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# 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

{"tags": ["generated_from_trainer"], "model-index": [{"name": "layoutlm-doclaynet-test", "results": []}]}
transformers pytorch tensorboard layoutlm token-classification generated_from_trainer autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #layoutlm #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 

layoutlm-doclaynet-test
=======================


This model is a fine-tuned version of 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



### Framework versions


* Transformers 4.26.1
* Pytorch 1.12.1+cu102
* Datasets 2.9.0
* Tokenizers 0.13.2



### 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 ### Framework versions


* Transformers 4.26.1
* Pytorch 1.12.1+cu102
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #layoutlm #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
 ### 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 ### Framework versions


* Transformers 4.26.1
* Pytorch 1.12.1+cu102
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #layoutlm #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
### 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### Framework versions


* Transformers 4.26.1
* Pytorch 1.12.1+cu102
* Datasets 2.9.0
* Tokenizers 0.13.2