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lobrien001
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roberta-base-finetuned-ner

Token Classification
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roberta
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  1. README.md +69 -37
  2. model.safetensors +1 -1
  3. training_args.bin +1 -1
README.md CHANGED
@@ -20,11 +20,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.4998
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- - Precision: 0.8686
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- - Recall: 0.8991
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- - F1: 0.8836
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- - Accuracy: 0.8638
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  ## Model description
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@@ -55,38 +55,70 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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- | No log | 0.37 | 100 | 0.5660 | 0.8574 | 0.9049 | 0.8805 | 0.8574 |
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- | No log | 0.75 | 200 | 0.5733 | 0.8574 | 0.9049 | 0.8805 | 0.8574 |
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- | No log | 1.12 | 300 | 0.5599 | 0.8574 | 0.9049 | 0.8805 | 0.8574 |
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- | No log | 1.49 | 400 | 0.5609 | 0.8574 | 0.9049 | 0.8805 | 0.8574 |
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- | 0.6419 | 1.87 | 500 | 0.5470 | 0.8574 | 0.9049 | 0.8805 | 0.8574 |
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- | 0.6419 | 2.24 | 600 | 0.5481 | 0.8574 | 0.9049 | 0.8805 | 0.8574 |
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- | 0.6419 | 2.61 | 700 | 0.5178 | 0.8574 | 0.9049 | 0.8805 | 0.8574 |
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- | 0.6419 | 2.99 | 800 | 0.5212 | 0.8574 | 0.9049 | 0.8805 | 0.8574 |
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- | 0.6419 | 3.36 | 900 | 0.5148 | 0.8604 | 0.9039 | 0.8816 | 0.8601 |
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- | 0.5913 | 3.73 | 1000 | 0.5056 | 0.8607 | 0.9058 | 0.8827 | 0.8603 |
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- | 0.5913 | 4.1 | 1100 | 0.5169 | 0.8595 | 0.9019 | 0.8802 | 0.8560 |
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- | 0.5913 | 4.48 | 1200 | 0.4942 | 0.8606 | 0.9049 | 0.8822 | 0.8599 |
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- | 0.5913 | 4.85 | 1300 | 0.5351 | 0.8588 | 0.9047 | 0.8812 | 0.8578 |
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- | 0.5913 | 5.22 | 1400 | 0.4681 | 0.8671 | 0.8997 | 0.8831 | 0.8611 |
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- | 0.5377 | 5.6 | 1500 | 0.5056 | 0.8713 | 0.8828 | 0.8771 | 0.8519 |
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- | 0.5377 | 5.97 | 1600 | 0.4747 | 0.8622 | 0.9008 | 0.8811 | 0.8599 |
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- | 0.5377 | 6.34 | 1700 | 0.4828 | 0.8661 | 0.9017 | 0.8835 | 0.8615 |
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- | 0.5377 | 6.72 | 1800 | 0.4807 | 0.8737 | 0.8841 | 0.8789 | 0.8554 |
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- | 0.5377 | 7.09 | 1900 | 0.4756 | 0.8686 | 0.8900 | 0.8791 | 0.8560 |
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- | 0.4764 | 7.46 | 2000 | 0.5021 | 0.8719 | 0.8828 | 0.8773 | 0.8576 |
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- | 0.4764 | 7.84 | 2100 | 0.4812 | 0.8690 | 0.8807 | 0.8748 | 0.8500 |
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- | 0.4764 | 8.21 | 2200 | 0.4949 | 0.8633 | 0.8781 | 0.8706 | 0.8482 |
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- | 0.4764 | 8.58 | 2300 | 0.5022 | 0.8694 | 0.8805 | 0.8749 | 0.8527 |
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- | 0.4764 | 8.96 | 2400 | 0.4998 | 0.8686 | 0.8991 | 0.8836 | 0.8638 |
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- | 0.429 | 9.33 | 2500 | 0.5103 | 0.8683 | 0.8811 | 0.8747 | 0.8527 |
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- | 0.429 | 9.7 | 2600 | 0.5269 | 0.8690 | 0.8735 | 0.8713 | 0.8500 |
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- | 0.429 | 10.07 | 2700 | 0.5303 | 0.8662 | 0.8677 | 0.8669 | 0.8437 |
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- | 0.429 | 10.45 | 2800 | 0.5402 | 0.8639 | 0.8703 | 0.8671 | 0.8451 |
