metadata
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
base_model: microsoft/layoutlmv2-base-uncased
model-index:
- name: lmv2-g-dl-243-doc-09-13
results: []
lmv2-g-dl-243-doc-09-13
This model is a fine-tuned version of microsoft/layoutlmv2-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2104
- Address Precision: 0.725
- Address Recall: 0.7632
- Address F1: 0.7436
- Address Number: 38
- Blood Group Precision: 0.8636
- Blood Group Recall: 0.8636
- Blood Group F1: 0.8636
- Blood Group Number: 22
- Date Of Issue Precision: 0.9787
- Date Of Issue Recall: 0.92
- Date Of Issue F1: 0.9485
- Date Of Issue Number: 50
- Dob Precision: 1.0
- Dob Recall: 0.9773
- Dob F1: 0.9885
- Dob Number: 44
- Driving Licence No Precision: 0.9796
- Driving Licence No Recall: 1.0
- Driving Licence No F1: 0.9897
- Driving Licence No Number: 48
- Name Precision: 0.9388
- Name Recall: 0.9388
- Name F1: 0.9388
- Name Number: 49
- S D W Name Precision: 0.9388
- S D W Name Recall: 0.9583
- S D W Name F1: 0.9485
- S D W Name Number: 48
- Valid Till Nt Precision: 0.7826
- Valid Till Nt Recall: 0.8780
- Valid Till Nt F1: 0.8276
- Valid Till Nt Number: 41
- Valid Till T Tr Precision: 0.9231
- Valid Till T Tr Recall: 0.8571
- Valid Till T Tr F1: 0.8889
- Valid Till T Tr Number: 14
- Overall Precision: 0.9078
- Overall Recall: 0.9181
- Overall F1: 0.9129
- Overall Accuracy: 0.9763
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 | Address Precision | Address Recall | Address F1 | Address Number | Blood Group Precision | Blood Group Recall | Blood Group F1 | Blood Group Number | Date Of Issue Precision | Date Of Issue Recall | Date Of Issue F1 | Date Of Issue Number | Dob Precision | Dob Recall | Dob F1 | Dob Number | Driving Licence No Precision | Driving Licence No Recall | Driving Licence No F1 | Driving Licence No Number | Name Precision | Name Recall | Name F1 | Name Number | S D W Name Precision | S D W Name Recall | S D W Name F1 | S D W Name Number | Valid Till Nt Precision | Valid Till Nt Recall | Valid Till Nt F1 | Valid Till Nt Number | Valid Till T Tr Precision | Valid Till T Tr Recall | Valid Till T Tr F1 | Valid Till T Tr Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1.9194 | 1.0 | 194 | 1.3597 | 0.3099 | 0.5789 | 0.4037 | 38 | 0.0 | 0.0 | 0.0 | 22 | 0.3147 | 0.9 | 0.4663 | 50 | 0.0 | 0.0 | 0.0 | 44 | 0.1059 | 0.1875 | 0.1353 | 48 | 0.0246 | 0.0612 | 0.0351 | 49 | 0.0 | 0.0 | 0.0 | 48 | 0.0 | 0.0 | 0.0 | 41 | 0.0 | 0.0 | 0.0 | 14 | 0.1876 | 0.2232 | 0.2039 | 0.8582 |
