update model card README.md
Browse files
README.md
ADDED
|
@@ -0,0 +1,121 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
license: cc-by-nc-sa-4.0
|
| 3 |
+
tags:
|
| 4 |
+
- generated_from_trainer
|
| 5 |
+
model-index:
|
| 6 |
+
- name: lmv2-g-recp-992-doc-09-09
|
| 7 |
+
results: []
|
| 8 |
+
---
|
| 9 |
+
|
| 10 |
+
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
|
| 11 |
+
should probably proofread and complete it, then remove this comment. -->
|
| 12 |
+
|
| 13 |
+
# lmv2-g-recp-992-doc-09-09
|
| 14 |
+
|
| 15 |
+
This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/microsoft/layoutlmv2-base-uncased) on the None dataset.
|
| 16 |
+
It achieves the following results on the evaluation set:
|
| 17 |
+
- Loss: 0.2241
|
| 18 |
+
- Purchase Time Precision: 0.872
|
| 19 |
+
- Purchase Time Recall: 0.8516
|
| 20 |
+
- Purchase Time F1: 0.8617
|
| 21 |
+
- Purchase Time Number: 128
|
| 22 |
+
- Receipt Date Precision: 0.8713
|
| 23 |
+
- Receipt Date Recall: 0.8817
|
| 24 |
+
- Receipt Date F1: 0.8765
|
| 25 |
+
- Receipt Date Number: 169
|
| 26 |
+
- Sub Total Precision: 0.8211
|
| 27 |
+
- Sub Total Recall: 0.7091
|
| 28 |
+
- Sub Total F1: 0.7610
|
| 29 |
+
- Sub Total Number: 110
|
| 30 |
+
- Supplier Address Precision: 0.7009
|
| 31 |
+
- Supplier Address Recall: 0.7193
|
| 32 |
+
- Supplier Address F1: 0.7100
|
| 33 |
+
- Supplier Address Number: 114
|
| 34 |
+
- Supplier Name Precision: 0.7442
|
| 35 |
+
- Supplier Name Recall: 0.7191
|
| 36 |
+
- Supplier Name F1: 0.7314
|
| 37 |
+
- Supplier Name Number: 267
|
| 38 |
+
- Tip Amount Precision: 0.6667
|
| 39 |
+
- Tip Amount Recall: 1.0
|
| 40 |
+
- Tip Amount F1: 0.8
|
| 41 |
+
- Tip Amount Number: 2
|
| 42 |
+
- Total Precision: 0.8436
|
| 43 |
+
- Total Recall: 0.8251
|
| 44 |
+
- Total F1: 0.8343
|
| 45 |
+
- Total Number: 183
|
| 46 |
+
- Total Tax Amount Precision: 0.8361
|
| 47 |
+
- Total Tax Amount Recall: 0.7846
|
| 48 |
+
- Total Tax Amount F1: 0.8095
|
| 49 |
+
- Total Tax Amount Number: 65
|
| 50 |
+
- Overall Precision: 0.8067
|
| 51 |
+
- Overall Recall: 0.7842
|
| 52 |
+
- Overall F1: 0.7953
|
| 53 |
+
- Overall Accuracy: 0.9728
|
| 54 |
+
|
| 55 |
+
## Model description
|
| 56 |
+
|
| 57 |
+
More information needed
|
| 58 |
+
|
| 59 |
+
## Intended uses & limitations
|
| 60 |
+
|
| 61 |
+
More information needed
|
| 62 |
+
|
| 63 |
+
## Training and evaluation data
|
| 64 |
+
|
