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---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
model-index:
- name: layoutlmv3-real_triplet
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. -->
# layoutlmv3-real_triplet
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0279
- Item: {'precision': 0.9425511197663097, 'recall': 0.7378048780487805, 'f1': 0.827704147071398, 'number': 2624}
- Aption: {'precision': 0.8349913494809689, 'recall': 0.7924876847290641, 'f1': 0.81318449873631, 'number': 4872}
- Ootnote: {'precision': 0.7846153846153846, 'recall': 0.8360655737704918, 'f1': 0.8095238095238095, 'number': 122}
- Ormula: {'precision': 0.9865976241242765, 'recall': 0.9920367534456356, 'f1': 0.9893097128894318, 'number': 3265}
- Overall Precision: 0.9056
- Overall Recall: 0.8397
- Overall F1: 0.8714
- Overall Accuracy: 0.9961
## 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: 3e-05
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Item | Aption | Ootnote | Ormula | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:----------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.0166 | 1.0 | 8507 | 0.0252 | {'precision': 0.9114948731786292, 'recall': 0.6436737804878049, 'f1': 0.7545231181594819, 'number': 2624} | {'precision': 0.778075463273052, 'recall': 0.715311986863711, 'f1': 0.7453748262217944, 'number': 4872} | {'precision': 0.8598130841121495, 'recall': 0.7540983606557377, 'f1': 0.8034934497816593, 'number': 122} | {'precision': 0.976365868631062, 'recall': 0.9742725880551302, 'f1': 0.9753181051663345, 'number': 3265} | 0.8711 | 0.7762 | 0.8209 | 0.9948 |
| 0.0075 | 2.0 | 17014 | 0.0318 | {'precision': 0.9079837618403248, 'recall': 0.5114329268292683, 'f1': 0.6543149683081424, 'number': 2624} | {'precision': 0.7194696441032798, 'recall': 0.6348522167487685, 'f1': 0.6745175008177952, 'number': 4872} | {'precision': 0.9207920792079208, 'recall': 0.7622950819672131, 'f1': 0.8340807174887892, 'number': 122} | {'precision': 0.9831132944427388, 'recall': 0.9807044410413476, 'f1': 0.9819073903710519, 'number': 3265} | 0.8462 | 0.7103 | 0.7723 | 0.9938 |
| 0.0057 | 3.0 | 25521 | 0.0338 | {'precision': 0.9227359088030399, 'recall': 0.5552591463414634, 'f1': 0.6933142993100166, 'number': 2624} | {'precision': 0.7442236598890942, 'recall': 0.6611247947454844, 'f1': 0.7002173913043479, 'number': 4872} | {'precision': 0.8859649122807017, 'recall': 0.8278688524590164, 'f1': 0.8559322033898306, 'number': 122} | {'precision': 0.9791538933169834, 'recall': 0.9782542113323124, 'f1': 0.9787038455645779, 'number': 3265} | 0.8589 | 0.7326 | 0.7907 | 0.9942 |
| 0.004 | 4.0 | 34028 | 0.0615 | {'precision': 0.9321486268174475, 'recall': 0.43978658536585363, 'f1': 0.5976178146038322, 'number': 2624} | {'precision': 0.6900404088424055, 'recall': 0.5958538587848933, 'f1': 0.639497742042075, 'number': 4872} | {'precision': 0.8738738738738738, 'recall': 0.7950819672131147, 'f1': 0.832618025751073, 'number': 122} | {'precision': 0.9880660954712362, 'recall': 0.9889739663093415, 'f1': 0.9885198224399205, 'number': 3265} | 0.8367 | 0.6784 | 0.7493 | 0.9932 |