1.099 | 2.0 | 388 | 0.7767 | 0.5333 | 0.6316 | 0.5783 | 38 | 0.0 | 0.0 | 0.0 | 22 | 0.4667 | 0.98 | 0.6323 | 50 | 0.9773 | 0.9773 | 0.9773 | 44 | 0.9057 | 1.0 | 0.9505 | 48 | 0.3070 | 0.7143 | 0.4294 | 49 | 0.0 | 0.0 | 0.0 | 48 | 0.8182 | 0.2195 | 0.3462 | 41 | 0.0 | 0.0 | 0.0 | 14 | 0.5591 | 0.5876 | 0.5730 | 0.9016 |
0.6398 | 3.0 | 582 | 0.4892 | 0.5532 | 0.6842 | 0.6118 | 38 | 0.0 | 0.0 | 0.0 | 22 | 1.0 | 0.94 | 0.9691 | 50 | 1.0 | 0.9091 | 0.9524 | 44 | 0.9412 | 1.0 | 0.9697 | 48 | 0.5538 | 0.7347 | 0.6316 | 49 | 0.5714 | 0.75 | 0.6486 | 48 | 0.6863 | 0.8537 | 0.7609 | 41 | 0.0 | 0.0 | 0.0 | 14 | 0.7363 | 0.7571 | 0.7465 | 0.9491 |
0.4251 | 4.0 | 776 | 0.3770 | 0.4364 | 0.6316 | 0.5161 | 38 | 0.4706 | 0.3636 | 0.4103 | 22 | 1.0 | 0.9 | 0.9474 | 50 | 1.0 | 0.9773 | 0.9885 | 44 | 0.9412 | 1.0 | 0.9697 | 48 | 0.92 | 0.9388 | 0.9293 | 49 | 0.9333 | 0.875 | 0.9032 | 48 | 0.6182 | 0.8293 | 0.7083 | 41 | 0.0 | 0.0 | 0.0 | 14 | 0.8033 | 0.8192 | 0.8112 | 0.9557 |
0.304 | 5.0 | 970 | 0.2942 | 0.65 | 0.6842 | 0.6667 | 38 | 0.4643 | 0.5909 | 0.52 | 22 | 0.9796 | 0.96 | 0.9697 | 50 | 1.0 | 0.9773 | 0.9885 | 44 | 0.8727 | 1.0 | 0.9320 | 48 | 0.9388 | 0.9388 | 0.9388 | 49 | 0.9388 | 0.9583 | 0.9485 | 48 | 0.85 | 0.8293 | 0.8395 | 41 | 0.8 | 0.2857 | 0.4211 | 14 | 0.8603 | 0.8701 | 0.8652 | 0.9627 |
0.2297 | 6.0 | 1164 | 0.2497 | 0.5854 | 0.6316 | 0.6076 | 38 | 0.5294 | 0.8182 | 0.6429 | 22 | 1.0 | 0.92 | 0.9583 | 50 | 1.0 | 0.9773 | 0.9885 | 44 | 0.9231 | 1.0 | 0.9600 | 48 | 0.9149 | 0.8776 | 0.8958 | 49 | 0.9184 | 0.9375 | 0.9278 | 48 | 0.8462 | 0.8049 | 0.8250 | 41 | 0.7692 | 0.7143 | 0.7407 | 14 | 0.8516 | 0.8757 | 0.8635 | 0.9679 |
0.1843 | 7.0 | 1358 | 0.2123 | 0.54 | 0.7105 | 0.6136 | 38 | 0.7778 | 0.9545 | 0.8571 | 22 | 0.9792 | 0.94 | 0.9592 | 50 | 1.0 | 0.9773 | 0.9885 | 44 | 0.9796 | 1.0 | 0.9897 | 48 | 0.9348 | 0.8776 | 0.9053 | 49 | 0.9020 | 0.9583 | 0.9293 | 48 | 0.7609 | 0.8537 | 0.8046 | 41 | 0.8 | 0.5714 | 0.6667 | 14 | 0.8595 | 0.8983 | 0.8785 | 0.9696 |
0.1455 | 8.0 | 1552 | 0.2166 | 0.6316 | 0.6316 | 0.6316 | 38 | 0.8077 | 0.9545 | 0.875 | 22 | 1.0 | 0.94 | 0.9691 | 50 | 1.0 | 0.9773 | 0.9885 | 44 | 0.9216 | 0.9792 | 0.9495 | 48 | 0.9167 | 0.8980 | 0.9072 | 49 | 0.9167 | 0.9167 | 0.9167 | 48 | 0.7826 | 0.8780 | 0.8276 | 41 | 0.9286 | 0.9286 | 0.9286 | 14 | 0.8837 | 0.9011 | 0.8923 | 0.9696 |