| 65 |
+
More information needed
|
| 66 |
+
|
| 67 |
+
## Training procedure
|
| 68 |
+
|
| 69 |
+
### Training hyperparameters
|
| 70 |
+
|
| 71 |
+
The following hyperparameters were used during training:
|
| 72 |
+
- learning_rate: 4e-05
|
| 73 |
+
- train_batch_size: 1
|
| 74 |
+
- eval_batch_size: 1
|
| 75 |
+
- seed: 42
|
| 76 |
+
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
|
| 77 |
+
- lr_scheduler_type: constant
|
| 78 |
+
- num_epochs: 30
|
| 79 |
+
|
| 80 |
+
### Training results
|
| 81 |
+
|
| 82 |
+
| Training Loss | Epoch | Step | Validation Loss | Purchase Time Precision | Purchase Time Recall | Purchase Time F1 | Purchase Time Number | Receipt Date Precision | Receipt Date Recall | Receipt Date F1 | Receipt Date Number | Sub Total Precision | Sub Total Recall | Sub Total F1 | Sub Total Number | Supplier Address Precision | Supplier Address Recall | Supplier Address F1 | Supplier Address Number | Supplier Name Precision | Supplier Name Recall | Supplier Name F1 | Supplier Name Number | Tip Amount Precision | Tip Amount Recall | Tip Amount F1 | Tip Amount Number | Total Precision | Total Recall | Total F1 | Total Number | Total Tax Amount Precision | Total Tax Amount Recall | Total Tax Amount F1 | Total Tax Amount Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|
| 83 |
+
|:-------------:|:-----:|:-----:|:---------------:|:-----------------------:|:--------------------:|:----------------:|:--------------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:-------------------:|:----------------:|:------------:|:----------------:|:--------------------------:|:-----------------------:|:-------------------:|:-----------------------:|:-----------------------:|:--------------------:|:----------------:|:--------------------:|:--------------------:|:-----------------:|:-------------:|:-----------------:|:---------------:|:------------:|:--------:|:------------:|:--------------------------:|:-----------------------:|:-------------------:|:-----------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
|
| 84 |
+
| 0.9017 | 1.0 | 793 | 0.3748 | 0.0 | 0.0 | 0.0 | 128 | 0.5 | 0.0710 | 0.1244 | 169 | 0.0 | 0.0 | 0.0 | 110 | 0.4632 | 0.5526 | 0.504 | 114 | 0.3724 | 0.2022 | 0.2621 | 267 | 0.0 | 0.0 | 0.0 | 2 | 0.7387 | 0.4481 | 0.5578 | 183 | 0.0 | 0.0 | 0.0 | 65 | 0.4637 | 0.2033 | 0.2827 | 0.9330 |
|
| 85 |
+
