File size: 51,654 Bytes
aea8278 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 |
---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
model-index:
- name: lmv2-g-w2-300-doc-09-08
results: []
---
<!-- 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. -->
# lmv2-g-w2-300-doc-09-08
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: 0.0262
- Control Number Precision: 1.0
- Control Number Recall: 1.0
- Control Number F1: 1.0
- Control Number Number: 17
- Ein Precision: 1.0
- Ein Recall: 0.9833
- Ein F1: 0.9916
- Ein Number: 60
- Employee’s Address Precision: 0.9667
- Employee’s Address Recall: 0.9831
- Employee’s Address F1: 0.9748
- Employee’s Address Number: 59
- Employee’s Name Precision: 0.9833
- Employee’s Name Recall: 1.0
- Employee’s Name F1: 0.9916
- Employee’s Name Number: 59
- Employee’s Ssn Precision: 0.9836
- Employee’s Ssn Recall: 1.0
- Employee’s Ssn F1: 0.9917
- Employee’s Ssn Number: 60
- Employer’s Address Precision: 0.9833
- Employer’s Address Recall: 0.9672
- Employer’s Address F1: 0.9752
- Employer’s Address Number: 61
- Employer’s Name Precision: 0.9833
- Employer’s Name Recall: 0.9833
- Employer’s Name F1: 0.9833
- Employer’s Name Number: 60
- Federal Income Tax Withheld Precision: 1.0
- Federal Income Tax Withheld Recall: 1.0
- Federal Income Tax Withheld F1: 1.0
- Federal Income Tax Withheld Number: 60
- Medicare Tax Withheld Precision: 1.0
- Medicare Tax Withheld Recall: 1.0
- Medicare Tax Withheld F1: 1.0
- Medicare Tax Withheld Number: 60
- Medicare Wages Tips Precision: 1.0
- Medicare Wages Tips Recall: 1.0
- Medicare Wages Tips F1: 1.0
- Medicare Wages Tips Number: 60
- Social Security Tax Withheld Precision: 1.0
- Social Security Tax Withheld Recall: 0.9836
- Social Security Tax Withheld F1: 0.9917
- Social Security Tax Withheld Number: 61
- Social Security Wages Precision: 0.9833
- Social Security Wages Recall: 1.0
- Social Security Wages F1: 0.9916
- Social Security Wages Number: 59
- Wages Tips Precision: 1.0
- Wages Tips Recall: 0.9836
- Wages Tips F1: 0.9917
- Wages Tips Number: 61
- Overall Precision: 0.9905
- Overall Recall: 0.9905
- Overall F1: 0.9905
- Overall Accuracy: 0.9973
## 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 | Control Number Precision | Control Number Recall | Control Number F1 | Control Number Number | Ein Precision | Ein Recall | Ein F1 | Ein Number | Employee’s Address Precision | Employee’s Address Recall | Employee’s Address F1 | Employee’s Address Number | Employee’s Name Precision | Employee’s Name Recall | Employee’s Name F1 | Employee’s Name Number | Employee’s Ssn Precision | Employee’s Ssn Recall | Employee’s Ssn F1 | Employee’s Ssn Number | Employer’s Address Precision | Employer’s Address Recall | Employer’s Address F1 | Employer’s Address Number | Employer’s Name Precision | Employer’s Name Recall | Employer’s Name F1 | Employer’s Name Number | Federal Income Tax Withheld Precision | Federal Income Tax Withheld Recall | Federal Income Tax Withheld F1 | Federal Income Tax Withheld Number | Medicare Tax Withheld Precision | Medicare Tax Withheld Recall | Medicare Tax Withheld F1 | Medicare Tax Withheld Number | Medicare Wages Tips Precision | Medicare Wages Tips Recall | Medicare Wages Tips F1 | Medicare Wages Tips Number | Social Security Tax Withheld Precision | Social Security Tax Withheld Recall | Social Security Tax Withheld F1 | Social Security Tax Withheld Number | Social Security Wages Precision | Social Security Wages Recall | Social Security Wages F1 | Social Security Wages Number | Wages Tips Precision | Wages Tips Recall | Wages Tips F1 | Wages Tips Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------------------------:|:---------------------:|:-----------------:|:---------------------:|:-------------:|:----------:|:------:|:----------:|:----------------------------:|:-------------------------:|:---------------------:|:-------------------------:|:-------------------------:|:----------------------:|:------------------:|:----------------------:|:------------------------:|:---------------------:|:-----------------:|:---------------------:|:----------------------------:|:-------------------------:|:---------------------:|:-------------------------:|:-------------------------:|:----------------------:|:------------------:|:----------------------:|:-------------------------------------:|:----------------------------------:|:------------------------------:|:----------------------------------:|:-------------------------------:|:----------------------------:|:------------------------:|:----------------------------:|:-----------------------------:|:--------------------------:|:----------------------:|:--------------------------:|:--------------------------------------:|:-----------------------------------:|:-------------------------------:|:-----------------------------------:|:-------------------------------:|:----------------------------:|:------------------------:|:----------------------------:|:--------------------:|:-----------------:|:-------------:|:-----------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.7717 | 1.0 | 240 | 0.9856 | 0.0 | 0.0 | 0.0 | 17 | 0.9206 | 0.9667 | 0.9431 | 60 | 0.6824 | 0.9831 | 0.8056 | 59 | 0.2333 | 0.5932 | 0.3349 | 59 | 