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---
tags:
- generated_from_trainer
model-index:
- name: layoutlm-doclaynet-test
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. -->
# layoutlm-doclaynet-test
This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3029
- Footer: {'precision': 0.7619047619047619, 'recall': 0.7960199004975125, 'f1': 0.7785888077858881, 'number': 201}
- Header: {'precision': 0.7631578947368421, 'recall': 0.6987951807228916, 'f1': 0.7295597484276729, 'number': 83}
- Able: {'precision': 0.569377990430622, 'recall': 0.7531645569620253, 'f1': 0.6485013623978202, 'number': 158}
- Aption: {'precision': 0.2857142857142857, 'recall': 0.26865671641791045, 'f1': 0.2769230769230769, 'number': 67}
- Ext: {'precision': 0.6098901098901099, 'recall': 0.6809815950920245, 'f1': 0.6434782608695652, 'number': 326}
- Icture: {'precision': 0.18055555555555555, 'recall': 0.2, 'f1': 0.18978102189781024, 'number': 65}
- Itle: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3}
- Ootnote: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4}
- Overall Precision: 0.5930
- Overall Recall: 0.6505
- Overall F1: 0.6204
- Overall Accuracy: 0.9197
## 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: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Footer | Header | Able | Aption | Ext | Icture | Itle | Ootnote | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------:|:---------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.2414 | 1.0 | 426 | 0.1727 | {'precision': 0.6724137931034483, 'recall': 0.7761194029850746, 'f1': 0.720554272517321, 'number': 201} | {'precision': 0.7142857142857143, 'recall': 0.5421686746987951, 'f1': 0.6164383561643836, 'number': 83} | {'precision': 0.5069124423963134, 'recall': 0.6962025316455697, 'f1': 0.5866666666666668, 'number': 158} | {'precision': 0.22916666666666666, 'recall': 0.16417910447761194, 'f1': 0.19130434782608696, 'number': 67} | {'precision': 0.5323383084577115, 'recall': 0.656441717791411, 'f1': 0.587912087912088, 'number': 326} | {'precision': 0.24528301886792453, 'recall': 0.2, 'f1': 0.22033898305084745, 'number': 65} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | 0.5409 | 0.6053 | 0.5713 | 0.9584 |
| 0.1037 | 2.0 | 852 | 0.1726 | {'precision': 0.7045454545454546, 'recall': 0.7711442786069652, 'f1': 0.7363420427553445, 'number': 201} | {'precision': 0.8529411764705882, 'recall': 0.6987951807228916, 'f1': 0.7682119205298014, 'number': 83} | {'precision': 0.5658536585365853, 'recall': 0.7341772151898734, 'f1': 0.6391184573002755, 'number': 158} | {'precision': 0.25333333333333335, 'recall': 0.2835820895522388, 'f1': 0.2676056338028169, 'number': 67} | {'precision': 0.5640394088669951, 'recall': 0.7024539877300614, 'f1': 0.6256830601092896, 'number': 326} | {'precision': 0.16666666666666666, 'recall': 0.18461538461538463, 'f1': 0.17518248175182485, 'number': 65} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | 0.5631 | 0.6494 | 0.6032 | 0.9510 |
| 0.0647 | 3.0 | 1278 | 0.3029 | {'precision': 0.7619047619047619, 'recall': 0.7960199004975125, 'f1': 0.7785888077858881, 'number': 201} | {'precision': 0.7631578947368421, 'recall': 0.6987951807228916, 'f1': 0.7295597484276729, 'number': 83} | {'precision': 0.569377990430622, 'recall': 0.7531645569620253, 'f1': 0.6485013623978202, 'number': 158} | {'precision': 0.2857142857142857, 'recall': 0.26865671641791045, 'f1': 0.2769230769230769, 'number': 67} | {'precision': 0.6098901098901099, 'recall': 0.6809815950920245, 'f1': 0.6434782608695652, 'number': 326} | {'precision': 0.18055555555555555, 'recall': 0.2, 'f1': 0.18978102189781024, 'number': 65} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | 0.5930 | 0.6505 | 0.6204 | 0.9197 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.12.1+cu102
- Datasets 2.9.0
- Tokenizers 0.13.2
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