--- license: cc-by-nc-sa-4.0 tags: - generated_from_trainer model-index: - name: lmv2-g-passport-197-doc-09-13 results: [] --- # lmv2-g-passport-197-doc-09-13 This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/microsoft/layoutlmv2-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0438 - Country Code Precision: 0.9412 - Country Code Recall: 0.9697 - Country Code F1: 0.9552 - Country Code Number: 33 - Date Of Birth Precision: 0.9714 - Date Of Birth Recall: 1.0 - Date Of Birth F1: 0.9855 - Date Of Birth Number: 34 - Date Of Expiry Precision: 1.0 - Date Of Expiry Recall: 1.0 - Date Of Expiry F1: 1.0 - Date Of Expiry Number: 36 - Date Of Issue Precision: 1.0 - Date Of Issue Recall: 1.0 - Date Of Issue F1: 1.0 - Date Of Issue Number: 36 - Given Name Precision: 0.9444 - Given Name Recall: 1.0 - Given Name F1: 0.9714 - Given Name Number: 34 - Nationality Precision: 0.9714 - Nationality Recall: 1.0 - Nationality F1: 0.9855 - Nationality Number: 34 - Passport No Precision: 0.9118 - Passport No Recall: 0.9688 - Passport No F1: 0.9394 - Passport No Number: 32 - Place Of Birth Precision: 1.0 - Place Of Birth Recall: 0.9730 - Place Of Birth F1: 0.9863 - Place Of Birth Number: 37 - Place Of Issue Precision: 1.0 - Place Of Issue Recall: 0.9722 - Place Of Issue F1: 0.9859 - Place Of Issue Number: 36 - Sex Precision: 0.9655 - Sex Recall: 0.9333 - Sex F1: 0.9492 - Sex Number: 30 - Surname Precision: 0.9259 - Surname Recall: 1.0 - Surname F1: 0.9615 - Surname Number: 25 - Type Precision: 1.0 - Type Recall: 1.0 - Type F1: 1.0 - Type Number: 27 - Overall Precision: 0.97 - Overall Recall: 0.9848 - Overall F1: 0.9773 - Overall Accuracy: 0.9941 ## 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: 4e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - num_epochs: 30 ### Training results | Training Loss | Epoch | Step | Validation Loss | Country Code Precision | Country Code Recall | Country Code F1 | Country Code Number | Date Of Birth Precision | Date Of Birth Recall | Date Of Birth F1 | Date Of Birth Number | Date Of Expiry Precision | Date Of Expiry Recall | Date Of Expiry F1 | Date Of Expiry Number | Date Of Issue Precision | Date Of Issue Recall | Date Of Issue F1 | Date Of Issue Number | Given Name Precision | Given Name Recall | Given Name F1 | Given Name Number | Nationality Precision | Nationality Recall | Nationality F1 | Nationality Number | Passport No Precision | Passport No Recall | Passport No F1 | Passport No Number | Place Of Birth Precision | Place Of Birth Recall | Place Of Birth F1 | Place Of Birth Number | Place Of Issue Precision | Place Of Issue Recall | Place Of Issue F1 | Place Of Issue Number | Sex Precision | Sex Recall | Sex F1 | Sex Number | Surname Precision | Surname Recall | Surname F1 | Surname Number | Type Precision | Type Recall | Type F1 | Type Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | 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| 1.6757 | 1.0 | 157 | 1.2569 | 0.0 | 0.0 | 0.0 | 33 | 0.0 | 0.0 | 0.0 | 34 | 0.2466 | 1.0 | 0.3956 | 36 | 0.0 | 0.0 | 0.0 | 36 | 0.0 | 0.0 | 0.0 | 34 | 0.0 | 0.0 | 0.0 | 34 | 0.0 | 0.0 | 0.0 | 32 | 0.0 | 0.0 | 0.0 | 37 | 0.0 | 0.0 | 0.0 | 36 | 0.0 | 0.0 | 0.0 | 30 | 0.0 | 0.0 | 0.0 | 25 | 0.0 | 0.0 | 0.0 | 27 | 0.2466 | 0.0914 | 0.1333 | 0.8446 | | 0.9214 | 2.0 | 314 | 0.5683 | 0.9394 | 0.9394 | 0.9394 | 33 | 0.9714 | 1.0 | 0.9855 | 34 | 1.0 | 1.0 | 1.0 | 36 | 1.0 | 1.0 | 1.0 | 36 | 0.5625 | 0.5294 | 0.5455 | 34 | 0.9714 | 1.0 | 0.9855 | 34 | 0.6098 | 0.7812 | 0.6849 | 32 | 0.9394 | 0.8378 | 0.8857 | 37 | 0.8293 | 0.9444 | 0.8831 | 36 | 1.0 | 0.9333 | 0.9655 | 30 | 0.6129 | 0.76 | 0.6786 | 25 | 1.0 | 0.8889 | 0.9412 | 27 | 0.8642 | 0.8883 | 0.8761 | 0.9777 | | 0.4452 | 3.0 | 471 | 0.3266 | 0.9412 | 0.9697 | 0.9552 | 33 | 0.9714 | 1.0 | 0.9855 | 34 | 1.0 | 1.0 | 1.0 | 36 | 1.0 | 1.0 | 1.0 | 36 | 0.5556 | 0.4412 | 0.4918 | 34 | 0.9714 | 1.0 | 0.9855 | 34 | 0.625 | 0.7812 | 0.6944 | 32 | 1.0 | 0.8108 | 0.8955 | 37 | 0.7556 | 0.9444 | 0.8395 | 36 | 0.9655 | 0.9333 | 0.9492 | 30 | 0.5556 | 0.8 | 0.6557 | 25 | 1.0 | 0.7037 | 0.8261 | 27 | 0.8532 | 0.8706 | 0.8618 | 0.9784 | | 0.2823 | 4.0 | 628 | 0.2215 | 0.9412 | 0.9697 | 0.9552 | 33 | 0.9714 | 1.0 | 0.9855 | 34 | 1.0 | 1.0 | 1.0 | 36 | 1.0 | 1.0 | 1.0 | 36 | 0.75 | 0.8824 | 0.8108 | 34 | 0.9714 | 1.0 | 0.9855 | 34 | 0.9118 | 0.9688 | 0.9394 | 32 | 1.0 | 0.8378 | 0.9118 | 37 | 0.9459 | 0.9722 | 