0.9836 | 1.0 | 0.9917 | 60 | 0.7609 | 0.5738 | 0.6542 | 61 | 0.3654 | 0.3167 | 0.3393 | 60 | 0.0 | 0.0 | 0.0 | 60 | 0.8194 | 0.9833 | 0.8939 | 60 | 0.6064 | 0.95 | 0.7403 | 60 | 0.5050 | 0.8361 | 0.6296 | 61 | 0.0 | 0.0 | 0.0 | 59 | 0.5859 | 0.9508 | 0.725 | 61 | 0.5954 | 0.6649 | 0.6282 | 0.9558 |
| 0.5578 | 2.0 | 480 | 0.2957 | 0.8462 | 0.6471 | 0.7333 | 17 | 0.9831 | 0.9667 | 0.9748 | 60 | 0.9048 | 0.9661 | 0.9344 | 59 | 0.8358 | 0.9492 | 0.8889 | 59 | 0.9836 | 1.0 | 0.9917 | 60 | 0.8125 | 0.8525 | 0.8320 | 61 | 0.8462 | 0.9167 | 0.8800 | 60 | 0.9672 | 0.9833 | 0.9752 | 60 | 0.9524 | 1.0 | 0.9756 | 60 | 0.9194 | 0.95 | 0.9344 | 60 | 0.9833 | 0.9672 | 0.9752 | 61 | 0.9508 | 0.9831 | 0.9667 | 59 | 0.9516 | 0.9672 | 0.9593 | 61 | 0.9212 | 0.9512 | 0.9359 | 0.9891 |
| 0.223 | 3.0 | 720 | 0.1626 | 0.5 | 0.6471 | 0.5641 | 17 | 0.9667 | 0.9667 | 0.9667 | 60 | 0.9355 | 0.9831 | 0.9587 | 59 | 0.9672 | 1.0 | 0.9833 | 59 | 0.9836 | 1.0 | 0.9917 | 60 | 0.8769 | 0.9344 | 0.9048 | 61 | 0.9508 | 0.9667 | 0.9587 | 60 | 0.9833 | 0.9833 | 0.9833 | 60 | 0.9836 | 1.0 | 0.9917 | 60 | 0.8769 | 0.95 | 0.912 | 60 | 1.0 | 0.9836 | 0.9917 | 61 | 0.9355 | 0.9831 | 0.9587 | 59 | 0.9516 | 0.9672 | 0.9593 | 61 | 0.9370 | 0.9688 | 0.9526 | 0.9923 |
| 0.1305 | 4.0 | 960 | 0.1025 | 0.9444 | 1.0 | 0.9714 | 17 | 0.9831 | 0.9667 | 0.9748 | 60 | 0.9194 | 0.9661 | 0.9421 | 59 | 0.9508 | 0.9831 | 0.9667 | 59 | 0.9836 | 1.0 | 0.9917 | 60 | 0.9219 | 0.9672 | 0.944 | 61 | 0.9667 | 0.9667 | 0.9667 | 60 | 0.9833 | 0.9833 | 0.9833 | 60 | 0.9524 | 1.0 | 0.9756 | 60 | 0.8906 | 0.95 | 0.9194 | 60 | 0.9833 | 0.9672 | 0.9752 | 61 | 0.9355 | 0.9831 | 0.9587 | 59 | 0.9516 | 0.9672 | 0.9593 | 61 | 0.9511 | 0.9756 | 0.9632 | 0.9947 |
| 0.0852 | 5.0 | 1200 | 0.0744 | 0.7391 | 1.0 | 0.85 | 17 | 0.9831 | 0.9667 | 0.9748 | 60 | 0.9667 | 0.9831 | 0.9748 | 59 | 0.9833 | 1.0 | 0.9916 | 59 | 0.9836 | 1.0 | 0.9917 | 60 | 0.9344 | 0.9344 | 0.9344 | 61 | 1.0 | 0.9833 | 0.9916 | 60 | 0.9365 | 0.9833 | 0.9593 | 60 | 0.9677 | 1.0 | 0.9836 | 60 | 0.95 | 0.95 | 0.9500 | 60 | 0.9836 | 0.9836 | 0.9836 | 61 | 0.9667 | 0.9831 | 0.9748 | 59 | 0.9833 | 0.9672 | 0.9752 | 61 | 0.9626 | 0.9783 | 0.9704 | 0.9953 |
| 0.0583 | 6.0 | 1440 | 0.0554 | 0.7727 | 1.0 | 0.8718 | 17 | 0.9831 | 0.9667 | 0.9748 | 60 | 0.9667 | 0.9831 | 0.9748 | 59 | 0.9833 | 1.0 | 0.9916 | 59 | 0.9836 | 1.0 | 0.9917 | 60 | 0.9048 | 0.9344 | 0.9194 | 61 | 1.0 | 0.9833 | 0.9916 | 60 | 1.0 | 0.9833 | 0.9916 | 60 | 0.9833 | 0.9833 | 0.9833 | 60 | 0.9344 | 0.95 | 0.9421 | 60 | 1.0 | 0.9672 | 0.9833 | 61 | 0.9667 | 0.9831 | 0.9748 | 59 | 0.9833 | 0.9672 | 0.9752 | 61 | 0.9677 | 0.9756 | 0.9716 | 0.9957 |