| 0.2651 | 2.0 | 1586 | 0.2025 | 0.8 | 0.8438 | 0.8213 | 128 | 0.8274 | 0.8225 | 0.8249 | 169 | 0.4 | 0.0182 | 0.0348 | 110 | 0.5329 | 0.7105 | 0.6090 | 114 | 0.5886 | 0.6592 | 0.6219 | 267 | 0.0 | 0.0 | 0.0 | 2 | 0.5720 | 0.8470 | 0.6828 | 183 | 1.0 | 0.0308 | 0.0597 | 65 | 0.6424 | 0.6387 | 0.6406 | 0.9624 |
|
| 86 |
+
| 0.1403 | 3.0 | 2379 | 0.1585 | 0.8248 | 0.8828 | 0.8528 | 128 | 0.7897 | 0.9112 | 0.8462 | 169 | 0.7054 | 0.7182 | 0.7117 | 110 | 0.5931 | 0.7544 | 0.6641 | 114 | 0.6288 | 0.6217 | 0.6252 | 267 | 0.0 | 0.0 | 0.0 | 2 | 0.7877 | 0.7705 | 0.7790 | 183 | 0.8276 | 0.7385 | 0.7805 | 65 | 0.7220 | 0.7582 | 0.7397 | 0.9683 |
|
| 87 |
+
| 0.0935 | 4.0 | 3172 | 0.1771 | 0.7891 | 0.7891 | 0.7891 | 128 | 0.6474 | 0.7278 | 0.6852 | 169 | 0.8205 | 0.5818 | 0.6809 | 110 | 0.6074 | 0.7193 | 0.6586 | 114 | 0.6548 | 0.6891 | 0.6715 | 267 | 0.0 | 0.0 | 0.0 | 2 | 0.8476 | 0.7596 | 0.8012 | 183 | 0.75 | 0.2308 | 0.3529 | 65 | 0.7108 | 0.6821 | 0.6962 | 0.9648 |
|
| 88 |
+
| 0.0684 | 5.0 | 3965 | 0.1552 | 0.9237 | 0.8516 | 0.8862 | 128 | 0.8362 | 0.8757 | 0.8555 | 169 | 0.7629 | 0.6727 | 0.7150 | 110 | 0.6029 | 0.7193 | 0.6560 | 114 | 0.7167 | 0.6442 | 0.6785 | 267 | 0.0 | 0.0 | 0.0 | 2 | 0.8128 | 0.8306 | 0.8216 | 183 | 0.7937 | 0.7692 | 0.7813 | 65 | 0.7731 | 0.7582 | 0.7656 | 0.9696 |
|
| 89 |
+
| 0.0491 | 6.0 | 4758 | 0.1702 | 0.8760 | 0.8828 | 0.8794 | 128 | 0.8352 | 0.8698 | 0.8522 | 169 | 0.8056 | 0.7909 | 0.7982 | 110 | 0.5894 | 0.7807 | 0.6717 | 114 | 0.6844 | 0.6742 | 0.6792 | 267 | 0.0 | 0.0 | 0.0 | 2 | 0.8778 | 0.8634 | 0.8705 | 183 | 0.9074 | 0.7538 | 0.8235 | 65 | 0.7757 | 0.7929 | 0.7842 | 0.9703 |
|
| 90 |
+
| 0.0472 | 7.0 | 5551 | 0.2037 | 0.8952 | 0.8672 | 0.8810 | 128 | 0.8876 | 0.8876 | 0.8876 | 169 | 0.8 | 0.7273 | 0.7619 | 110 | 0.6557 | 0.7018 | 0.6780 | 114 | 0.7953 | 0.6404 | 0.7095 | 267 | 0.0 | 0.0 | 0.0 | 2 | 0.8795 | 0.7978 | 0.8367 | 183 | 0.9394 | 0.4769 | 0.6327 | 65 | 0.8278 | 0.7408 | 0.7819 | 0.9701 |
|
| 91 |
+
| 0.0361 | 8.0 | 6344 | 0.1862 | 0.875 | 0.8203 | 0.8468 | 128 | 0.7978 | 0.8402 | 0.8184 | 169 | 0.7739 | 0.8091 | 0.7911 | 110 | 0.6512 | 0.7368 | 0.6914 | 114 | 0.6906 | 0.6854 | 0.6880 | 267 | 0.0 | 0.0 | 0.0 | 2 | 0.8486 | 0.8579 | 0.8533 | 183 | 0.6780 | 0.6154 | 0.6452 | 65 | 0.7612 | 0.7707 | 0.7659 | 0.9701 |
|
| 92 |
+