0.1262 | 9.0 | 1746 | 0.2060 | 0.5333 | 0.6316 | 0.5783 | 38 | 0.8333 | 0.9091 | 0.8696 | 22 | 0.96 | 0.96 | 0.96 | 50 | 1.0 | 0.9773 | 0.9885 | 44 | 0.9216 | 0.9792 | 0.9495 | 48 | 0.9 | 0.9184 | 0.9091 | 49 | 0.9184 | 0.9375 | 0.9278 | 48 | 0.7826 | 0.8780 | 0.8276 | 41 | 0.5185 | 1.0 | 0.6829 | 14 | 0.8364 | 0.9096 | 0.8714 | 0.9661 |
0.109 | 10.0 | 1940 | 0.1912 | 0.6154 | 0.6316 | 0.6234 | 38 | 0.7692 | 0.9091 | 0.8333 | 22 | 0.9792 | 0.94 | 0.9592 | 50 | 1.0 | 0.9773 | 0.9885 | 44 | 0.9796 | 1.0 | 0.9897 | 48 | 0.9388 | 0.9388 | 0.9388 | 49 | 0.9362 | 0.9167 | 0.9263 | 48 | 0.8537 | 0.8537 | 0.8537 | 41 | 0.9333 | 1.0 | 0.9655 | 14 | 0.8992 | 0.9068 | 0.9030 | 0.9725 |
0.0911 | 11.0 | 2134 | 0.2063 | 0.5897 | 0.6053 | 0.5974 | 38 | 0.8 | 0.9091 | 0.8511 | 22 | 0.9412 | 0.96 | 0.9505 | 50 | 1.0 | 0.9773 | 0.9885 | 44 | 0.9412 | 1.0 | 0.9697 | 48 | 0.9184 | 0.9184 | 0.9184 | 49 | 0.9375 | 0.9375 | 0.9375 | 48 | 0.8 | 0.8780 | 0.8372 | 41 | 0.8235 | 1.0 | 0.9032 | 14 | 0.875 | 0.9096 | 0.8920 | 0.9690 |
0.0771 | 12.0 | 2328 | 0.2262 | 0.525 | 0.5526 | 0.5385 | 38 | 0.8333 | 0.9091 | 0.8696 | 22 | 1.0 | 0.98 | 0.9899 | 50 | 1.0 | 0.9773 | 0.9885 | 44 | 0.9184 | 0.9375 | 0.9278 | 48 | 0.9388 | 0.9388 | 0.9388 | 49 | 0.9375 | 0.9375 | 0.9375 | 48 | 0.8537 | 0.8537 | 0.8537 | 41 | 0.8571 | 0.8571 | 0.8571 | 14 | 0.8852 | 0.8927 | 0.8889 | 0.9638 |
0.0753 | 13.0 | 2522 | 0.2170 | 0.5714 | 0.6316 | 0.6 | 38 | 0.8 | 0.9091 | 0.8511 | 22 | 0.9796 | 0.96 | 0.9697 | 50 | 1.0 | 0.9773 | 0.9885 | 44 | 0.9412 | 1.0 | 0.9697 | 48 | 0.9375 | 0.9184 | 0.9278 | 49 | 0.9574 | 0.9375 | 0.9474 | 48 | 0.875 | 0.8537 | 0.8642 | 41 | 0.7059 | 0.8571 | 0.7742 | 14 | 0.8840 | 0.9040 | 0.8939 | 0.9673 |
0.0676 | 14.0 | 2716 | 0.2148 | 0.5610 | 0.6053 | 0.5823 | 38 | 0.8261 | 0.8636 | 0.8444 | 22 | 0.9245 | 0.98 | 0.9515 | 50 | 1.0 | 0.9773 | 0.9885 | 44 | 0.96 | 1.0 | 0.9796 | 48 | 0.9362 | 0.8980 | 0.9167 | 49 | 0.9130 | 0.875 | 0.8936 | 48 | 0.8182 | 0.8780 | 0.8471 | 41 | 0.9333 | 1.0 | 0.9655 | 14 | 0.8785 | 0.8983 | 0.8883 | 0.9670 |
0.0588 | 15.0 | 2910 | 0.2140 | 0.65 | 0.6842 | 0.6667 | 38 | 0.7241 | 0.9545 | 0.8235 | 22 | 0.9792 | 0.94 | 0.9592 | 50 | 1.0 | 0.9773 | 0.9885 | 44 | 0.9412 | 1.0 | 0.9697 | 48 | 0.8824 | 0.9184 | 0.9 | 49 | 0.9130 | 0.875 | 0.8936 | 48 | 0.8 | 0.8780 | 0.8372 | 41 | 0.9286 | 0.9286 | 0.9286 | 14 | 0.8747 | 0.9068 | 0.8904 | 0.9690 |
0.0592 | 16.0 | 3104 | 0.2353 | 0.6410 | 0.6579 | 0.6494 | 38 | 0.75 | 0.9545 | 0.84 | 22 | 0.9767 | 0.84 | 0.9032 | 50 | 1.0 | 0.9773 | 0.9885 | 44 | 0.9796 | 1.0 | 0.9897 | 48 | 0.9362 | 0.8980 | 0.9167 | 49 | 0.9574 | 0.9375 | 0.9474 | 48 | 0.8571 | 0.8780 | 0.8675 | 41 | 0.9333 | 1.0 | 0.9655 | 14 | 0.9008 | 0.8983 | 0.8996 | 0.9664 |