| 0.0431 | 7.0 | 1680 | 0.0471 | 0.9444 | 1.0 | 0.9714 | 17 | 0.9831 | 0.9667 | 0.9748 | 60 | 0.9016 | 0.9322 | 0.9167 | 59 | 0.95 | 0.9661 | 0.9580 | 59 | 0.9836 | 1.0 | 0.9917 | 60 | 0.8676 | 0.9672 | 0.9147 | 61 | 0.9831 | 0.9667 | 0.9748 | 60 | 1.0 | 0.9833 | 0.9916 | 60 | 1.0 | 1.0 | 1.0 | 60 | 0.9516 | 0.9833 | 0.9672 | 60 | 0.9836 | 0.9836 | 0.9836 | 61 | 0.9831 | 0.9831 | 0.9831 | 59 | 0.9833 | 0.9672 | 0.9752 | 61 | 0.9625 | 0.9756 | 0.9690 | 0.9947 |
| 0.0314 | 8.0 | 1920 | 0.0359 | 1.0 | 1.0 | 1.0 | 17 | 0.9831 | 0.9667 | 0.9748 | 60 | 0.9355 | 0.9831 | 0.9587 | 59 | 0.9833 | 1.0 | 0.9916 | 59 | 0.9836 | 1.0 | 0.9917 | 60 | 0.9516 | 0.9672 | 0.9593 | 61 | 1.0 | 0.9667 | 0.9831 | 60 | 0.9833 | 0.9833 | 0.9833 | 60 | 1.0 | 1.0 | 1.0 | 60 | 0.9516 | 0.9833 | 0.9672 | 60 | 1.0 | 0.9836 | 0.9917 | 61 | 0.9831 | 0.9831 | 0.9831 | 59 | 0.9672 | 0.9672 | 0.9672 | 61 | 0.9771 | 0.9824 | 0.9797 | 0.9969 |
| 0.0278 | 9.0 | 2160 | 0.0338 | 0.8947 | 1.0 | 0.9444 | 17 | 0.9833 | 0.9833 | 0.9833 | 60 | 0.9355 | 0.9831 | 0.9587 | 59 | 0.9667 | 0.9831 | 0.9748 | 59 | 1.0 | 1.0 | 1.0 | 60 | 0.9365 | 0.9672 | 0.9516 | 61 | 0.9672 | 0.9833 | 0.9752 | 60 | 1.0 | 0.9833 | 0.9916 | 60 | 1.0 | 1.0 | 1.0 | 60 | 0.9516 | 0.9833 | 0.9672 | 60 | 1.0 | 0.9836 | 0.9917 | 61 | 0.9667 | 0.9831 | 0.9748 | 59 | 0.9672 | 0.9672 | 0.9672 | 61 | 0.9705 | 0.9837 | 0.9771 | 0.9965 |
| 0.0231 | 10.0 | 2400 | 0.0332 | 0.9444 | 1.0 | 0.9714 | 17 | 0.9831 | 0.9667 | 0.9748 | 60 | 0.9508 | 0.9831 | 0.9667 | 59 | 0.9048 | 0.9661 | 0.9344 | 59 | 0.9836 | 1.0 | 0.9917 | 60 | 0.9667 | 0.9508 | 0.9587 | 61 | 0.9667 | 0.9667 | 0.9667 | 60 | 1.0 | 0.9833 | 0.9916 | 60 | 0.9836 | 1.0 | 0.9917 | 60 | 0.9365 | 0.9833 | 0.9593 | 60 | 1.0 | 0.9672 | 0.9833 | 61 | 0.9831 | 0.9831 | 0.9831 | 59 | 0.9833 | 0.9672 | 0.9752 | 61 | 0.9690 | 0.9769 | 0.9730 | 0.9964 |
| 0.0189 | 11.0 | 2640 | 0.0342 | 1.0 | 1.0 | 1.0 | 17 | 0.9667 | 0.9667 | 0.9667 | 60 | 0.8657 | 0.9831 | 0.9206 | 59 | 0.9833 | 1.0 | 0.9916 | 59 | 0.9836 | 1.0 | 0.9917 | 60 | 0.8594 | 0.9016 | 0.88 | 61 | 1.0 | 0.9833 | 0.9916 | 60 | 0.9833 | 0.9833 | 0.9833 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 0.9836 | 0.9917 | 61 | 0.9833 | 1.0 | 0.9916 | 59 | 0.9516 | 0.9672 | 0.9593 | 61 | 0.964 | 0.9810 | 0.9724 | 0.9958 |
| 0.0187 | 12.0 | 2880 | 0.0255 | 1.0 | 1.0 | 1.0 | 17 | 0.9667 | 0.9667 | 0.9667 | 60 | 0.9508 | 0.9831 | 0.9667 | 59 | 0.9833 | 1.0 | 0.9916 | 59 | 0.9836 | 1.0 | 0.9917 | 60 | 0.9667 | 0.9508 | 0.9587 | 61 | 1.0 | 0.9833 | 0.9916 | 60 | 0.9672 | 0.9833 | 0.9752 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 0.9836 | 0.9917 | 61 | 0.9833 | 1.0 | 0.9916 | 59 | 0.9833 | 0.9672 | 0.9752 | 61 | 0.9824 | 0.9851 | 0.9837 | 0.9976 |