0.9589 | 36 | 0.9333 | 0.9333 | 0.9333 | 30 | 0.75 | 0.96 | 0.8421 | 25 | 1.0 | 0.9630 | 0.9811 | 27 | 0.9286 | 0.9569 | 0.9425 | 0.9885 | | 0.2092 | 5.0 | 785 | 0.1633 | 0.9412 | 0.9697 | 0.9552 | 33 | 0.9714 | 1.0 | 0.9855 | 34 | 1.0 | 1.0 | 1.0 | 36 | 1.0 | 1.0 | 1.0 | 36 | 0.8889 | 0.9412 | 0.9143 | 34 | 0.9714 | 1.0 | 0.9855 | 34 | 0.8857 | 0.9688 | 0.9254 | 32 | 1.0 | 0.8649 | 0.9275 | 37 | 0.8974 | 0.9722 | 0.9333 | 36 | 1.0 | 0.9333 | 0.9655 | 30 | 0.8889 | 0.96 | 0.9231 | 25 | 1.0 | 1.0 | 1.0 | 27 | 0.9525 | 0.9670 | 0.9597 | 0.9918 | | 0.1593 | 6.0 | 942 | 0.1331 | 0.9412 | 0.9697 | 0.9552 | 33 | 0.9714 | 1.0 | 0.9855 | 34 | 1.0 | 1.0 | 1.0 | 36 | 0.9730 | 1.0 | 0.9863 | 36 | 0.8857 | 0.9118 | 0.8986 | 34 | 0.9714 | 1.0 | 0.9855 | 34 | 0.9118 | 0.9688 | 0.9394 | 32 | 0.9722 | 0.9459 | 0.9589 | 37 | 0.9722 | 0.9722 | 0.9722 | 36 | 1.0 | 0.9 | 0.9474 | 30 | 0.8571 | 0.96 | 0.9057 | 25 | 1.0 | 0.9630 | 0.9811 | 27 | 0.9549 | 0.9670 | 0.9609 | 0.9908 | | 0.1288 | 7.0 | 1099 | 0.1064 | 0.9412 | 0.9697 | 0.9552 | 33 | 0.9714 | 1.0 | 0.9855 | 34 | 1.0 | 1.0 | 1.0 | 36 | 1.0 | 1.0 | 1.0 | 36 | 0.9444 | 1.0 | 0.9714 | 34 | 0.9714 | 1.0 | 0.9855 | 34 | 0.9118 | 0.9688 | 0.9394 | 32 | 1.0 | 0.9730 | 0.9863 | 37 | 1.0 | 0.9722 | 0.9859 | 36 | 1.0 | 0.9333 | 0.9655 | 30 | 0.92 | 0.92 | 0.92 | 25 | 1.0 | 1.0 | 1.0 | 27 | 0.9723 | 0.9797 | 0.9760 | 0.9941 | | 0.1035 | 8.0 | 1256 | 0.1043 | 0.9412 | 0.9697 | 0.9552 | 33 | 0.9714 | 1.0 | 0.9855 | 34 | 1.0 | 1.0 | 1.0 | 36 | 1.0 | 1.0 | 1.0 | 36 | 0.9706 | 0.9706 | 0.9706 | 34 | 0.9714 | 1.0 | 0.9855 | 34 | 0.9118 | 0.9688 | 0.9394 | 32 | 0.9231 | 0.9730 | 0.9474 | 37 | 0.75 | 1.0 | 0.8571 | 36 | 0.9032 | 0.9333 | 0.9180 | 30 | 0.6486 | 0.96 | 0.7742 | 25 | 1.0 | 1.0 | 1.0 | 27 | 0.9085 | 0.9822 | 0.9439 | 0.9856 | | 0.0843 | 9.0 | 1413 | 0.0823 | 0.9412 | 0.9697 | 0.9552 | 33 | 0.9714 | 1.0 | 0.9855 | 34 | 1.0 | 1.0 | 1.0 | 36 | 1.0 | 1.0 | 1.0 | 36 | 0.9143 | 0.9412 | 0.9275 | 34 | 0.9714 | 1.0 | 0.9855 | 34 | 0.9394 | 0.9688 | 0.9538 | 32 | 0.9032 | 0.7568 | 0.8235 | 37 | 0.9211 | 0.9722 | 0.9459 | 36 | 0.9655 | 0.9333 | 0.9492 | 30 | 0.7059 | 0.96 | 0.8136 | 25 | 1.0 | 1.0 | 1.0 | 27 | 0.9355 | 0.9569 | 0.9460 | 