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--- |
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tags: |
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- generated_from_trainer |
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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: Finetuned-camem-ner |
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results: [] |
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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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# Finetuned-camem-ner |
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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: 0.1080 |
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- Precision: 0.8445 |
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- Recall: 0.8740 |
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- F1: 0.8590 |
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- Accuracy: 0.9793 |
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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: 1e-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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- distributed_type: multi-GPU |
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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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- lr_scheduler_warmup_steps: 1000 |
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- num_epochs: 10 |
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- mixed_precision_training: Native AMP |
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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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| 1.2864 | 0.09 | 100 | 1.2891 | 0.0295 | 0.1206 | 0.0474 | 0.8750 | |
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| 0.8284 | 0.17 | 200 | 0.5688 | 0.0376 | 0.1252 | 0.0579 | 0.8888 | |
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| 0.374 | 0.26 | 300 | 0.2753 | 0.1477 | 0.2320 | 0.1805 | 0.9366 | |
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| 0.2215 | 0.35 | 400 | 0.1742 | 0.3205 | 0.3816 | 0.3484 | 0.9584 | |
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| 0.1447 | 0.43 | 500 | 0.1271 | 0.6077 | 0.7105 | 0.6551 | 0.9735 | |
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| 0.1183 | 0.52 | 600 | 0.1067 | 0.7066 | 0.7857 | 0.7440 | 0.9773 | |
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| 0.108 | 0.61 | 700 | 0.0983 | 0.7236 | 0.8071 | 0.7631 | 0.9779 | |
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| 0.0978 | 0.69 | 800 | 0.0880 | 0.7678 | 0.8224 | 0.7942 | 0.9789 | |
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| 0.0897 | 0.78 | 900 | 0.0908 | 0.7970 | 0.8432 | 0.8195 | 0.9797 | |
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| 0.0799 | 0.87 | 1000 | 0.0883 | 0.8052 | 0.8587 | 0.8311 | 0.9799 | |
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| 0.0868 | 0.95 | 1100 | 0.0832 | 0.8073 | 0.8622 | 0.8338 | 0.9801 | |
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| 0.0749 | 1.04 | 1200 | 0.0832 | 0.8138 | 0.8651 | 0.8387 | 0.9800 | |
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| 0.0765 | 1.13 | 1300 | 0.0844 | 0.8139 | 0.8689 | 0.8405 | 0.9800 | |
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| 0.0712 | 1.21 | 1400 | 0.0835 | 0.8262 | 0.8636 | 0.8445 | 0.9800 | |
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| 0.0678 | 1.3 | 1500 | 0.0838 | 0.8228 | 0.8687 | 0.8451 | 0.9801 | |
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| 0.0699 | 1.39 | 1600 | 0.0850 | 0.8212 | 0.8714 | 0.8455 | 0.9800 | |
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| 0.0731 | 1.47 | 1700 | 0.0809 | 0.8272 | 0.8709 | 0.8485 | 0.9800 | |
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| 0.0704 | 1.56 | 1800 | 0.0818 | 0.8400 | 0.8697 | 0.8546 | 0.9803 | |
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| 0.0749 | 1.65 | 1900 | 0.0820 | 0.8330 | 0.8726 | 0.8523 | 0.9802 | |