| 0.0126 | 13.0 | 3120 | 0.0257 | 1.0 | 1.0 | 1.0 | 17 | 0.9667 | 0.9667 | 0.9667 | 60 | 0.9344 | 0.9661 | 0.95 | 59 | 0.8889 | 0.9492 | 0.9180 | 59 | 0.9836 | 1.0 | 0.9917 | 60 | 0.8788 | 0.9508 | 0.9134 | 61 | 1.0 | 0.9833 | 0.9916 | 60 | 1.0 | 1.0 | 1.0 | 60 | 0.9836 | 1.0 | 0.9917 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 0.9672 | 0.9833 | 61 | 0.9508 | 0.9831 | 0.9667 | 59 | 1.0 | 0.9836 | 0.9917 | 61 | 0.9652 | 0.9796 | 0.9724 | 0.9971 |
| 0.012 | 14.0 | 3360 | 0.0227 | 1.0 | 1.0 | 1.0 | 17 | 0.9667 | 0.9667 | 0.9667 | 60 | 0.9516 | 1.0 | 0.9752 | 59 | 0.9833 | 1.0 | 0.9916 | 59 | 0.9836 | 1.0 | 0.9917 | 60 | 0.9194 | 0.9344 | 0.9268 | 61 | 1.0 | 0.9833 | 0.9916 | 60 | 0.9672 | 0.9833 | 0.9752 | 60 | 1.0 | 0.9833 | 0.9916 | 60 | 1.0 | 1.0 | 1.0 | 60 | 0.9836 | 0.9836 | 0.9836 | 61 | 0.9833 | 1.0 | 0.9916 | 59 | 1.0 | 0.9836 | 0.9917 | 61 | 0.9784 | 0.9851 | 0.9817 | 0.9977 |
| 0.0119 | 15.0 | 3600 | 0.0284 | 1.0 | 1.0 | 1.0 | 17 | 1.0 | 1.0 | 1.0 | 60 | 0.9355 | 0.9831 | 0.9587 | 59 | 0.9833 | 1.0 | 0.9916 | 59 | 1.0 | 1.0 | 1.0 | 60 | 0.9167 | 0.9016 | 0.9091 | 61 | 0.9661 | 0.95 | 0.9580 | 60 | 0.9833 | 0.9833 | 0.9833 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 0.9836 | 0.9917 | 61 | 0.9833 | 1.0 | 0.9916 | 59 | 1.0 | 0.9836 | 0.9917 | 61 | 0.9810 | 0.9824 | 0.9817 | 0.9965 |
| 0.0103 | 16.0 | 3840 | 0.0289 | 0.9444 | 1.0 | 0.9714 | 17 | 0.9672 | 0.9833 | 0.9752 | 60 | 0.9344 | 0.9661 | 0.95 | 59 | 0.9833 | 1.0 | 0.9916 | 59 | 1.0 | 1.0 | 1.0 | 60 | 0.8088 | 0.9016 | 0.8527 | 61 | 0.9667 | 0.9667 | 0.9667 | 60 | 0.9833 | 0.9833 | 0.9833 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 0.9836 | 0.9917 | 61 | 0.9833 | 1.0 | 0.9916 | 59 | 1.0 | 0.9836 | 0.9917 | 61 | 0.9666 | 0.9810 | 0.9737 | 0.9963 |
| 0.01 | 17.0 | 4080 | 0.0305 | 0.8947 | 1.0 | 0.9444 | 17 | 1.0 | 0.9833 | 0.9916 | 60 | 0.9355 | 0.9831 | 0.9587 | 59 | 0.9516 | 1.0 | 0.9752 | 59 | 0.9836 | 1.0 | 0.9917 | 60 | 0.9355 | 0.9508 | 0.9431 | 61 | 0.9833 | 0.9833 | 0.9833 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 1.0 | 1.0 | 60 | 0.8955 | 1.0 | 0.9449 | 60 | 1.0 | 0.9836 | 0.9917 | 61 | 0.9833 | 1.0 | 0.9916 | 59 | 1.0 | 0.9836 | 0.9917 | 61 | 0.9694 | 0.9891 | 0.9792 | 0.9961 |
| 0.0082 | 18.0 | 4320 | 0.0256 | 1.0 | 1.0 | 1.0 | 17 | 1.0 | 0.9833 | 0.9916 | 60 | 0.9508 | 0.9831 | 0.9667 | 59 | 0.9833 | 1.0 | 0.9916 | 59 | 0.9836 | 1.0 | 0.9917 | 60 | 0.8636 | 0.9344 | 0.8976 | 61 | 0.9831 | 0.9667 | 0.9748 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 0.9836 | 0.9917 | 61 | 0.9833 | 1.0 | 0.9916 | 59 | 1.0 | 0.9836 | 0.9917 | 61 | 0.9785 | 0.9864 | 0.9824 | 0.9970 |