0.9905 | | 0.0733 | 10.0 | 1570 | 0.0738 | 0.9412 | 0.9697 | 0.9552 | 33 | 0.9714 | 1.0 | 0.9855 | 34 | 1.0 | 1.0 | 1.0 | 36 | 1.0 | 1.0 | 1.0 | 36 | 0.9714 | 1.0 | 0.9855 | 34 | 0.9714 | 1.0 | 0.9855 | 34 | 0.9118 | 0.9688 | 0.9394 | 32 | 0.9459 | 0.9459 | 0.9459 | 37 | 1.0 | 0.9444 | 0.9714 | 36 | 0.8485 | 0.9333 | 0.8889 | 30 | 0.8333 | 1.0 | 0.9091 | 25 | 0.9643 | 1.0 | 0.9818 | 27 | 0.9484 | 0.9797 | 0.9638 | 0.9911 | | 0.0614 | 11.0 | 1727 | 0.0661 | 0.9412 | 0.9697 | 0.9552 | 33 | 0.9714 | 1.0 | 0.9855 | 34 | 1.0 | 1.0 | 1.0 | 36 | 1.0 | 1.0 | 1.0 | 36 | 0.9714 | 1.0 | 0.9855 | 34 | 0.9714 | 1.0 | 0.9855 | 34 | 0.9118 | 0.9688 | 0.9394 | 32 | 0.9459 | 0.9459 | 0.9459 | 37 | 1.0 | 0.9722 | 0.9859 | 36 | 0.9655 | 0.9333 | 0.9492 | 30 | 0.9231 | 0.96 | 0.9412 | 25 | 1.0 | 0.9630 | 0.9811 | 27 | 0.9673 | 0.9772 | 0.9722 | 0.9934 | | 0.0548 | 12.0 | 1884 | 0.0637 | 0.9412 | 0.9697 | 0.9552 | 33 | 0.9714 | 1.0 | 0.9855 | 34 | 1.0 | 1.0 | 1.0 | 36 | 0.9730 | 1.0 | 0.9863 | 36 | 0.9167 | 0.9706 | 0.9429 | 34 | 0.9714 | 1.0 | 0.9855 | 34 | 0.9118 | 0.9688 | 0.9394 | 32 | 0.9459 | 0.9459 | 0.9459 | 37 | 1.0 | 0.9722 | 0.9859 | 36 | 0.875 | 0.9333 | 0.9032 | 30 | 0.9259 | 1.0 | 0.9615 | 25 | 0.9643 | 1.0 | 0.9818 | 27 | 0.9507 | 0.9797 | 0.965 | 0.9921 | | 0.0515 | 13.0 | 2041 | 0.0562 | 0.9412 | 0.9697 | 0.9552 | 33 | 0.9714 | 1.0 | 0.9855 | 34 | 1.0 | 1.0 | 1.0 | 36 | 1.0 | 1.0 | 1.0 | 36 | 0.9714 | 1.0 | 0.9855 | 34 | 0.9714 | 1.0 | 0.9855 | 34 | 0.9118 | 0.9688 | 0.9394 | 32 | 0.9730 | 0.9730 | 0.9730 | 37 | 1.0 | 1.0 | 1.0 | 36 | 0.9333 | 0.9333 | 0.9333 | 30 | 0.8621 | 1.0 | 0.9259 | 25 | 0.9643 | 1.0 | 0.9818 | 27 | 0.9605 | 0.9873 | 0.9737 | 0.9931 | | 0.0431 | 14.0 | 2198 | 0.0513 | 0.9412 | 0.9697 | 0.9552 | 33 | 0.9714 | 1.0 | 0.9855 | 34 | 1.0 | 1.0 | 1.0 | 36 | 1.0 | 1.0 | 1.0 | 36 | 0.9444 | 1.0 | 0.9714 | 34 | 0.9714 | 1.0 | 0.9855 | 34 | 0.9118 | 0.9688 | 0.9394 | 32 | 1.0 | 0.9730 | 0.9863 | 37 | 1.0 | 1.0 | 1.0 | 36 | 1.0 | 0.9333 | 0.9655 | 30 | 0.9231 | 0.96 | 0.9412 | 25 | 1.0 | 0.9630 | 0.9811 | 27 | 0.9724 | 0.9822 | 0.9773 | 0.9944 | | 0.0413 | 15.0 | 2355 | 0.0582 | 0.9412 | 0.9697 | 0.9552 | 33 | 0.9706 | 0.9706 | 0.9706 | 34 | 0.9730 | 