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| 0.0723 | 1.73 | 2000 | 0.0814 | 0.8423 | 0.8709 | 0.8563 | 0.9802 | |
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| 0.0737 | 1.82 | 2100 | 0.0814 | 0.8312 | 0.8737 | 0.8519 | 0.9801 | |
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| 0.073 | 1.91 | 2200 | 0.0821 | 0.8347 | 0.8769 | 0.8553 | 0.9799 | |
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| 0.0617 | 1.99 | 2300 | 0.0830 | 0.8375 | 0.8760 | 0.8563 | 0.9801 | |
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| 0.0607 | 2.08 | 2400 | 0.0863 | 0.8295 | 0.8803 | 0.8541 | 0.9803 | |
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| 0.0578 | 2.17 | 2500 | 0.0849 | 0.8365 | 0.8797 | 0.8575 | 0.9803 | |
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| 0.0546 | 2.25 | 2600 | 0.0854 | 0.8376 | 0.8785 | 0.8576 | 0.9802 | |
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| 0.0634 | 2.34 | 2700 | 0.0832 | 0.8375 | 0.8764 | 0.8565 | 0.9801 | |
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| 0.058 | 2.43 | 2800 | 0.0852 | 0.8405 | 0.8748 | 0.8573 | 0.9802 | |
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| 0.0616 | 2.51 | 2900 | 0.0851 | 0.8378 | 0.8796 | 0.8582 | 0.9800 | |
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| 0.0585 | 2.6 | 3000 | 0.0845 | 0.8434 | 0.8785 | 0.8606 | 0.9800 | |
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| 0.0542 | 2.69 | 3100 | 0.0847 | 0.8471 | 0.8773 | 0.8619 | 0.9801 | |
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| 0.0617 | 2.77 | 3200 | 0.0869 | 0.8396 | 0.8765 | 0.8577 | 0.9799 | |
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| 0.0634 | 2.86 | 3300 | 0.0828 | 0.8338 | 0.8773 | 0.8550 | 0.9796 | |
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| 0.0593 | 2.95 | 3400 | 0.0855 | 0.8360 | 0.8789 | 0.8569 | 0.9798 | |
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| 0.0486 | 3.03 | 3500 | 0.0888 | 0.8439 | 0.8781 | 0.8606 | 0.9801 | |
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| 0.0549 | 3.12 | 3600 | 0.0886 | 0.8444 | 0.8793 | 0.8615 | 0.9798 | |
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| 0.0499 | 3.21 | 3700 | 0.0925 | 0.8462 | 0.8771 | 0.8613 | 0.9800 | |
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| 0.0484 | 3.29 | 3800 | 0.0913 | 0.8449 | 0.8773 | 0.8608 | 0.9798 | |
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| 0.049 | 3.38 | 3900 | 0.0927 | 0.8409 | 0.8774 | 0.8588 | 0.9796 | |
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| 0.05 | 3.47 | 4000 | 0.0900 | 0.8468 | 0.8780 | 0.8621 | 0.9800 | |
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| 0.0456 | 3.55 | 4100 | 0.0904 | 0.8464 | 0.8787 | 0.8623 | 0.9801 | |
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| 0.051 | 3.64 | 4200 | 0.0911 | 0.8411 | 0.8778 | 0.8591 | 0.9798 | |
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| 0.0507 | 3.73 | 4300 | 0.0921 | 0.8457 | 0.8768 | 0.8610 | 0.9797 | |
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| 0.0526 | 3.81 | 4400 | 0.0888 | 0.8453 | 0.8774 | 0.8610 | 0.9801 | |
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| 0.0494 | 3.9 | 4500 | 0.0892 | 0.8440 | 0.8785 | 0.8609 | 0.9800 | |
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| 0.0513 | 3.99 | 4600 | 0.0901 | 0.8392 | 0.8811 | 0.8597 | 0.9796 | |
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| 0.0479 | 4.07 | 4700 | 0.0914 | 0.8461 | 0.8781 | 0.8618 | 0.9798 | |
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| 0.0408 | 4.16 | 4800 | 0.0938 | 0.8518 | 0.8724 | 0.8620 | 0.9797 | |
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| 0.0446 | 4.25 | 4900 | 0.0926 | 0.8475 | 0.8766 | 0.8618 | 0.9797 | |
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| 0.0425 | 4.33 | 5000 | 0.0927 | 0.8434 | 0.8762 | 0.8595 | 0.9795 | |
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| 0.0428 | 4.42 | 5100 | 0.0966 | 0.8473 | 0.8788 | 0.8628 | 0.9799 | |
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| 0.045 | 4.51 | 5200 | 0.0941 | 0.8428 | 0.8787 | 0.8604 | 0.9795 | |