0.0461 | 17.0 | 3298 | 0.2137 | 0.5714 | 0.6316 | 0.6 | 38 | 0.7143 | 0.9091 | 0.8 | 22 | 0.9057 | 0.96 | 0.9320 | 50 | 1.0 | 0.9773 | 0.9885 | 44 | 0.9796 | 1.0 | 0.9897 | 48 | 0.9388 | 0.9388 | 0.9388 | 49 | 0.9184 | 0.9375 | 0.9278 | 48 | 0.8571 | 0.8780 | 0.8675 | 41 | 0.7368 | 1.0 | 0.8485 | 14 | 0.8663 | 0.9153 | 0.8901 | 0.9685 |
0.0424 | 18.0 | 3492 | 0.2057 | 0.5610 | 0.6053 | 0.5823 | 38 | 0.84 | 0.9545 | 0.8936 | 22 | 0.9423 | 0.98 | 0.9608 | 50 | 1.0 | 0.9773 | 0.9885 | 44 | 0.9796 | 1.0 | 0.9897 | 48 | 0.9388 | 0.9388 | 0.9388 | 49 | 0.9565 | 0.9167 | 0.9362 | 48 | 0.8095 | 0.8293 | 0.8193 | 41 | 0.8667 | 0.9286 | 0.8966 | 14 | 0.8867 | 0.9068 | 0.8966 | 0.9708 |
0.0389 | 19.0 | 3686 | 0.2400 | 0.6098 | 0.6579 | 0.6329 | 38 | 0.8 | 0.9091 | 0.8511 | 22 | 0.9574 | 0.9 | 0.9278 | 50 | 1.0 | 0.9773 | 0.9885 | 44 | 0.9796 | 1.0 | 0.9897 | 48 | 0.86 | 0.8776 | 0.8687 | 49 | 0.8980 | 0.9167 | 0.9072 | 48 | 0.8537 | 0.8537 | 0.8537 | 41 | 0.9167 | 0.7857 | 0.8462 | 14 | 0.8796 | 0.8870 | 0.8833 | 0.9670 |
0.0375 | 20.0 | 3880 | 0.2258 | 0.6341 | 0.6842 | 0.6582 | 38 | 0.8636 | 0.8636 | 0.8636 | 22 | 1.0 | 0.96 | 0.9796 | 50 | 1.0 | 0.9773 | 0.9885 | 44 | 0.9796 | 1.0 | 0.9897 | 48 | 0.9388 | 0.9388 | 0.9388 | 49 | 0.9565 | 0.9167 | 0.9362 | 48 | 0.8718 | 0.8293 | 0.8500 | 41 | 0.75 | 0.8571 | 0.8000 | 14 | 0.9065 | 0.9040 | 0.9052 | 0.9693 |
0.036 | 21.0 | 4074 | 0.2686 | 0.5952 | 0.6579 | 0.625 | 38 | 0.7778 | 0.9545 | 0.8571 | 22 | 1.0 | 0.96 | 0.9796 | 50 | 1.0 | 0.9773 | 0.9885 | 44 | 0.9796 | 1.0 | 0.9897 | 48 | 0.9362 | 0.8980 | 0.9167 | 49 | 0.9565 | 0.9167 | 0.9362 | 48 | 0.8684 | 0.8049 | 0.8354 | 41 | 0.7059 | 0.8571 | 0.7742 | 14 | 0.8908 | 0.8983 | 0.8945 | 0.9644 |
0.0321 | 22.0 | 4268 | 0.2102 | 0.6923 | 0.7105 | 0.7013 | 38 | 0.7778 | 0.9545 | 0.8571 | 22 | 0.96 | 0.96 | 0.96 | 50 | 1.0 | 0.9773 | 0.9885 | 44 | 0.9796 | 1.0 | 0.9897 | 48 | 0.92 | 0.9388 | 0.9293 | 49 | 0.9362 | 0.9167 | 0.9263 | 48 | 0.8182 | 0.8780 | 0.8471 | 41 | 0.8571 | 0.8571 | 0.8571 | 14 | 0.8953 | 0.9181 | 0.9066 | 0.9722 |
0.0244 | 23.0 | 4462 | 0.2432 | 0.6279 | 0.7105 | 0.6667 | 38 | 0.8 | 0.9091 | 0.8511 | 22 | 1.0 | 0.86 | 0.9247 | 50 | 1.0 | 0.9773 | 0.9885 | 44 | 0.9796 | 1.0 | 0.9897 | 48 | 0.9388 | 0.9388 | 0.9388 | 49 | 0.9362 | 0.9167 | 0.9263 | 48 | 0.7955 | 0.8537 | 0.8235 | 41 | 0.9091 | 0.7143 | 0.8 | 14 | 0.8927 | 0.8927 | 0.8927 | 0.9676 |