1.0 | 0.9863 | 36 | 0.9730 | 1.0 | 0.9863 | 36 | 0.9429 | 0.9706 | 0.9565 | 34 | 0.9714 | 1.0 | 0.9855 | 34 | 0.9118 | 0.9688 | 0.9394 | 32 | 1.0 | 0.9730 | 0.9863 | 37 | 1.0 | 1.0 | 1.0 | 36 | 0.9655 | 0.9333 | 0.9492 | 30 | 0.8929 | 1.0 | 0.9434 | 25 | 1.0 | 1.0 | 1.0 | 27 | 0.9627 | 0.9822 | 0.9724 | 0.9934 | | 0.035 | 16.0 | 2512 | 0.0556 | 0.9412 | 0.9697 | 0.9552 | 33 | 0.9714 | 1.0 | 0.9855 | 34 | 1.0 | 1.0 | 1.0 | 36 | 1.0 | 0.9722 | 0.9859 | 36 | 0.8857 | 0.9118 | 0.8986 | 34 | 0.9714 | 1.0 | 0.9855 | 34 | 0.9118 | 0.9688 | 0.9394 | 32 | 0.9730 | 0.9730 | 0.9730 | 37 | 1.0 | 0.9722 | 0.9859 | 36 | 0.9333 | 0.9333 | 0.9333 | 30 | 0.8621 | 1.0 | 0.9259 | 25 | 1.0 | 1.0 | 1.0 | 27 | 0.9552 | 0.9746 | 0.9648 | 0.9915 | | 0.0316 | 17.0 | 2669 | 0.0517 | 0.9412 | 0.9697 | 0.9552 | 33 | 0.9714 | 1.0 | 0.9855 | 34 | 1.0 | 1.0 | 1.0 | 36 | 1.0 | 1.0 | 1.0 | 36 | 0.9167 | 0.9706 | 0.9429 | 34 | 0.9714 | 1.0 | 0.9855 | 34 | 0.9118 | 0.9688 | 0.9394 | 32 | 1.0 | 0.9730 | 0.9863 | 37 | 1.0 | 0.9722 | 0.9859 | 36 | 0.875 | 0.9333 | 0.9032 | 30 | 0.8929 | 1.0 | 0.9434 | 25 | 1.0 | 1.0 | 1.0 | 27 | 0.9579 | 0.9822 | 0.9699 | 0.9928 | | 0.027 | 18.0 | 2826 | 0.0502 | 0.9412 | 0.9697 | 0.9552 | 33 | 0.9714 | 1.0 | 0.9855 | 34 | 0.9730 | 1.0 | 0.9863 | 36 | 1.0 | 1.0 | 1.0 | 36 | 0.9444 | 1.0 | 0.9714 | 34 | 0.9714 | 1.0 | 0.9855 | 34 | 0.9118 | 0.9688 | 0.9394 | 32 | 1.0 | 0.9730 | 0.9863 | 37 | 1.0 | 0.9722 | 0.9859 | 36 | 0.9032 | 0.9333 | 0.9180 | 30 | 0.9259 | 1.0 | 0.9615 | 25 | 1.0 | 1.0 | 1.0 | 27 | 0.9628 | 0.9848 | 0.9737 | 0.9931 | | 0.026 | 19.0 | 2983 | 0.0481 | 0.9412 | 0.9697 | 0.9552 | 33 | 0.9714 | 1.0 | 0.9855 | 34 | 1.0 | 1.0 | 1.0 | 36 | 1.0 | 1.0 | 1.0 | 36 | 0.9189 | 1.0 | 0.9577 | 34 | 0.9714 | 1.0 | 0.9855 | 34 | 0.9118 | 0.9688 | 0.9394 | 32 | 1.0 | 0.9730 | 0.9863 | 37 | 1.0 | 1.0 | 1.0 | 36 | 0.9333 | 0.9333 | 0.9333 | 30 | 0.8333 | 1.0 | 0.9091 | 25 | 1.0 | 1.0 | 1.0 | 27 | 0.9581 | 0.9873 | 0.9725 | 0.9928 | | 0.026 | 20.0 | 3140 | 0.0652 | 0.9412 | 0.9697 | 0.9552 | 33 | 0.9714 | 1.0 | 0.9855 | 34 | 0.9730 | 1.0 | 0.9863 | 36 | 1.0 | 1.0 | 1.0 | 36 | 0.9714 | 1.0 | 0.9855 | 34 | 0.9714 | 1.0 | 0.9855 | 34 | 0.8611 | 0.9688 | 0.9118 | 32 | 