| 0.0059 | 19.0 | 4560 | 0.0255 | 1.0 | 1.0 | 1.0 | 17 | 1.0 | 0.9833 | 0.9916 | 60 | 0.9667 | 0.9831 | 0.9748 | 59 | 0.9833 | 1.0 | 0.9916 | 59 | 0.9836 | 1.0 | 0.9917 | 60 | 0.9355 | 0.9508 | 0.9431 | 61 | 0.9833 | 0.9833 | 0.9833 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 0.9836 | 0.9917 | 61 | 0.9833 | 1.0 | 0.9916 | 59 | 1.0 | 0.9836 | 0.9917 | 61 | 0.9865 | 0.9891 | 0.9878 | 0.9974 |
| 0.0078 | 20.0 | 4800 | 0.0293 | 1.0 | 1.0 | 1.0 | 17 | 1.0 | 0.9833 | 0.9916 | 60 | 0.9508 | 0.9831 | 0.9667 | 59 | 0.9833 | 1.0 | 0.9916 | 59 | 0.9836 | 1.0 | 0.9917 | 60 | 0.9 | 0.8852 | 0.8926 | 61 | 0.9661 | 0.95 | 0.9580 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 0.9836 | 0.9917 | 61 | 0.9833 | 1.0 | 0.9916 | 59 | 1.0 | 0.9836 | 0.9917 | 61 | 0.9810 | 0.9810 | 0.9810 | 0.9966 |
| 0.009 | 21.0 | 5040 | 0.0264 | 1.0 | 1.0 | 1.0 | 17 | 1.0 | 0.9833 | 0.9916 | 60 | 0.9206 | 0.9831 | 0.9508 | 59 | 0.9667 | 0.9831 | 0.9748 | 59 | 0.9836 | 1.0 | 0.9917 | 60 | 0.8889 | 0.9180 | 0.9032 | 61 | 0.9672 | 0.9833 | 0.9752 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 1.0 | 1.0 | 60 | 0.9836 | 0.9836 | 0.9836 | 61 | 0.9831 | 0.9831 | 0.9831 | 59 | 1.0 | 0.9836 | 0.9917 | 61 | 0.9745 | 0.9837 | 0.9791 | 0.9969 |
| 0.0046 | 22.0 | 5280 | 0.0271 | 1.0 | 1.0 | 1.0 | 17 | 1.0 | 0.9833 | 0.9916 | 60 | 0.9355 | 0.9831 | 0.9587 | 59 | 0.9667 | 0.9831 | 0.9748 | 59 | 0.9836 | 1.0 | 0.9917 | 60 | 0.9032 | 0.9180 | 0.9106 | 61 | 0.9672 | 0.9833 | 0.9752 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 0.9836 | 0.9917 | 61 | 0.9833 | 1.0 | 0.9916 | 59 | 1.0 | 0.9836 | 0.9917 | 61 | 0.9784 | 0.9851 | 0.9817 | 0.9970 |
| 0.0087 | 23.0 | 5520 | 0.0278 | 0.9444 | 1.0 | 0.9714 | 17 | 1.0 | 0.9833 | 0.9916 | 60 | 0.9194 | 0.9661 | 0.9421 | 59 | 0.9667 | 0.9831 | 0.9748 | 59 | 0.9836 | 1.0 | 0.9917 | 60 | 0.8657 | 0.9508 | 0.9062 | 61 | 0.9836 | 1.0 | 0.9917 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 0.9836 | 0.9917 | 61 | 0.9833 | 1.0 | 0.9916 | 59 | 1.0 | 0.9836 | 0.9917 | 61 | 0.9733 | 0.9878 | 0.9805 | 0.9958 |
| 0.0054 | 24.0 | 5760 | 0.0276 | 0.9444 | 1.0 | 0.9714 | 17 | 1.0 | 0.9833 | 0.9916 | 60 | 0.95 | 0.9661 | 0.9580 | 59 | 0.9833 | 1.0 | 0.9916 | 59 | 0.9836 | 1.0 | 0.9917 | 60 | 0.9355 | 0.9508 | 0.9431 | 61 | 0.9831 | 0.9667 | 0.9748 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 1.0 | 1.0 | 60 | 0.9355 | 0.9667 | 0.9508 | 60 | 1.0 | 0.9836 | 0.9917 | 61 | 0.9833 | 1.0 | 0.9916 | 59 | 1.0 | 0.9836 | 0.9917 | 61 | 0.9784 | 0.9837 | 0.9811 | 0.9971 |
