.9732 | |
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| No log | 0.51 | 225 | 0.0883 | 0.8453 | 0.8635 | 0.8543 | 0.9763 | |
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| No log | 0.57 | 250 | 0.0973 | 0.8439 | 0.8633 | 0.8535 | 0.9763 | |
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| No log | 0.63 | 275 | 0.0883 | 0.8497 | 0.8655 | 0.8575 | 0.9765 | |
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| No log | 0.68 | 300 | 0.0879 | 0.8462 | 0.8642 | 0.8551 | 0.9766 | |
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| No log | 0.74 | 325 | 0.0781 | 0.8592 | 0.8834 | 0.8711 | 0.9787 | |
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| No log | 0.8 | 350 | 0.0725 | 0.8697 | 0.8928 | 0.8811 | 0.9803 | |
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| No log | 0.85 | 375 | 0.0755 | 0.8687 | 0.8943 | 0.8813 | 0.9807 | |
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| No log | 0.91 | 400 | 0.0666 | 0.8781 | 0.9004 | 0.8891 | 0.9822 | |
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| No log | 0.97 | 425 | 0.0658 | 0.8877 | 0.8995 | 0.8936 | 0.9823 | |
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| No log | 1.03 | 450 | 0.0645 | 0.8951 | 0.9036 | 0.8993 | 0.9837 | |
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| No log | 1.08 | 475 | 0.0697 | 0.8864 | 0.9039 | 0.8951 | 0.9831 | |
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| 0.1392 | 1.14 | 500 | 0.0688 | 0.8824 | 0.8994 | 0.8908 | 0.9824 | |
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| 0.1392 | 1.2 | 525 | 0.0681 | 0.8950 | 0.9049 | 0.8999 | 0.9827 | |
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| 0.1392 | 1.25 | 550 | 0.0676 | 0.8855 | 0.8977 | 0.8915 | 0.9823 | |
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| 0.1392 | 1.31 | 575 | 0.0618 | 0.8940 | 0.9088 | 0.9014 | 0.9842 | |
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| 0.1392 | 1.37 | 600 | 0.0644 | 0.8945 | 0.9076 | 0.9010 | 0.9840 | |
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| 0.1392 | 1.42 | 625 | 0.0641 | 0.8936 | 0.9086 | 0.9010 | 0.9837 | |
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| 0.1392 | 1.48 | 650 | 0.0619 | 0.8969 | 0.9120 | 0.9044 | 0.9846 | |
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| 0.1392 | 1.54 | 675 | 0.0608 | 0.9045 | 0.9105 | 0.9075 | 0.9848 | |
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| 0.1392 | 1.59 | 700 | 0.0624 | 0.9038 | 0.9143 | 0.9091 | 0.9851 | |
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| 0.1392 | 1.65 | 725 | 0.0596 | 0.9062 | 0.9170 | 0.9116 | 0.9852 | |
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| 0.1392 | 1.71 | 750 | 0.0580 | 0.8995 | 0.9143 | 0.9069 | 0.9848 | |
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| 0.1392 | 1.77 | 775 | 0.0582 | 0.9082 | 0.9172 | 0.9127 | 0.9858 | |
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| 0.1392 | 1.82 | 800 | 0.0588 | 0.9024 | 0.9179 | 0.9101 | 0.9852 | |
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| 0.1392 | 1.88 | 825 | 0.0592 | 0.9020 | 0.9219 | 0.9119 | 0.9856 | |
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| 0.1392 | 1.94 | 850 | 0.0600 | 0.9054 | 0.9182 | 0.9118 | 0.9852 | |
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| 0.1392 | 1.99 | 875 | 0.0568 | 0.9068 | 0.9202 | 0.9135 | 0.9861 | |
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| 0.1392 | 2.05 | 900 | 0.0571 | 0.9131 | 0.9212 | 0.9171 | 0.9861 | |
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| 0.1392 | 2.11 | 925 | 0.0577 | 0.9110 | 0.9204 | 0.9157 | 0.9858 | |
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| 0.1392 | 2.16 | 950 | 0.0605 | 0.9127 | 0.9243 | 0.9185 | 0.9860 | |
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| 0.1392 | 2.22 | 975 | 0.0575 | 0.9109 | 0.9224 | 0.9166 | 0.9867 | |
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| 0.0392 | 2.28 | 1000 | 0.0572 | 0.9121 | 0.9243 | 0.9182 | 0.9862 | |
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| 0.0392 | 2.33 | 1025 | 0.0567 | 0.9171 | 0.9253 | 0.9212 | 0.9870 | |
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| 0.0392 | 2.39 | 1050 | 0.0570 | 0.9193 | 0.9295 | 0.9244 | 0.9871 | |
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| 0.0392 | 2.45 | 1075 | 0.0584 | 0.9155 | 0.9276 | 0.9215 | 0.9867 | |
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| 0.0392 | 2.51 | 1100 | 0.0591 | 0.9168 | 0.9286 | 0.9227 | 0.9867 | |
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| 0.0392 | 2.56 | 1125 | 0.0577 | 0.9182 | 0.9312 | 0.9246 | 0.9874 | |
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| 0.0392 | 2.62 | 1150 | 0.0570 | 0.9184 | 0.9283 | 0.9233 | 0.9870 | |
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| 0.0392 | 2.68 | 1175 | 0.0563 | 0.9191 | 0.9298 | 0.9245 | 0.9872 | |
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| 0.0392 | 2.73 | 1200 | 0.0565 | 0.9180 | 0.9313 | 0.9246 | 0.9872 | |
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| 0.0392 | 2.79 | 1225 | 0.0559 | 0.9190 | 0.9298 | 0.9244 | 0.9873 | |
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| 0.0392 | 2.85 | 1250 | 0.0562 | 0.9185 | 0.9293 | 0.9239 | 0.9873 | |
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| 0.0392 | 2.9 | 1275 | 0.0564 | 0.9175 | 0.9285 | 0.9230 | 0.9872 | |
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| 0.0392 | 2.96 | 1300 | 0.0563 | 0.9181 | 0.9295 | 0.9237 | 0.9873 | |
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### Framework versions |
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- Transformers 4.20.1 |
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- Pytorch 1.12.0 |
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- Datasets 2.4.0 |
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- Tokenizers 0.12.1 |
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