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| 0.0472 | 4.59 | 5300 | 0.0894 | 0.8436 | 0.8757 | 0.8593 | 0.9794 | |
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| 0.0436 | 4.68 | 5400 | 0.0961 | 0.8464 | 0.8755 | 0.8607 | 0.9800 | |
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| 0.0466 | 4.77 | 5500 | 0.0947 | 0.8451 | 0.8767 | 0.8606 | 0.9797 | |
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| 0.0438 | 4.85 | 5600 | 0.0951 | 0.8398 | 0.8779 | 0.8584 | 0.9795 | |
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| 0.0444 | 4.94 | 5700 | 0.0965 | 0.8431 | 0.8767 | 0.8596 | 0.9797 | |
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| 0.0444 | 5.03 | 5800 | 0.0929 | 0.8421 | 0.8780 | 0.8597 | 0.9798 | |
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| 0.0382 | 5.11 | 5900 | 0.0983 | 0.8460 | 0.8772 | 0.8613 | 0.9796 | |
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| 0.0388 | 5.2 | 6000 | 0.0979 | 0.8406 | 0.8806 | 0.8601 | 0.9797 | |
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| 0.0434 | 5.29 | 6100 | 0.0963 | 0.8463 | 0.8783 | 0.8620 | 0.9795 | |
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| 0.038 | 5.37 | 6200 | 0.0977 | 0.8457 | 0.8774 | 0.8612 | 0.9795 | |
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| 0.0406 | 5.46 | 6300 | 0.0970 | 0.8454 | 0.8780 | 0.8614 | 0.9796 | |
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| 0.0415 | 5.55 | 6400 | 0.0971 | 0.8442 | 0.8769 | 0.8602 | 0.9795 | |
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| 0.037 | 5.63 | 6500 | 0.1001 | 0.8448 | 0.8771 | 0.8607 | 0.9794 | |
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| 0.0375 | 5.72 | 6600 | 0.1000 | 0.8448 | 0.8744 | 0.8593 | 0.9794 | |
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| 0.0414 | 5.81 | 6700 | 0.0955 | 0.8478 | 0.8745 | 0.8609 | 0.9794 | |
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| 0.0422 | 5.89 | 6800 | 0.0966 | 0.8482 | 0.8746 | 0.8612 | 0.9794 | |
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| 0.04 | 5.98 | 6900 | 0.0995 | 0.8410 | 0.8776 | 0.8589 | 0.9795 | |
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| 0.0367 | 6.07 | 7000 | 0.1008 | 0.8460 | 0.8757 | 0.8606 | 0.9795 | |
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| 0.0385 | 6.15 | 7100 | 0.1025 | 0.8428 | 0.8766 | 0.8593 | 0.9793 | |
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| 0.039 | 6.24 | 7200 | 0.1003 | 0.8424 | 0.8766 | 0.8592 | 0.9794 | |
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| 0.0344 | 6.33 | 7300 | 0.1047 | 0.8421 | 0.8784 | 0.8599 | 0.9794 | |
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| 0.0346 | 6.41 | 7400 | 0.1022 | 0.8419 | 0.8780 | 0.8596 | 0.9793 | |
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| 0.0379 | 6.5 | 7500 | 0.0978 | 0.8467 | 0.8772 | 0.8617 | 0.9797 | |
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| 0.0358 | 6.59 | 7600 | 0.1018 | 0.8446 | 0.8767 | 0.8603 | 0.9792 | |
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| 0.0363 | 6.67 | 7700 | 0.1001 | 0.8432 | 0.8768 | 0.8597 | 0.9792 | |
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| 0.0378 | 6.76 | 7800 | 0.1030 | 0.8456 | 0.8767 | 0.8609 | 0.9794 | |
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| 0.0403 | 6.85 | 7900 | 0.0971 | 0.8418 | 0.8761 | 0.8586 | 0.9793 | |
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| 0.0352 | 6.93 | 8000 | 0.1035 | 0.8456 | 0.8757 | 0.8604 | 0.9793 | |
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| 0.0332 | 7.02 | 8100 | 0.1021 | 0.8450 | 0.8755 | 0.8600 | 0.9792 | |
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| 0.0371 | 7.11 | 8200 | 0.1032 | 0.8478 | 0.8746 | 0.8610 | 0.9794 | |
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| 0.034 | 7.19 | 8300 | 0.1037 | 0.8467 | 0.8738 | 0.8600 | 0.9794 | |
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| 0.033 | 7.28 | 8400 | 0.1037 | 0.8457 | 0.8747 | 0.8599 | 0.9793 | |
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| 0.0329 | 7.37 | 8500 | 0.1048 | 0.8459 | 0.8751 | 0.8602 | 0.9791 | |