| 0.0027 | 5.0 | 42535 | 0.0227 | {'precision': 0.9356973995271868, 'recall': 0.7541920731707317, 'f1': 0.8351972990082295, 'number': 2624} | {'precision': 0.843103448275862, 'recall': 0.8029556650246306, 'f1': 0.8225399495374264, 'number': 4872} | {'precision': 0.8571428571428571, 'recall': 0.7868852459016393, 'f1': 0.8205128205128205, 'number': 122} | {'precision': 0.9850655288021944, 'recall': 0.9898928024502297, 'f1': 0.9874732661167125, 'number': 3265} | 0.9085 | 0.8471 | 0.8767 | 0.9963 |
| 0.0021 | 6.0 | 51042 | 0.0165 | {'precision': 0.9341987466427932, 'recall': 0.7953506097560976, 'f1': 0.859201317414574, 'number': 2624} | {'precision': 0.856687898089172, 'recall': 0.8282019704433498, 'f1': 0.8422041327489042, 'number': 4872} | {'precision': 0.9174311926605505, 'recall': 0.819672131147541, 'f1': 0.8658008658008659, 'number': 122} | {'precision': 0.9736523319200484, 'recall': 0.9846860643185299, 'f1': 0.9791381148165067, 'number': 3265} | 0.9113 | 0.8671 | 0.8887 | 0.9966 |
| 0.0015 | 7.0 | 59549 | 0.0271 | {'precision': 0.9294605809128631, 'recall': 0.6829268292682927, 'f1': 0.7873462214411249, 'number': 2624} | {'precision': 0.8111135515045025, 'recall': 0.7580049261083743, 'f1': 0.7836604774535808, 'number': 4872} | {'precision': 0.8389830508474576, 'recall': 0.8114754098360656, 'f1': 0.825, 'number': 122} | {'precision': 0.9880879657910813, 'recall': 0.9908116385911179, 'f1': 0.9894479278177092, 'number': 3265} | 0.8932 | 0.8103 | 0.8498 | 0.9956 |
| 0.0012 | 8.0 | 68056 | 0.0231 | {'precision': 0.9250706880301602, 'recall': 0.7480945121951219, 'f1': 0.8272229245680573, 'number': 2624} | {'precision': 0.8451156812339332, 'recall': 0.8097290640394089, 'f1': 0.8270440251572327, 'number': 4872} | {'precision': 0.8962264150943396, 'recall': 0.7786885245901639, 'f1': 0.8333333333333333, 'number': 122} | {'precision': 0.9889739663093415, 'recall': 0.9889739663093415, 'f1': 0.9889739663093415, 'number': 3265} | 0.9086 | 0.8483 | 0.8774 | 0.9962 |
| 0.0009 | 9.0 | 76563 | 0.0224 | {'precision': 0.9263715110683349, 'recall': 0.7336128048780488, 'f1': 0.8188005104210974, 'number': 2624} | {'precision': 0.835820895522388, 'recall': 0.7931034482758621, 'f1': 0.8139020537124803, 'number': 4872} | {'precision': 0.832, 'recall': 0.8524590163934426, 'f1': 0.8421052631578947, 'number': 122} | {'precision': 0.9829787234042553, 'recall': 0.9905053598774886, 'f1': 0.9867276887871854, 'number': 3265} | 0.9022 | 0.8386 | 0.8693 | 0.9960 |
| 0.0007 | 10.0 | 85070 | 0.0279 | {'precision': 0.9425511197663097, 'recall': 0.7378048780487805, 'f1': 0.827704147071398, 'number': 2624} | {'precision': 0.8349913494809689, 'recall': 0.7924876847290641, 'f1': 0.81318449873631, 'number': 4872} | {'precision': 0.7846153846153846, 'recall': 0.8360655737704918, 'f1': 0.8095238095238095, 'number': 122} | {'precision': 0.9865976241242765, 'recall': 0.9920367534456356, 'f1': 0.9893097128894318, 'number': 3265} | 0.9056 | 0.8397 | 0.8714 | 0.9961 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.12.1
- Datasets 2.9.0
- Tokenizers 0.13.2
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