0.9730 | 0.9730 | 0.9730 | 37 | 0.9730 | 1.0 | 0.9863 | 36 | 0.8235 | 0.9333 | 0.8750 | 30 | 0.8333 | 1.0 | 0.9091 | 25 | 1.0 | 1.0 | 1.0 | 27 | 0.9419 | 0.9873 | 0.9641 | 0.9882 | | 0.0311 | 21.0 | 3297 | 0.0438 | 0.9412 | 0.9697 | 0.9552 | 33 | 0.9714 | 1.0 | 0.9855 | 34 | 1.0 | 1.0 | 1.0 | 36 | 1.0 | 1.0 | 1.0 | 36 | 0.9444 | 1.0 | 0.9714 | 34 | 0.9714 | 1.0 | 0.9855 | 34 | 0.9118 | 0.9688 | 0.9394 | 32 | 1.0 | 0.9730 | 0.9863 | 37 | 1.0 | 0.9722 | 0.9859 | 36 | 0.9655 | 0.9333 | 0.9492 | 30 | 0.9259 | 1.0 | 0.9615 | 25 | 1.0 | 1.0 | 1.0 | 27 | 0.97 | 0.9848 | 0.9773 | 0.9941 | | 0.0216 | 22.0 | 3454 | 0.0454 | 0.9412 | 0.9697 | 0.9552 | 33 | 0.9714 | 1.0 | 0.9855 | 34 | 1.0 | 1.0 | 1.0 | 36 | 1.0 | 1.0 | 1.0 | 36 | 0.9706 | 0.9706 | 0.9706 | 34 | 0.9714 | 1.0 | 0.9855 | 34 | 0.9118 | 0.9688 | 0.9394 | 32 | 1.0 | 0.9730 | 0.9863 | 37 | 1.0 | 0.9722 | 0.9859 | 36 | 0.9333 | 0.9333 | 0.9333 | 30 | 0.9259 | 1.0 | 0.9615 | 25 | 1.0 | 1.0 | 1.0 | 27 | 0.9699 | 0.9822 | 0.9760 | 0.9941 | | 0.0196 | 23.0 | 3611 | 0.0510 | 0.9412 | 0.9697 | 0.9552 | 33 | 0.9714 | 1.0 | 0.9855 | 34 | 1.0 | 1.0 | 1.0 | 36 | 1.0 | 1.0 | 1.0 | 36 | 0.9714 | 1.0 | 0.9855 | 34 | 0.9714 | 1.0 | 0.9855 | 34 | 0.9118 | 0.9688 | 0.9394 | 32 | 0.8718 | 0.9189 | 0.8947 | 37 | 1.0 | 0.9722 | 0.9859 | 36 | 0.9655 | 0.9333 | 0.9492 | 30 | 0.9259 | 1.0 | 0.9615 | 25 | 1.0 | 1.0 | 1.0 | 27 | 0.9602 | 0.9797 | 0.9698 | 0.9934 | | 0.0176 | 24.0 | 3768 | 0.0457 | 0.9412 | 0.9697 | 0.9552 | 33 | 0.9714 | 1.0 | 0.9855 | 34 | 1.0 | 1.0 | 1.0 | 36 | 1.0 | 1.0 | 1.0 | 36 | 0.9706 | 0.9706 | 0.9706 | 34 | 0.9714 | 1.0 | 0.9855 | 34 | 0.9118 | 0.9688 | 0.9394 | 32 | 1.0 | 0.9730 | 0.9863 | 37 | 1.0 | 1.0 | 1.0 | 36 | 0.9333 | 0.9333 | 0.9333 | 30 | 0.8929 | 1.0 | 0.9434 | 25 | 1.0 | 1.0 | 1.0 | 27 | 0.9676 | 0.9848 | 0.9761 | 0.9938 | | 0.0141 | 25.0 | 3925 | 0.0516 | 0.9412 | 0.9697 | 0.9552 | 33 | 0.9714 | 1.0 | 0.9855 | 34 | 1.0 | 1.0 | 1.0 | 36 | 1.0 | 1.0 | 1.0 | 36 | 0.9714 | 1.0 | 0.9855 | 34 | 0.9714 | 1.0 | 0.9855 | 34 | 0.9118 | 0.9688 | 0.9394 | 32 | 0.9722 | 0.9459 | 0.9589 | 37 | 0.9730 | 1.0 | 0.9863 | 36 | 0.875 | 0.9333 | 0.9032 | 30 | 0.9231 | 0.96 | 0.9412 | 25 | 0.9643 | 1.0 | 0.9818 | 27 | 