| 0.0318 | 9.0 | 7137 | 0.1889 | 0.9 | 0.8438 | 0.8710 | 128 | 0.8743 | 0.8639 | 0.8690 | 169 | 0.875 | 0.6364 | 0.7368 | 110 | 0.6417 | 0.6754 | 0.6581 | 114 | 0.6914 | 0.6966 | 0.6940 | 267 | 0.0 | 0.0 | 0.0 | 2 | 0.7833 | 0.8689 | 0.8238 | 183 | 0.7797 | 0.7077 | 0.7419 | 65 | 0.7772 | 0.7630 | 0.7701 | 0.9697 |
|
| 93 |
+
| 0.3481 | 10.0 | 7930 | 0.7581 | 0.0 | 0.0 | 0.0 | 128 | 0.0 | 0.0 | 0.0 | 169 | 0.0 | 0.0 | 0.0 | 110 | 0.0 | 0.0 | 0.0 | 114 | 0.0 | 0.0 | 0.0 | 267 | 0.0 | 0.0 | 0.0 | 2 | 0.0 | 0.0 | 0.0 | 183 | 0.0 | 0.0 | 0.0 | 65 | 0.0 | 0.0 | 0.0 | 0.8967 |
|
| 94 |
+
| 0.7157 | 11.0 | 8723 | 0.7634 | 0.0 | 0.0 | 0.0 | 128 | 0.0 | 0.0 | 0.0 | 169 | 0.0 | 0.0 | 0.0 | 110 | 0.0 | 0.0 | 0.0 | 114 | 0.0 | 0.0 | 0.0 | 267 | 0.0 | 0.0 | 0.0 | 2 | 0.0 | 0.0 | 0.0 | 183 | 0.0 | 0.0 | 0.0 | 65 | 0.0 | 0.0 | 0.0 | 0.8967 |
|
| 95 |
+
| 0.7136 | 12.0 | 9516 | 0.7611 | 0.0 | 0.0 | 0.0 | 128 | 0.0 | 0.0 | 0.0 | 169 | 0.0 | 0.0 | 0.0 | 110 | 0.0 | 0.0 | 0.0 | 114 | 0.0 | 0.0 | 0.0 | 267 | 0.0 | 0.0 | 0.0 | 2 | 0.0 | 0.0 | 0.0 | 183 | 0.0 | 0.0 | 0.0 | 65 | 0.0 | 0.0 | 0.0 | 0.8967 |
|
| 96 |
+
| 0.1095 | 13.0 | 10309 | 0.1744 | 0.8284 | 0.8672 | 0.8473 | 128 | 0.8531 | 0.8935 | 0.8728 | 169 | 0.7717 | 0.6455 | 0.7030 | 110 | 0.5662 | 0.6754 | 0.6160 | 114 | 0.6424 | 0.6929 | 0.6667 | 267 | 0.0 | 0.0 | 0.0 | 2 | 0.8211 | 0.8525 | 0.8365 | 183 | 0.8214 | 0.7077 | 0.7603 | 65 | 0.7428 | 0.7678 | 0.7551 | 0.9698 |
|
| 97 |
+
| 0.0316 | 14.0 | 11102 | 0.1812 | 0.8943 | 0.8594 | 0.8765 | 128 | 0.8409 | 0.8757 | 0.8580 | 169 | 0.8415 | 0.6273 | 0.7188 | 110 | 0.5714 | 0.6667 | 0.6154 | 114 | 0.6279 | 0.7079 | 0.6655 | 267 | 1.0 | 0.5 | 0.6667 | 2 | 0.8256 | 0.8798 | 0.8519 | 183 | 0.8136 | 0.7385 | 0.7742 | 65 | 0.7495 | 0.7726 | 0.7609 | 0.9703 |
|
| 98 |
+
| 0.0226 | 15.0 | 11895 | 0.2132 | 0.8843 | 0.8359 | 0.8594 | 128 | 0.8476 | 0.8225 | 0.8348 | 169 | 0.7525 | 0.6909 | 0.7204 | 110 | 0.5804 | 0.7281 | 0.6459 | 114 | 0.6679 | 0.6929 | 0.6801 | 267 | 0.2 | 0.5 | 0.2857 | 2 | 0.8571 | 0.8525 | 0.8548 | 183 | 0.4835 | 0.6769 | 0.5641 | 65 | 0.7297 | 0.7620 | 0.7455 | 0.9672 |
|
| 99 |
+
| 0.0241 | 16.0 | 12688 | 0.1962 | 0.8984 | 0.8984 | 0.8984 | 128 | 0.8613 | 0.8817 | 0.8713 | 169 | 0.6615 | 0.7818 | 0.7167 | 110 | 0.6 | 0.7368 | 0.6614 | 114 | 0.6431 | 0.7154 | 0.6773 | 267 | 0.0833 | 0.5 | 0.1429 | 2 | 0.8795 | 0.7978 | 0.8367 | 183 | 0.7727 | 0.7846 | 0.7786 | 65 | 0.7401 | 0.7929 | 0.7656 | 0.9709 |
|
| 100 |
+