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- | 0.429 | 10.82 | 2900 | 0.5407 | 0.8664 | 0.8774 | 0.8719 | 0.8500 |
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- | 0.3833 | 11.19 | 3000 | 0.5413 | 0.8654 | 0.8744 | 0.8699 | 0.8474 |
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- | 0.3833 | 11.57 | 3100 | 0.5447 | 0.8647 | 0.8718 | 0.8682 | 0.8457 |
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- | 0.3833 | 11.94 | 3200 | 0.5420 | 0.8632 | 0.8707 | 0.8670 | 0.8457 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.5055
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+ - Precision: 0.8737
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+ - Recall: 0.8677
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+ - F1: 0.8707
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+ - Accuracy: 0.8449
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | No log | 0.19 | 50 | 0.5675 | 0.8574 | 0.9049 | 0.8805 | 0.8574 |
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+ | No log | 0.37 | 100 | 0.5571 | 0.8574 | 0.9049 | 0.8805 | 0.8574 |
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+ | No log | 0.56 | 150 | 0.5541 | 0.8574 | 0.9049 | 0.8805 | 0.8574 |
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+ | No log | 0.75 | 200 | 0.5682 | 0.8574 | 0.9049 | 0.8805 | 0.8574 |
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+ | No log | 0.93 | 250 | 0.5845 | 0.8574 | 0.9049 | 0.8805 | 0.8574 |
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+ | No log | 1.12 | 300 | 0.5533 | 0.8574 | 0.9049 | 0.8805 | 0.8574 |
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+ | No log | 1.31 | 350 | 0.5940 | 0.8574 | 0.9049 | 0.8805 | 0.8574 |
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+ | No log | 1.49 | 400 | 0.5553 | 0.8574 | 0.9049 | 0.8805 | 0.8574 |
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+ | No log | 1.68 | 450 | 0.5661 | 0.8574 | 0.9049 | 0.8805 | 0.8574 |
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+ | 0.6392 | 1.87 | 500 | 0.5435 | 0.8574 | 0.9049 | 0.8805 | 0.8574 |
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+ | 0.6392 | 2.05 | 550 | 0.5300 | 0.8574 | 0.9049 | 0.8805 | 0.8574 |
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+ | 0.6392 | 2.24 | 600 | 0.5522 | 0.8574 | 0.9049 | 0.8805 | 0.8574 |
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+ | 0.6392 | 2.43 | 650 | 0.5155 | 0.8574 | 0.9049 | 0.8805 | 0.8574 |
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+ | 0.6392 | 2.61 | 700 | 0.5037 | 0.8574 | 0.9049 | 0.8805 | 0.8574 |
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+ | 0.6392 | 2.8 | 750 | 0.4923 | 0.8574 | 0.9049 | 0.8805 | 0.8574 |
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+ | 0.6392 | 2.99 | 800 | 0.4897 | 0.8574 | 0.9049 | 0.8805 | 0.8574 |
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+ | 0.6392 | 3.17 | 850 | 0.5021 | 0.8574 | 0.9049 | 0.8805 | 0.8574 |
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+ | 0.6392 | 3.36 | 900 | 0.5122 | 0.8574 | 0.9049 | 0.8805 | 0.8574 |
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+ | 0.6392 | 3.54 | 950 | 0.4987 | 0.8575 | 0.9004 | 0.8784 | 0.8560 |
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+ | 0.5724 | 3.73 | 1000 | 0.4861 | 0.8587 | 0.8971 | 0.8775 | 0.8541 |
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+ | 0.5724 | 3.92 | 1050 | 0.4788 | 0.8607 | 0.9019 | 0.8808 | 0.8580 |
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+ | 0.5724 | 4.1 | 1100 | 0.4989 | 0.8634 | 0.8826 | 0.8729 | 0.8459 |
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+ | 0.5724 | 4.29 | 1150 | 0.4760 | 0.8653 | 0.8976 | 0.8812 | 0.8572 |
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+ | 0.5724 | 4.48 | 1200 | 0.4699 | 0.8659 | 0.8835 | 0.8746 | 0.8482 |
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+ | 0.5724 | 4.66 | 1250 | 0.4865 | 0.8729 | 0.8822 | 0.8775 | 0.8519 |
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+ | 0.5724 | 4.85 | 1300 | 0.4763 | 0.8626 | 0.9023 | 0.8820 | 0.8586 |
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+ | 0.5724 | 5.04 | 1350 | 0.4676 | 0.8653 | 0.8941 | 0.8794 | 0.8564 |
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+ | 0.5724 | 5.22 | 1400 | 0.4979 | 0.8672 | 0.8850 | 0.8760 | 0.8494 |
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+ | 0.5724 | 5.41 | 1450 | 0.4749 | 0.8648 | 0.8965 | 0.8804 | 0.8566 |