| 0.0057 | 25.0 | 6000 | 0.0260 | 1.0 | 1.0 | 1.0 | 17 | 1.0 | 0.9667 | 0.9831 | 60 | 0.9077 | 1.0 | 0.9516 | 59 | 0.95 | 0.9661 | 0.9580 | 59 | 0.9677 | 1.0 | 0.9836 | 60 | 0.9508 | 0.9508 | 0.9508 | 61 | 1.0 | 0.9833 | 0.9916 | 60 | 0.9833 | 0.9833 | 0.9833 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 0.9672 | 0.9833 | 61 | 0.9672 | 1.0 | 0.9833 | 59 | 1.0 | 0.9836 | 0.9917 | 61 | 0.9771 | 0.9837 | 0.9804 | 0.9971 |
| 0.0074 | 26.0 | 6240 | 0.0340 | 1.0 | 1.0 | 1.0 | 17 | 1.0 | 0.9833 | 0.9916 | 60 | 0.9180 | 0.9492 | 0.9333 | 59 | 0.9667 | 0.9831 | 0.9748 | 59 | 0.9836 | 1.0 | 0.9917 | 60 | 0.8906 | 0.9344 | 0.9120 | 61 | 0.9831 | 0.9667 | 0.9748 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 0.9836 | 0.9917 | 61 | 0.9833 | 1.0 | 0.9916 | 59 | 0.9836 | 0.9836 | 0.9836 | 61 | 0.9757 | 0.9824 | 0.9790 | 0.9959 |
| 0.0047 | 27.0 | 6480 | 0.0306 | 1.0 | 1.0 | 1.0 | 17 | 1.0 | 1.0 | 1.0 | 60 | 0.8923 | 0.9831 | 0.9355 | 59 | 0.9672 | 1.0 | 0.9833 | 59 | 1.0 | 1.0 | 1.0 | 60 | 0.9016 | 0.9016 | 0.9016 | 61 | 0.9667 | 0.9667 | 0.9667 | 60 | 0.9833 | 0.9833 | 0.9833 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 0.9672 | 0.9833 | 61 | 0.8551 | 1.0 | 0.9219 | 59 | 1.0 | 0.8525 | 0.9204 | 61 | 0.9624 | 0.9715 | 0.9669 | 0.9961 |
| 0.0052 | 28.0 | 6720 | 0.0262 | 1.0 | 1.0 | 1.0 | 17 | 1.0 | 0.9833 | 0.9916 | 60 | 0.9667 | 0.9831 | 0.9748 | 59 | 0.9833 | 1.0 | 0.9916 | 59 | 0.9836 | 1.0 | 0.9917 | 60 | 0.9833 | 0.9672 | 0.9752 | 61 | 0.9833 | 0.9833 | 0.9833 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 0.9836 | 0.9917 | 61 | 0.9833 | 1.0 | 0.9916 | 59 | 1.0 | 0.9836 | 0.9917 | 61 | 0.9905 | 0.9905 | 0.9905 | 0.9973 |
| 0.0033 | 29.0 | 6960 | 0.0320 | 0.9444 | 1.0 | 0.9714 | 17 | 1.0 | 0.9833 | 0.9916 | 60 | 0.8406 | 0.9831 | 0.9062 | 59 | 0.9672 | 1.0 | 0.9833 | 59 | 0.9836 | 1.0 | 0.9917 | 60 | 0.8852 | 0.8852 | 0.8852 | 61 | 0.9833 | 0.9833 | 0.9833 | 60 | 1.0 | 0.9667 | 0.9831 | 60 | 1.0 | 1.0 | 1.0 | 60 | 0.9833 | 0.9833 | 0.9833 | 60 | 1.0 | 0.9836 | 0.9917 | 61 | 0.9365 | 1.0 | 0.9672 | 59 | 1.0 | 0.9836 | 0.9917 | 61 | 0.9627 | 0.9796 | 0.9711 | 0.9960 |
| 0.0048 | 30.0 | 7200 | 0.0215 | 1.0 | 1.0 | 1.0 | 17 | 1.0 | 0.9833 | 0.9916 | 60 | 0.9672 | 1.0 | 0.9833 | 59 | 0.9833 | 1.0 | 0.9916 | 59 | 0.9836 | 1.0 | 0.9917 | 60 | 0.9833 | 0.9672 | 0.9752 | 61 | 1.0 | 0.9833 | 0.9916 | 60 | 0.9833 | 0.9833 | 0.9833 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 0.9672 | 0.9833 | 61 | 0.9672 | 1.0 | 0.9833 | 59 | 1.0 | 0.9836 | 0.9917 | 61 | 0.9891 | 0.9891 | 0.9891 | 0.9980 |
### Framework versions
- Transformers 4.22.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|