| 0.0155 | 17.0 | 13481 | 0.1995 | 0.8906 | 0.8906 | 0.8906 | 128 | 0.8678 | 0.8935 | 0.8805 | 169 | 0.7438 | 0.8182 | 0.7792 | 110 | 0.6042 | 0.7632 | 0.6744 | 114 | 0.6193 | 0.7678 | 0.6856 | 267 | 1.0 | 0.5 | 0.6667 | 2 | 0.8325 | 0.8689 | 0.8503 | 183 | 0.8644 | 0.7846 | 0.8226 | 65 | 0.7467 | 0.8266 | 0.7846 | 0.9696 |
|
| 101 |
+
| 0.0165 | 18.0 | 14274 | 0.2402 | 0.8966 | 0.8125 | 0.8525 | 128 | 0.8293 | 0.8047 | 0.8168 | 169 | 0.8118 | 0.6273 | 0.7077 | 110 | 0.5766 | 0.6930 | 0.6295 | 114 | 0.7220 | 0.6517 | 0.6850 | 267 | 1.0 | 1.0 | 1.0 | 2 | 0.8603 | 0.8415 | 0.8508 | 183 | 0.7826 | 0.5538 | 0.6486 | 65 | 0.7773 | 0.7264 | 0.7510 | 0.9683 |
|
| 102 |
+
| 0.0721 | 19.0 | 15067 | 0.2718 | 0.3506 | 0.6328 | 0.4513 | 128 | 0.7268 | 0.7870 | 0.7557 | 169 | 0.7742 | 0.4364 | 0.5581 | 110 | 0.5271 | 0.5965 | 0.5597 | 114 | 0.5294 | 0.5056 | 0.5172 | 267 | 0.0 | 0.0 | 0.0 | 2 | 0.7526 | 0.7978 | 0.7745 | 183 | 0.7414 | 0.6615 | 0.6992 | 65 | 0.5881 | 0.6301 | 0.6084 | 0.9564 |
|
| 103 |
+
| 0.0136 | 20.0 | 15860 | 0.2213 | 0.8651 | 0.8516 | 0.8583 | 128 | 0.8555 | 0.8757 | 0.8655 | 169 | 0.8191 | 0.7 | 0.7549 | 110 | 0.6103 | 0.7281 | 0.664 | 114 | 0.6977 | 0.6742 | 0.6857 | 267 | 1.0 | 0.5 | 0.6667 | 2 | 0.8571 | 0.8197 | 0.8380 | 183 | 0.7656 | 0.7538 | 0.7597 | 65 | 0.7760 | 0.7678 | 0.7719 | 0.9697 |
|
| 104 |
+
| 0.0111 | 21.0 | 16653 | 0.2241 | 0.872 | 0.8516 | 0.8617 | 128 | 0.8713 | 0.8817 | 0.8765 | 169 | 0.8211 | 0.7091 | 0.7610 | 110 | 0.7009 | 0.7193 | 0.7100 | 114 | 0.7442 | 0.7191 | 0.7314 | 267 | 0.6667 | 1.0 | 0.8 | 2 | 0.8436 | 0.8251 | 0.8343 | 183 | 0.8361 | 0.7846 | 0.8095 | 65 | 0.8067 | 0.7842 | 0.7953 | 0.9728 |
|
| 105 |
+
| 0.011 | 22.0 | 17446 | 0.2206 | 0.7770 | 0.8984 | 0.8333 | 128 | 0.8270 | 0.9053 | 0.8644 | 169 | 0.8586 | 0.7727 | 0.8134 | 110 | 0.5985 | 0.6930 | 0.6423 | 114 | 0.6618 | 0.6742 | 0.6679 | 267 | 0.0 | 0.0 | 0.0 | 2 | 0.8870 | 0.8579 | 0.8722 | 183 | 0.7391 | 0.7846 | 0.7612 | 65 | 0.7579 | 0.7900 | 0.7736 | 0.9697 |
|
| 106 |
+
| 0.0104 | 23.0 | 18239 | 0.2571 | 0.9310 | 0.8438 | 0.8852 | 128 | 0.875 | 0.8698 | 0.8724 | 169 | 0.8316 | 0.7182 | 0.7707 | 110 | 0.6417 | 0.6754 | 0.6581 | 114 | 0.7386 | 0.6667 | 0.7008 | 267 | 0.1429 | 0.5 | 0.2222 | 2 | 0.8579 | 0.8579 | 0.8579 | 183 | 0.7812 | 0.7692 | 0.7752 | 65 | 0.8018 | 0.7678 | 0.7844 | 0.9705 |
|
| 107 |
+
