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# layoutlm-funsd

This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the funsd dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6845
- Answer: {'precision': 0.6932907348242812, 'recall': 0.8046971569839307, 'f1': 0.7448512585812357, 'number': 809}
- Header: {'precision': 0.3220338983050847, 'recall': 0.31932773109243695, 'f1': 0.32067510548523204, 'number': 119}
- Question: {'precision': 0.7827225130890052, 'recall': 0.8422535211267606, 'f1': 0.8113975576662144, 'number': 1065}
- Overall Precision: 0.7199
- Overall Recall: 0.7958
- Overall F1: 0.7560
- Overall Accuracy: 0.8087

## 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: 15
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Answer                                                                                                        | Header                                                                                                     | Question                                                                                                   | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.7948        | 1.0   | 10   | 1.5982          | {'precision': 0.019115890083632018, 'recall': 0.019777503090234856, 'f1': 0.01944106925880923, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                | {'precision': 0.1559202813599062, 'recall': 0.12488262910798122, 'f1': 0.1386861313868613, 'number': 1065} | 0.0882            | 0.0748         | 0.0809     | 0.3666           |
| 1.4548        | 2.0   | 20   | 1.2137          | {'precision': 0.18571428571428572, 'recall': 0.19283065512978986, 'f1': 0.18920557913887204, 'number': 809}   | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                | {'precision': 0.5027844073190135, 'recall': 0.5934272300469483, 'f1': 0.5443583118001722, 'number': 1065}  | 0.3758            | 0.3954         | 0.3853     | 0.6060           |
| 1.0759        | 3.0   | 30   | 0.9074          | {'precision': 0.45133689839572194, 'recall': 0.5216316440049443, 'f1': 0.48394495412844035, 'number': 809}    | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                | {'precision': 0.6371453138435081, 'recall': 0.6957746478873239, 'f1': 0.6651705565529622, 'number': 1065}  | 0.5491            | 0.5835         | 0.5658     | 0.7138           |
| 0.818         | 4.0   | 40   | 0.7636          | {'precision': 0.601010101010101, 'recall': 0.7354758961681088, 'f1': 0.6614785992217899, 'number': 809}       | {'precision': 0.22, 'recall': 0.09243697478991597, 'f1': 0.13017751479289943, 'number': 119}               | {'precision': 0.6860670194003528, 'recall': 0.7305164319248826, 'f1': 0.707594361073215, 'number': 1065}   | 0.6366            | 0.6944         | 0.6643     | 0.7580           |
| 0.6744        | 5.0   | 50   | 0.6948          | {'precision': 0.6172106824925816, 'recall': 0.7713226205191595, 'f1': 0.6857142857142857, 'number': 809}      | {'precision': 0.2608695652173913, 'recall': 0.15126050420168066, 'f1': 0.19148936170212766, 'number': 119} | {'precision': 0.7063758389261745, 'recall': 0.7906103286384977, 'f1': 0.7461231723526807, 'number': 1065}  | 0.6532            | 0.7446         | 0.6959     | 0.7803           |
| 0.5678        | 6.0   | 60   | 0.6772          | {'precision': 0.6684100418410042, 'recall': 0.7898640296662547, 'f1': 0.7240793201133144, 'number': 809}      | {'precision': 0.32857142857142857, 'recall': 0.19327731092436976, 'f1': 0.2433862433862434, 'number': 119} | {'precision': 0.7155309033280507, 'recall': 0.847887323943662, 'f1': 0.7761065749892565, 'number': 1065}   | 0.6840            | 0.7852         | 0.7311     | 0.7902           |
| 0.4886        | 7.0   | 70   | 0.6596          | {'precision': 0.6836518046709129, 'recall': 0.796044499381953, 'f1': 0.7355796687607081, 'number': 809}       | {'precision': 0.30952380952380953, 'recall': 0.2184873949579832, 'f1': 0.2561576354679803, 'number': 119}  | {'precision': 0.75, 'recall': 0.8422535211267606, 'f1': 0.793454223794781, 'number': 1065}                 | 0.7052            | 0.7863         | 0.7435     | 0.7931           |
| 0.4432        | 8.0   | 80   | 0.6525          | {'precision': 0.6792849631966351, 'recall': 0.7985166872682324, 'f1': 0.734090909090909, 'number': 809}       | {'precision': 0.2736842105263158, 'recall': 0.2184873949579832, 'f1': 0.2429906542056075, 'number': 119}   | {'precision': 0.7472984206151289, 'recall': 0.844131455399061, 'f1': 0.7927689594356261, 'number': 1065}   | 0.6985            | 0.7883         | 0.7407     | 0.7965           |
