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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: 0.0166
- Item: {'precision': 0.944560669456067, 'recall': 0.9495268138801262, 'f1': 0.9470372312532774, 'number': 951}
- Aption: {'precision': 0.9277436946148603, 'recall': 0.9249065579340808, 'f1': 0.9263229538880381, 'number': 2943}
- Ootnote: {'precision': 0.8297872340425532, 'recall': 0.8068965517241379, 'f1': 0.8181818181818181, 'number': 145}
- Ormula: {'precision': 0.9745836985100789, 'recall': 0.9754385964912281, 'f1': 0.9750109601052169, 'number': 2280}
- Overall Precision: 0.9450
- Overall Recall: 0.9441
- Overall F1: 0.9446
- Overall Accuracy: 0.9980
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.0173 | 1.0 | 8507 | 0.0103 | {'precision': 0.9440175631174533, 'recall': 0.9043112513144059, 'f1': 0.9237379162191193, 'number': 951} | {'precision': 0.9030054644808743, 'recall': 0.8984029901461094, 'f1': 0.9006983478112757, 'number': 2943} | {'precision': 0.7337662337662337, 'recall': 0.7793103448275862, 'f1': 0.7558528428093645, 'number': 145} | {'precision': 0.9664195377235063, 'recall': 0.9719298245614035, 'f1': 0.9691668488956922, 'number': 2280} | 0.9279 | 0.9231 | 0.9255 | 0.9975 |
| 0.0072 | 2.0 | 17014 | 0.0121 | {'precision': 0.9393305439330544, 'recall': 0.9442691903259727, 'f1': 0.9417933927635028, 'number': 951} | {'precision': 0.9193493730938664, 'recall': 0.9218484539585458, 'f1': 0.9205972175093315, 'number': 2943} | {'precision': 0.7651006711409396, 'recall': 0.7862068965517242, 'f1': 0.7755102040816326, 'number': 145} | {'precision': 0.9686820356676816, 'recall': 0.9767543859649123, 'f1': 0.9727014632015725, 'number': 2280} | 0.9366 | 0.9419 | 0.9392 | 0.9977 |
| 0.0056 | 3.0 | 25521 | 0.0104 | {'precision': 0.9389067524115756, 'recall': 0.9211356466876972, 'f1': 0.9299363057324842, 'number': 951} | {'precision': 0.9154160982264665, 'recall': 0.9119945633707102, 'f1': 0.9137021276595745, 'number': 2943} | {'precision': 0.6842105263157895, 'recall': 0.8068965517241379, 'f1': 0.7405063291139241, 'number': 145} | {'precision': 0.973568281938326, 'recall': 0.9692982456140351, 'f1': 0.9714285714285714, 'number': 2280} | 0.9336 | 0.9316 | 0.9326 | 0.9978 |
| 0.004 | 4.0 | 34028 | 0.0120 | {'precision': 0.9771428571428571, 'recall': 0.8990536277602523, 'f1': 0.9364731653888281, 'number': 951} | {'precision': 0.9220199244245963, 'recall': 0.9119945633707102, 'f1': 0.9169798428425008, 'number': 2943} | {'precision': 0.8214285714285714, 'recall': 0.7931034482758621, 'f1': 0.8070175438596492, 'number': 145} | {'precision': 0.9733158355205599, 'recall': 0.9758771929824561, 'f1': 0.9745948313622427, 'number': 2280} | 0.9464 | 0.9304 | 0.9383 | 0.9981 |
| 0.0028 | 5.0 | 42535 | 0.0122 | {'precision': 0.9588431590656284, 'recall': 0.9064143007360673, 'f1': 0.9318918918918919, 'number': 951} | {'precision': 0.924378453038674, 'recall': 0.909616038056405, 'f1': 0.9169378318205172, 'number': 2943} | {'precision': 0.8984375, 'recall': 0.7931034482758621, 'f1': 0.8424908424908425, 'number': 145} | {'precision': 0.971453667105841, 'recall': 0.9701754385964912, 'f1': 0.9708141321044547, 'number': 2280} | 0.9461 | 0.9283 | 0.9371 | 0.9980 |
| 0.0022 | 6.0 | 51042 | 0.0161 | {'precision': 0.899009900990099, 'recall': 0.9547844374342797, 'f1': 0.9260581336053034, 'number': 951} | {'precision': 0.9120916133378242, 'recall': 0.9201495073054706, 'f1': 0.9161028416779432, 'number': 2943} | {'precision': 0.8538461538461538, 'recall': 0.7655172413793103, 'f1': 0.8072727272727271, 'number': 145} | {'precision': 0.9733275032794053, 'recall': 0.9763157894736842, 'f1': 0.9748193562513685, 'number': 2280} | 0.9307 | 0.9421 | 0.9364 | 0.9978 |
| 0.0015 | 7.0 | 59549 | 0.0187 | {'precision': 0.9438444924406048, 'recall': 0.9190325972660357, 'f1': 0.9312733084709643, 'number': 951} | {'precision': 0.9206730769230769, 'recall': 0.9109751953788651, 'f1': 0.9157984628522631, 'number': 2943} | {'precision': 0.875968992248062, 'recall': 0.7793103448275862, 'f1': 0.8248175182481752, 'number': 145} | {'precision': 0.9741681260945709, 'recall': 0.9758771929824561, 'f1': 0.9750219106047328, 'number': 2280} | 0.9427 | 0.9326 | 0.9376 | 0.9981 |
| 0.0012 | 8.0 | 68056 | 0.0145 | {'precision': 0.9401260504201681, 'recall': 0.9411146161934806, 'f1': 0.9406200735680504, 'number': 951} | {'precision': 0.9231032125768968, 'recall': 0.9177709819911655, 'f1': 0.9204293746805248, 'number': 2943} | {'precision': 0.8041958041958042, 'recall': 0.7931034482758621, 'f1': 0.7986111111111112, 'number': 145} | {'precision': 0.9753629564452265, 'recall': 0.9723684210526315, 'f1': 0.9738633867779486, 'number': 2280} | 0.9418 | 0.9381 | 0.9400 | 0.9981 |
| 0.0009 | 9.0 | 76563 | 0.0140 | {'precision': 0.9475890985324947, 'recall': 0.9505783385909569, 'f1': 0.9490813648293962, 'number': 951} | {'precision': 0.9295003422313484, 'recall': 0.9228678219503907, 'f1': 0.9261722080136402, 'number': 2943} | {'precision': 0.8740740740740741, 'recall': 0.8137931034482758, 'f1': 0.8428571428571429, 'number': 145} | {'precision': 0.9771025979744606, 'recall': 0.9732456140350877, 'f1': 0.975170292243463, 'number': 2280} | 0.9483 | 0.9427 | 0.9455 | 0.9980 |
| 0.0007 | 10.0 | 85070 | 0.0166 | {'precision': 0.944560669456067, 'recall': 0.9495268138801262, 'f1': 0.9470372312532774, 'number': 951} | {'precision': 0.9277436946148603, 'recall': 0.9249065579340808, 'f1': 0.9263229538880381, 'number': 2943} | {'precision': 0.8297872340425532, 'recall': 0.8068965517241379, 'f1': 0.8181818181818181, 'number': 145} | {'precision': 0.9745836985100789, 'recall': 0.9754385964912281, 'f1': 0.9750109601052169, 'number': 2280} | 0.9450 | 0.9441 | 0.9446 | 0.9980 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.12.1
- Datasets 2.9.0
- Tokenizers 0.13.2
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