| 0.0132 | 24.0 | 19032 | 0.2252 | 0.8810 | 0.8672 | 0.8740 | 128 | 0.8297 | 0.8935 | 0.8604 | 169 | 0.7607 | 0.8091 | 0.7841 | 110 | 0.6074 | 0.7193 | 0.6586 | 114 | 0.6578 | 0.7416 | 0.6972 | 267 | 0.3333 | 1.0 | 0.5 | 2 | 0.8659 | 0.8470 | 0.8564 | 183 | 0.7966 | 0.7231 | 0.7581 | 65 | 0.7557 | 0.8044 | 0.7793 | 0.9717 |
|
| 108 |
+
| 0.0114 | 25.0 | 19825 | 0.2303 | 0.8917 | 0.8359 | 0.8629 | 128 | 0.8947 | 0.9053 | 0.9000 | 169 | 0.8144 | 0.7182 | 0.7633 | 110 | 0.6296 | 0.7456 | 0.6827 | 114 | 0.6937 | 0.7041 | 0.6989 | 267 | 1.0 | 0.5 | 0.6667 | 2 | 0.8533 | 0.8579 | 0.8556 | 183 | 0.8913 | 0.6308 | 0.7387 | 65 | 0.7912 | 0.7813 | 0.7862 | 0.9705 |
|
| 109 |
+
| 0.0121 | 26.0 | 20618 | 0.2485 | 0.8810 | 0.8672 | 0.8740 | 128 | 0.8793 | 0.9053 | 0.8921 | 169 | 0.8667 | 0.7091 | 0.7800 | 110 | 0.5926 | 0.7018 | 0.6426 | 114 | 0.7446 | 0.6442 | 0.6908 | 267 | 0.25 | 0.5 | 0.3333 | 2 | 0.8361 | 0.8361 | 0.8361 | 183 | 0.7581 | 0.7231 | 0.7402 | 65 | 0.7910 | 0.7659 | 0.7783 | 0.9705 |
|
| 110 |
+
| 0.0124 | 27.0 | 21411 | 0.2280 | 0.8504 | 0.8438 | 0.8471 | 128 | 0.8391 | 0.8639 | 0.8513 | 169 | 0.8119 | 0.7455 | 0.7773 | 110 | 0.6435 | 0.6491 | 0.6463 | 114 | 0.6259 | 0.6891 | 0.6560 | 267 | 0.4 | 1.0 | 0.5714 | 2 | 0.8548 | 0.8689 | 0.8618 | 183 | 0.8627 | 0.6769 | 0.7586 | 65 | 0.7588 | 0.7697 | 0.7642 | 0.9702 |
|
| 111 |
+
| 0.0111 | 28.0 | 22204 | 0.2728 | 0.8917 | 0.8359 | 0.8629 | 128 | 0.8704 | 0.8343 | 0.8520 | 169 | 0.9059 | 0.7 | 0.7897 | 110 | 0.5833 | 0.6754 | 0.6260 | 114 | 0.6618 | 0.6816 | 0.6716 | 267 | 1.0 | 0.5 | 0.6667 | 2 | 0.8713 | 0.8142 | 0.8418 | 183 | 0.8837 | 0.5846 | 0.7037 | 65 | 0.7806 | 0.7437 | 0.7617 | 0.9692 |
|
| 112 |
+
| 0.0079 | 29.0 | 22997 | 0.2596 | 0.8661 | 0.8594 | 0.8627 | 128 | 0.8817 | 0.8817 | 0.8817 | 169 | 0.7436 | 0.7909 | 0.7665 | 110 | 0.616 | 0.6754 | 0.6444 | 114 | 0.6794 | 0.6667 | 0.6730 | 267 | 1.0 | 1.0 | 1.0 | 2 | 0.8681 | 0.8634 | 0.8658 | 183 | 0.8727 | 0.7385 | 0.8 | 65 | 0.7786 | 0.7794 | 0.7790 | 0.9705 |
|
| 113 |
+
| 0.0076 | 30.0 | 23790 | 0.2476 | 0.8088 | 0.8594 | 0.8333 | 128 | 0.8889 | 0.8994 | 0.8941 | 169 | 0.7909 | 0.7909 | 0.7909 | 110 | 0.6397 | 0.7632 | 0.6960 | 114 | 0.6727 | 0.6929 | 0.6827 | 267 | 0.3333 | 1.0 | 0.5 | 2 | 0.8641 | 0.8689 | 0.8665 | 183 | 0.6512 | 0.8615 | 0.7417 | 65 | 0.7591 | 0.8073 | 0.7824 | 0.9705 |
|
| 114 |
+
|
| 115 |
+
|
| 116 |
+
### Framework versions
|
| 117 |
+
|
| 118 |
+
- Transformers 4.22.0.dev0
|
| 119 |
+
- Pytorch 1.12.1+cu113
|
| 120 |
+
- Datasets 2.2.2
|
| 121 |
+
- Tokenizers 0.12.1
|