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| 0.0317 | 7.45 | 8600 | 0.1074 | 0.8441 | 0.8757 | 0.8596 | 0.9792 | |
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| 0.0319 | 7.54 | 8700 | 0.1056 | 0.8437 | 0.8753 | 0.8592 | 0.9792 | |
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| 0.0335 | 7.63 | 8800 | 0.1034 | 0.8446 | 0.8736 | 0.8589 | 0.9793 | |
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| 0.0346 | 7.71 | 8900 | 0.1069 | 0.8461 | 0.8735 | 0.8596 | 0.9792 | |
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| 0.0342 | 7.8 | 9000 | 0.1031 | 0.8427 | 0.8757 | 0.8589 | 0.9793 | |
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| 0.0371 | 7.89 | 9100 | 0.1024 | 0.8438 | 0.8747 | 0.8590 | 0.9793 | |
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| 0.0384 | 7.97 | 9200 | 0.1032 | 0.8472 | 0.8746 | 0.8607 | 0.9795 | |
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| 0.0308 | 8.06 | 9300 | 0.1070 | 0.8449 | 0.8753 | 0.8598 | 0.9793 | |
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| 0.0318 | 8.15 | 9400 | 0.1070 | 0.8459 | 0.8738 | 0.8596 | 0.9794 | |
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| 0.0285 | 8.23 | 9500 | 0.1077 | 0.8474 | 0.8751 | 0.8610 | 0.9794 | |
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| 0.0334 | 8.32 | 9600 | 0.1066 | 0.8443 | 0.8757 | 0.8598 | 0.9793 | |
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| 0.0332 | 8.41 | 9700 | 0.1055 | 0.8462 | 0.8747 | 0.8602 | 0.9793 | |
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| 0.0341 | 8.49 | 9800 | 0.1056 | 0.8442 | 0.8749 | 0.8593 | 0.9793 | |
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| 0.0304 | 8.58 | 9900 | 0.1066 | 0.8447 | 0.8729 | 0.8586 | 0.9792 | |
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| 0.0353 | 8.67 | 10000 | 0.1057 | 0.8446 | 0.8741 | 0.8591 | 0.9792 | |
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| 0.0348 | 8.75 | 10100 | 0.1051 | 0.8443 | 0.8736 | 0.8587 | 0.9792 | |
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| 0.0326 | 8.84 | 10200 | 0.1047 | 0.8443 | 0.8757 | 0.8597 | 0.9793 | |
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| 0.0332 | 8.93 | 10300 | 0.1044 | 0.8461 | 0.8732 | 0.8594 | 0.9793 | |
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| 0.0328 | 9.01 | 10400 | 0.1053 | 0.8438 | 0.8744 | 0.8588 | 0.9792 | |
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| 0.0318 | 9.1 | 10500 | 0.1072 | 0.8415 | 0.8746 | 0.8577 | 0.9793 | |
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| 0.0296 | 9.19 | 10600 | 0.1084 | 0.8431 | 0.8743 | 0.8584 | 0.9793 | |
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| 0.0324 | 9.27 | 10700 | 0.1074 | 0.8448 | 0.8746 | 0.8594 | 0.9794 | |
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| 0.0326 | 9.36 | 10800 | 0.1080 | 0.8439 | 0.8752 | 0.8593 | 0.9793 | |
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| 0.0288 | 9.45 | 10900 | 0.1084 | 0.8451 | 0.8739 | 0.8593 | 0.9794 | |
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| 0.0314 | 9.53 | 11000 | 0.1082 | 0.8450 | 0.8746 | 0.8596 | 0.9794 | |
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| 0.0292 | 9.62 | 11100 | 0.1084 | 0.8446 | 0.8740 | 0.8590 | 0.9794 | |
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| 0.0328 | 9.71 | 11200 | 0.1080 | 0.8447 | 0.8741 | 0.8591 | 0.9794 | |
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| 0.0313 | 9.79 | 11300 | 0.1080 | 0.8439 | 0.8747 | 0.8590 | 0.9794 | |
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| 0.0295 | 9.88 | 11400 | 0.1080 | 0.8445 | 0.8739 | 0.8589 | 0.9793 | |
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| 0.0316 | 9.97 | 11500 | 0.1080 | 0.8445 | 0.8740 | 0.8590 | 0.9793 | |
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### Framework versions |
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- Transformers 4.39.3 |
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- Pytorch 2.1.2 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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