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+ | 0.5092 | 5.6 | 1500 | 0.5003 | 0.8686 | 0.8720 | 0.8703 | 0.8410 |
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+ | 0.5092 | 5.78 | 1550 | 0.4635 | 0.8713 | 0.8872 | 0.8792 | 0.8547 |
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+ | 0.5092 | 5.97 | 1600 | 0.4615 | 0.8653 | 0.8928 | 0.8788 | 0.8543 |
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+ | 0.5092 | 6.16 | 1650 | 0.4785 | 0.8677 | 0.8937 | 0.8805 | 0.8556 |
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+ | 0.5092 | 6.34 | 1700 | 0.4856 | 0.8728 | 0.8813 | 0.8771 | 0.8535 |
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+ | 0.5092 | 6.53 | 1750 | 0.4681 | 0.8695 | 0.8917 | 0.8805 | 0.8574 |
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+ | 0.5092 | 6.72 | 1800 | 0.4633 | 0.8683 | 0.8950 | 0.8814 | 0.8586 |
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+ | 0.5092 | 6.9 | 1850 | 0.4887 | 0.8787 | 0.8655 | 0.8720 | 0.8432 |
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+ | 0.5092 | 7.09 | 1900 | 0.4807 | 0.8706 | 0.8759 | 0.8733 | 0.8476 |
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+ | 0.5092 | 7.28 | 1950 | 0.4613 | 0.8723 | 0.8935 | 0.8828 | 0.8607 |
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+ | 0.4572 | 7.46 | 2000 | 0.4582 | 0.8729 | 0.8861 | 0.8794 | 0.8545 |
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+ | 0.4572 | 7.65 | 2050 | 0.4784 | 0.8794 | 0.8681 | 0.8737 | 0.8476 |
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+ | 0.4572 | 7.84 | 2100 | 0.4749 | 0.8710 | 0.8798 | 0.8754 | 0.8504 |
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+ | 0.4572 | 8.02 | 2150 | 0.4755 | 0.8721 | 0.8828 | 0.8774 | 0.8531 |
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+ | 0.4572 | 8.21 | 2200 | 0.4875 | 0.8736 | 0.8668 | 0.8702 | 0.8463 |
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+ | 0.4572 | 8.4 | 2250 | 0.4763 | 0.8807 | 0.8664 | 0.8735 | 0.8480 |
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+ | 0.4572 | 8.58 | 2300 | 0.4795 | 0.8745 | 0.8644 | 0.8694 | 0.8445 |
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+ | 0.4572 | 8.77 | 2350 | 0.4822 | 0.8739 | 0.8616 | 0.8677 | 0.8385 |
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+ | 0.4572 | 8.96 | 2400 | 0.4824 | 0.8761 | 0.8774 | 0.8768 | 0.8510 |
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+ | 0.4572 | 9.14 | 2450 | 0.4818 | 0.8748 | 0.8608 | 0.8677 | 0.8400 |
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+ | 0.4061 | 9.33 | 2500 | 0.4814 | 0.8795 | 0.8712 | 0.8753 | 0.8488 |
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+ | 0.4061 | 9.51 | 2550 | 0.4846 | 0.8754 | 0.8796 | 0.8775 | 0.8510 |
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+ | 0.4061 | 9.7 | 2600 | 0.5112 | 0.8758 | 0.8718 | 0.8738 | 0.8461 |
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+ | 0.4061 | 9.89 | 2650 | 0.5002 | 0.8689 | 0.8701 | 0.8695 | 0.8461 |
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+ | 0.4061 | 10.07 | 2700 | 0.5163 | 0.8769 | 0.8605 | 0.8686 | 0.8391 |
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+ | 0.4061 | 10.26 | 2750 | 0.4947 | 0.8733 | 0.8774 | 0.8754 | 0.8510 |
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+ | 0.4061 | 10.45 | 2800 | 0.4895 | 0.8795 | 0.8850 | 0.8822 | 0.8599 |
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+ | 0.4061 | 10.63 | 2850 | 0.4984 | 0.8737 | 0.8705 | 0.8721 | 0.8457 |
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+ | 0.4061 | 10.82 | 2900 | 0.4952 | 0.8733 | 0.8779 | 0.8756 | 0.8521 |
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+ | 0.4061 | 11.01 | 2950 | 0.5012 | 0.8720 | 0.8644 | 0.8682 | 0.8422 |
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+ | 0.3677 | 11.19 | 3000 | 0.4994 | 0.8717 | 0.8751 | 0.8734 | 0.8486 |
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+ | 0.3677 | 11.38 | 3050 | 0.5002 | 0.875 | 0.8777 | 0.8763 | 0.8529 |
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+ | 0.3677 | 11.57 | 3100 | 0.5039 | 0.8724 | 0.8735 | 0.8730 | 0.8490 |
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+ | 0.3677 | 11.75 | 3150 | 0.5094 | 0.8729 | 0.8642 | 0.8686 | 0.8416 |
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+ | 0.3677 | 11.94 | 3200 | 0.5059 | 0.8731 | 0.8673 | 0.8702 | 0.8443 |
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  ### Framework versions
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