| --- |
| license: mit |
| tags: |
| - generated_from_trainer |
| datasets: |
| - conll2003 |
| metrics: |
| - precision |
| - recall |
| - f1 |
| - accuracy |
| model-index: |
| - name: hmBERT-CoNLL-cp2 |
| results: |
| - task: |
| name: Token Classification |
| type: token-classification |
| dataset: |
| name: conll2003 |
| type: conll2003 |
| args: conll2003 |
| metrics: |
| - name: Precision |
| type: precision |
| value: 0.8931730929727926 |
| - name: Recall |
| type: recall |
| value: 0.9005385392123864 |
| - name: F1 |
| type: f1 |
| value: 0.8968406938741306 |
| - name: Accuracy |
| type: accuracy |
| value: 0.983217164440637 |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # hmBERT-CoNLL-cp2 |
|
|
| 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. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.0666 |
| - Precision: 0.8932 |
| - Recall: 0.9005 |
| - F1: 0.8968 |
| - Accuracy: 0.9832 |
|
|
| ## Model description |
|
|
| More information needed |
|
|
| ## Intended uses & limitations |
|
|
| More information needed |
|
|
| ## Training and evaluation data |
|
|
| More information needed |
|
|
| ## Training procedure |
|
|
| ### Training hyperparameters |
|
|
| The following hyperparameters were used during training: |
| - learning_rate: 5e-05 |
| - train_batch_size: 32 |
| - eval_batch_size: 32 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 2 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | No log | 0.06 | 25 | 0.4116 | 0.3632 | 0.3718 | 0.3674 | 0.9005 | |
| | No log | 0.11 | 50 | 0.2247 | 0.6384 | 0.6902 | 0.6633 | 0.9459 | |
| | No log | 0.17 | 75 | 0.1624 | 0.7303 | 0.7627 | 0.7461 | 0.9580 | |
| | No log | 0.23 | 100 | 0.1541 | 0.7338 | 0.7688 | 0.7509 | 0.9588 | |
| | No log | 0.28 | 125 | 0.1349 | 0.7610 | 0.8095 | 0.7845 | 0.9643 | |
| | No log | 0.34 | 150 | 0.1230 | 0.7982 | 0.8253 | 0.8115 | 0.9694 | |
| | No log | 0.4 | 175 | 0.0997 | 0.8069 | 0.8406 | 0.8234 | 0.9727 | |
| | No log | 0.46 | 200 | 0.1044 | 0.8211 | 0.8410 | 0.8309 | 0.9732 | |
| | No log | 0.51 | 225 | 0.0871 | 0.8413 | 0.8603 | 0.8507 | 0.9760 | |
| | No log | 0.57 | 250 | 0.1066 | 0.8288 | 0.8465 | 0.8376 | 0.9733 | |
| | No log | 0.63 | 275 | 0.0872 | 0.8580 | 0.8667 | 0.8624 | 0.9766 | |
| | No log | 0.68 | 300 | 0.0834 | 0.8522 | 0.8706 | 0.8613 | 0.9773 | |
| | No log | 0.74 | 325 | 0.0832 | 0.8545 | 0.8834 | 0.8687 | 0.9783 | |
| | No log | 0.8 | 350 | 0.0776 | 0.8542 | 0.8834 | 0.8685 | 0.9787 | |
| | No log | 0.85 | 375 | 0.0760 | 0.8629 | 0.8896 | 0.8760 | 0.9801 | |
| | No log | 0.91 | 400 | 0.0673 | 0.8775 | 0.9004 | 0.8888 | 0.9824 | |
| | No log | 0.97 | 425 | 0.0681 | 0.8827 | 0.8938 | 0.8882 | 0.9817 | |
| | No log | 1.03 | 450 | 0.0659 | 0.8844 | 0.8950 | 0.8897 | 0.9824 | |
| | No log | 1.08 | 475 | 0.0690 | 0.8833 | 0.9015 | 0.8923 | 0.9832 | |
| | 0.1399 | 1.14 | 500 | 0.0666 | 0.8932 | 0.9005 | 0.8968 | 0.9832 | |
| | 0.1399 | 1.2 | 525 | 0.0667 | 0.8891 | 0.8997 | 0.8944 | 0.9825 | |
| | 0.1399 | 1.25 | 550 | 0.0699 | 0.8751 | 0.8953 | 0.8851 | 0.9820 | |
| | 0.1399 | 1.31 | 575 | 0.0617 | 0.8947 | 0.9068 | 0.9007 | 0.9840 | |
| | 0.1399 | 1.37 | 600 | 0.0633 | 0.9 | 0.9058 | 0.9029 | 0.9841 | |
| | 0.1399 | 1.42 | 625 | 0.0639 | 0.8966 | 0.9116 | 0.9040 | 0.9843 | |
| | 0.1399 | 1.48 | 650 | 0.0624 | 0.8972 | 0.9110 | 0.9041 | 0.9845 | |
| | 0.1399 | 1.54 | 675 | 0.0619 | 0.8980 | 0.9081 | 0.9030 | 0.9842 | |
| | 0.1399 | 1.59 | 700 | 0.0615 | 0.9002 | 0.9090 | 0.9045 | 0.9843 | |
| | 0.1399 | 1.65 | 725 | 0.0601 | 0.9037 | 0.9128 | 0.9082 | 0.9850 | |
| | 0.1399 | 1.71 | 750 | 0.0585 | 0.9031 | 0.9142 | 0.9086 | 0.9849 | |
| | 0.1399 | 1.77 | 775 | 0.0582 | 0.9035 | 0.9143 | 0.9089 | 0.9851 | |
| | 0.1399 | 1.82 | 800 | 0.0580 | 0.9044 | 0.9157 | 0.9100 | 0.9853 | |
| | 0.1399 | 1.88 | 825 | 0.0583 | 0.9034 | 0.9160 | 0.9097 | 0.9851 | |
| | 0.1399 | 1.94 | 850 | 0.0578 | 0.9058 | 0.9170 | 0.9114 | 0.9854 | |
| | 0.1399 | 1.99 | 875 | 0.0576 | 0.9060 | 0.9165 | 0.9112 | 0.9852 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.20.1 |
| - Pytorch 1.12.0 |
| - Datasets 2.4.0 |
| - Tokenizers 0.12.1 |
|
|