0.9579 | 0.9822 | 0.9699 | 0.9928 | | 0.0129 | 26.0 | 4082 | 0.0508 | 0.9412 | 0.9697 | 0.9552 | 33 | 0.9714 | 1.0 | 0.9855 | 34 | 0.9730 | 1.0 | 0.9863 | 36 | 1.0 | 1.0 | 1.0 | 36 | 0.9714 | 1.0 | 0.9855 | 34 | 0.9714 | 1.0 | 0.9855 | 34 | 0.9118 | 0.9688 | 0.9394 | 32 | 1.0 | 0.9730 | 0.9863 | 37 | 1.0 | 1.0 | 1.0 | 36 | 0.875 | 0.9333 | 0.9032 | 30 | 0.9259 | 1.0 | 0.9615 | 25 | 1.0 | 1.0 | 1.0 | 27 | 0.9629 | 0.9873 | 0.9749 | 0.9934 | | 0.0125 | 27.0 | 4239 | 0.0455 | 0.9412 | 0.9697 | 0.9552 | 33 | 0.9714 | 1.0 | 0.9855 | 34 | 1.0 | 1.0 | 1.0 | 36 | 1.0 | 1.0 | 1.0 | 36 | 0.9714 | 1.0 | 0.9855 | 34 | 0.9714 | 1.0 | 0.9855 | 34 | 0.9118 | 0.9688 | 0.9394 | 32 | 1.0 | 0.9730 | 0.9863 | 37 | 1.0 | 0.9722 | 0.9859 | 36 | 1.0 | 0.9333 | 0.9655 | 30 | 0.9259 | 1.0 | 0.9615 | 25 | 0.8710 | 1.0 | 0.9310 | 27 | 0.9652 | 0.9848 | 0.9749 | 0.9934 | | 0.0131 | 28.0 | 4396 | 0.0452 | 0.9412 | 0.9697 | 0.9552 | 33 | 0.9714 | 1.0 | 0.9855 | 34 | 1.0 | 1.0 | 1.0 | 36 | 1.0 | 0.9722 | 0.9859 | 36 | 0.9429 | 0.9706 | 0.9565 | 34 | 0.9714 | 1.0 | 0.9855 | 34 | 0.9118 | 0.9688 | 0.9394 | 32 | 1.0 | 0.9730 | 0.9863 | 37 | 1.0 | 0.9722 | 0.9859 | 36 | 1.0 | 0.9333 | 0.9655 | 30 | 0.9231 | 0.96 | 0.9412 | 25 | 1.0 | 1.0 | 1.0 | 27 | 0.9722 | 0.9772 | 0.9747 | 0.9941 | | 0.0112 | 29.0 | 4553 | 0.0465 | 0.9412 | 0.9697 | 0.9552 | 33 | 0.9714 | 1.0 | 0.9855 | 34 | 1.0 | 1.0 | 1.0 | 36 | 1.0 | 1.0 | 1.0 | 36 | 0.9714 | 1.0 | 0.9855 | 34 | 0.9714 | 1.0 | 0.9855 | 34 | 0.9118 | 0.9688 | 0.9394 | 32 | 0.9459 | 0.9459 | 0.9459 | 37 | 0.9722 | 0.9722 | 0.9722 | 36 | 0.9333 | 0.9333 | 0.9333 | 30 | 0.9583 | 0.92 | 0.9388 | 25 | 1.0 | 1.0 | 1.0 | 27 | 0.9649 | 0.9772 | 0.9710 | 0.9931 | | 0.0152 | 30.0 | 4710 | 0.0510 | 0.9412 | 0.9697 | 0.9552 | 33 | 0.9714 | 1.0 | 0.9855 | 34 | 1.0 | 1.0 | 1.0 | 36 | 1.0 | 1.0 | 1.0 | 36 | 0.8857 | 0.9118 | 0.8986 | 34 | 0.9714 | 1.0 | 0.9855 | 34 | 0.9118 | 0.9688 | 0.9394 | 32 | 0.9730 | 0.9730 | 0.9730 | 37 | 1.0 | 0.9722 | 0.9859 | 36 | 1.0 | 0.9333 | 0.9655 | 30 | 0.9231 | 0.96 | 0.9412 | 25 | 1.0 | 1.0 | 1.0 | 27 | 0.9648 | 0.9746 | 0.9697 | 0.9931 | ### Framework versions - Transformers 4.22.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1