0.0217 | 24.0 | 4656 | 0.2290 | 0.6923 | 0.7105 | 0.7013 | 38 | 0.8261 | 0.8636 | 0.8444 | 22 | 1.0 | 0.92 | 0.9583 | 50 | 1.0 | 0.9773 | 0.9885 | 44 | 0.9796 | 1.0 | 0.9897 | 48 | 0.9388 | 0.9388 | 0.9388 | 49 | 0.9375 | 0.9375 | 0.9375 | 48 | 0.8333 | 0.8537 | 0.8434 | 41 | 0.9167 | 0.7857 | 0.8462 | 14 | 0.9117 | 0.9040 | 0.9078 | 0.9728 |
0.0215 | 25.0 | 4850 | 0.2677 | 0.5897 | 0.6053 | 0.5974 | 38 | 0.7778 | 0.9545 | 0.8571 | 22 | 0.9592 | 0.94 | 0.9495 | 50 | 1.0 | 0.9773 | 0.9885 | 44 | 0.9796 | 1.0 | 0.9897 | 48 | 0.9388 | 0.9388 | 0.9388 | 49 | 0.9375 | 0.9375 | 0.9375 | 48 | 0.7955 | 0.8537 | 0.8235 | 41 | 0.7 | 1.0 | 0.8235 | 14 | 0.875 | 0.9096 | 0.8920 | 0.9676 |
0.0258 | 26.0 | 5044 | 0.2356 | 0.6341 | 0.6842 | 0.6582 | 38 | 0.7692 | 0.9091 | 0.8333 | 22 | 0.9796 | 0.96 | 0.9697 | 50 | 1.0 | 0.9773 | 0.9885 | 44 | 0.9796 | 1.0 | 0.9897 | 48 | 0.9388 | 0.9388 | 0.9388 | 49 | 0.8980 | 0.9167 | 0.9072 | 48 | 0.7727 | 0.8293 | 0.8000 | 41 | 0.6923 | 0.6429 | 0.6667 | 14 | 0.8760 | 0.8983 | 0.8870 | 0.9696 |
0.0191 | 27.0 | 5238 | 0.2115 | 0.625 | 0.6579 | 0.6410 | 38 | 0.8696 | 0.9091 | 0.8889 | 22 | 0.9792 | 0.94 | 0.9592 | 50 | 1.0 | 0.9773 | 0.9885 | 44 | 0.9796 | 1.0 | 0.9897 | 48 | 0.9388 | 0.9388 | 0.9388 | 49 | 0.9375 | 0.9375 | 0.9375 | 48 | 0.8571 | 0.8780 | 0.8675 | 41 | 0.8 | 0.8571 | 0.8276 | 14 | 0.9020 | 0.9096 | 0.9058 | 0.9751 |
0.0238 | 28.0 | 5432 | 0.2104 | 0.725 | 0.7632 | 0.7436 | 38 | 0.8636 | 0.8636 | 0.8636 | 22 | 0.9787 | 0.92 | 0.9485 | 50 | 1.0 | 0.9773 | 0.9885 | 44 | 0.9796 | 1.0 | 0.9897 | 48 | 0.9388 | 0.9388 | 0.9388 | 49 | 0.9388 | 0.9583 | 0.9485 | 48 | 0.7826 | 0.8780 | 0.8276 | 41 | 0.9231 | 0.8571 | 0.8889 | 14 | 0.9078 | 0.9181 | 0.9129 | 0.9763 |
0.0282 | 29.0 | 5626 | 0.2352 | 0.5238 | 0.5789 | 0.5500 | 38 | 0.8696 | 0.9091 | 0.8889 | 22 | 0.9778 | 0.88 | 0.9263 | 50 | 1.0 | 0.9773 | 0.9885 | 44 | 0.9796 | 1.0 | 0.9897 | 48 | 0.9388 | 0.9388 | 0.9388 | 49 | 0.9362 | 0.9167 | 0.9263 | 48 | 0.7609 | 0.8537 | 0.8046 | 41 | 0.75 | 0.8571 | 0.8000 | 14 | 0.8722 | 0.8870 | 0.8796 | 0.9705 |
0.0157 | 30.0 | 5820 | 0.2614 | 0.6190 | 0.6842 | 0.6500 | 38 | 0.84 | 0.9545 | 0.8936 | 22 | 1.0 | 0.92 | 0.9583 | 50 | 1.0 | 0.9773 | 0.9885 | 44 | 0.9796 | 1.0 | 0.9897 | 48 | 0.9388 | 0.9388 | 0.9388 | 49 | 0.9583 | 0.9583 | 0.9583 | 48 | 0.72 | 0.8780 | 0.7912 | 41 | 0.7059 | 0.8571 | 0.7742 | 14 | 0.8780 | 0.9153 | 0.8963 | 0.9688 |
Framework versions
- Transformers 4.22.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1