| 0.3961        | 9.0   | 90   | 0.6515          | {'precision': 0.6940540540540541, 'recall': 0.7935723114956736, 'f1': 0.740484429065744, 'number': 809}       | {'precision': 0.2803738317757009, 'recall': 0.25210084033613445, 'f1': 0.2654867256637167, 'number': 119}  | {'precision': 0.7613344739093242, 'recall': 0.8356807511737089, 'f1': 0.7967770814682185, 'number': 1065}  | 0.7097            | 0.7837         | 0.7449     | 0.8019           |
| 0.3531        | 10.0  | 100  | 0.6628          | {'precision': 0.697452229299363, 'recall': 0.8121137206427689, 'f1': 0.750428326670474, 'number': 809}        | {'precision': 0.2962962962962963, 'recall': 0.2689075630252101, 'f1': 0.28193832599118945, 'number': 119}  | {'precision': 0.7577276524644946, 'recall': 0.8516431924882629, 'f1': 0.801945181255526, 'number': 1065}   | 0.7103            | 0.8008         | 0.7528     | 0.8034           |
| 0.3201        | 11.0  | 110  | 0.6678          | {'precision': 0.6915005246589717, 'recall': 0.8145859085290482, 'f1': 0.7480136208853576, 'number': 809}      | {'precision': 0.2909090909090909, 'recall': 0.2689075630252101, 'f1': 0.2794759825327511, 'number': 119}   | {'precision': 0.7679794520547946, 'recall': 0.8422535211267606, 'f1': 0.8034034930586654, 'number': 1065}  | 0.7118            | 0.7968         | 0.7519     | 0.8071           |
| 0.3055        | 12.0  | 120  | 0.6760          | {'precision': 0.6869747899159664, 'recall': 0.8084054388133498, 'f1': 0.7427597955706984, 'number': 809}      | {'precision': 0.296, 'recall': 0.31092436974789917, 'f1': 0.30327868852459017, 'number': 119}              | {'precision': 0.7839506172839507, 'recall': 0.8347417840375587, 'f1': 0.8085493406093679, 'number': 1065}  | 0.7146            | 0.7928         | 0.7517     | 0.8047           |
| 0.29          | 13.0  | 130  | 0.6844          | {'precision': 0.7013963480128894, 'recall': 0.8071693448702101, 'f1': 0.7505747126436783, 'number': 809}      | {'precision': 0.28346456692913385, 'recall': 0.3025210084033613, 'f1': 0.2926829268292683, 'number': 119}  | {'precision': 0.7771084337349398, 'recall': 0.847887323943662, 'f1': 0.8109564436461607, 'number': 1065}   | 0.7171            | 0.7988         | 0.7558     | 0.8041           |
| 0.2797        | 14.0  | 140  | 0.6841          | {'precision': 0.6956055734190782, 'recall': 0.8022249690976514, 'f1': 0.7451205510907002, 'number': 809}      | {'precision': 0.3064516129032258, 'recall': 0.31932773109243695, 'f1': 0.31275720164609055, 'number': 119} | {'precision': 0.7750865051903114, 'recall': 0.8413145539906103, 'f1': 0.8068437640702386, 'number': 1065}  | 0.7153            | 0.7943         | 0.7527     | 0.8070           |
| 0.2733        | 15.0  | 150  | 0.6845          | {'precision': 0.6932907348242812, 'recall': 0.8046971569839307, 'f1': 0.7448512585812357, 'number': 809}      | {'precision': 0.3220338983050847, 'recall': 0.31932773109243695, 'f1': 0.32067510548523204, 'number': 119} | {'precision': 0.7827225130890052, 'recall': 0.8422535211267606, 'f1': 0.8113975576662144, 'number': 1065}  | 0.7199            | 0.7958         | 0.7560     | 0.8087           |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2

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

layoutlm-funsd
==============


This model is a fine-tuned version of microsoft/layoutlm-base-uncased on the funsd dataset.
It achieves the following results on the evaluation set:


* Loss: 0.6845
* Answer: {'precision': 0.6932907348242812, 'recall': 0.8046971569839307, 'f1': 0.7448512585812357, 'number': 809}
* Header: {'precision': 0.3220338983050847, 'recall': 0.31932773109243695, 'f1': 0.32067510548523204, 'number': 119}
* Question: {'precision': 0.7827225130890052, 'recall': 0.8422535211267606, 'f1': 0.8113975576662144, 'number': 1065}
* Overall Precision: 0.7199
* Overall Recall: 0.7958
* Overall F1: 0.7560
* Overall Accuracy: 0.8087


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: 15
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.0
* 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: 15
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #layoutlm #token-classification #generated_from_trainer #dataset-funsd #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: 15
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #layoutlm #token-classification #generated_from_trainer #dataset-funsd #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: 15
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.0
* Tokenizers 0.13.2