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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 an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6857
- Answer: {'precision': 0.7176981541802389, 'recall': 0.8170580964153276, 'f1': 0.7641618497109827, 'number': 809}
- Header: {'precision': 0.28368794326241137, 'recall': 0.33613445378151263, 'f1': 0.3076923076923077, 'number': 119}
- Question: {'precision': 0.7773820124666073, 'recall': 0.819718309859155, 'f1': 0.7979890310786105, 'number': 1065}
- Overall Precision: 0.7204
- Overall Recall: 0.7898
- Overall F1: 0.7535
- Overall Accuracy: 0.8139

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.8064        | 1.0   | 10   | 1.6080          | {'precision': 0.020618556701030927, 'recall': 0.012360939431396786, 'f1': 0.01545595054095827, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                   | {'precision': 0.2702127659574468, 'recall': 0.11924882629107982, 'f1': 0.16547231270358306, 'number': 1065} | 0.1435            | 0.0687         | 0.0929     | 0.3378           |
| 1.4826        | 2.0   | 20   | 1.2520          | {'precision': 0.20166320166320167, 'recall': 0.23980222496909764, 'f1': 0.21908526256352345, 'number': 809}   | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                   | {'precision': 0.4309507286606523, 'recall': 0.5830985915492958, 'f1': 0.49561053471667993, 'number': 1065}  | 0.3392            | 0.4089         | 0.3708     | 0.5993           |
| 1.1438        | 3.0   | 30   | 0.9584          | {'precision': 0.463519313304721, 'recall': 0.5339925834363412, 'f1': 0.49626651349798967, 'number': 809}      | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                   | {'precision': 0.6199664429530202, 'recall': 0.6938967136150235, 'f1': 0.6548515728843598, 'number': 1065}   | 0.5492            | 0.5876         | 0.5678     | 0.6897           |
| 0.8546        | 4.0   | 40   | 0.7900          | {'precision': 0.5885714285714285, 'recall': 0.7639060568603214, 'f1': 0.6648735879505111, 'number': 809}      | {'precision': 0.06666666666666667, 'recall': 0.025210084033613446, 'f1': 0.036585365853658534, 'number': 119} | {'precision': 0.6505823627287853, 'recall': 0.7342723004694836, 'f1': 0.6898985443317159, 'number': 1065}   | 0.6108            | 0.7040         | 0.6541     | 0.7537           |
| 0.6765        | 5.0   | 50   | 0.7144          | {'precision': 0.6514047866805411, 'recall': 0.7737948084054388, 'f1': 0.7073446327683616, 'number': 809}      | {'precision': 0.09230769230769231, 'recall': 0.05042016806722689, 'f1': 0.06521739130434782, 'number': 119}   | {'precision': 0.7019810508182601, 'recall': 0.7652582159624414, 'f1': 0.7322551662174304, 'number': 1065}   | 0.6616            | 0.7260         | 0.6923     | 0.7773           |
| 0.5613        | 6.0   | 60   | 0.6796          | {'precision': 0.6635514018691588, 'recall': 0.7898640296662547, 'f1': 0.7212189616252822, 'number': 809}      | {'precision': 0.15306122448979592, 'recall': 0.12605042016806722, 'f1': 0.1382488479262673, 'number': 119}    | {'precision': 0.7274320771253286, 'recall': 0.7793427230046949, 'f1': 0.7524932003626473, 'number': 1065}   | 0.6739            | 0.7446         | 0.7075     | 0.7927           |
| 0.4872        | 7.0   | 70   | 0.6554          | {'precision': 0.6592517694641051, 'recall': 0.8059332509270705, 'f1': 0.7252502780867631, 'number': 809}      | {'precision': 0.22549019607843138, 'recall': 0.19327731092436976, 'f1': 0.20814479638009048, 'number': 119}   | {'precision': 0.7383177570093458, 'recall': 0.815962441314554, 'f1': 0.775200713648528, 'number': 1065}     | 0.6808            | 0.7747         | 0.7247     | 0.7997           |
| 0.4334        | 8.0   | 80   | 0.6526          | {'precision': 0.6941176470588235, 'recall': 0.8022249690976514, 'f1': 0.7442660550458714, 'number': 809}      | {'precision': 0.24545454545454545, 'recall': 0.226890756302521, 'f1': 0.23580786026200873, 'number': 119}     | {'precision': 0.7493627867459643, 'recall': 0.828169014084507, 'f1': 0.7867975022301517, 'number': 1065}    | 0.7012            | 0.7817         | 0.7393     | 0.8035           |
| 0.3941        | 9.0   | 90   | 0.6694          | {'precision': 0.7048997772828508, 'recall': 0.7824474660074165, 'f1': 0.741652021089631, 'number': 809}       | {'precision': 0.22099447513812154, 'recall': 0.33613445378151263, 'f1': 0.26666666666666666, 'number': 119}   | {'precision': 0.7218984179850125, 'recall': 0.8140845070422535, 'f1': 0.76522506619594, 'number': 1065}     | 0.6754            | 0.7727         | 0.7208     | 0.8007           |
| 0.3556        | 10.0  | 100  | 0.6607          | {'precision': 0.694006309148265, 'recall': 0.8158220024721878, 'f1': 0.75, 'number': 809}                     | {'precision': 0.25, 'recall': 0.2773109243697479, 'f1': 0.26294820717131473, 'number': 119}                   | {'precision': 0.7846153846153846, 'recall': 0.8140845070422535, 'f1': 0.7990783410138248, 'number': 1065}   | 0.7130            | 0.7827         | 0.7462     | 0.8068           |
| 0.3245        | 11.0  | 110  | 0.6728          | {'precision': 0.6990595611285266, 'recall': 0.826946847960445, 'f1': 0.7576443941109853, 'number': 809}       | {'precision': 0.2892561983471074, 'recall': 0.29411764705882354, 'f1': 0.2916666666666667, 'number': 119}     | {'precision': 0.7817703768624014, 'recall': 0.8375586854460094, 'f1': 0.8087035358114233, 'number': 1065}   | 0.7192            | 0.8008         | 0.7578     | 0.8089           |
| 0.3113        | 12.0  | 120  | 0.6799          | {'precision': 0.71875, 'recall': 0.796044499381953, 'f1': 0.755425219941349, 'number': 809}                   | {'precision': 0.25903614457831325, 'recall': 0.36134453781512604, 'f1': 0.3017543859649123, 'number': 119}    | {'precision': 0.775330396475771, 'recall': 0.8262910798122066, 'f1': 0.8, 'number': 1065}                   | 0.7132            | 0.7863         | 0.7480     | 0.8106           |
| 0.2921        | 13.0  | 130  | 0.6836          | {'precision': 0.7070063694267515, 'recall': 0.823238566131026, 'f1': 0.7607081667618503, 'number': 809}       | {'precision': 0.32432432432432434, 'recall': 0.3025210084033613, 'f1': 0.31304347826086953, 'number': 119}    | {'precision': 0.7976513098464318, 'recall': 0.8291079812206573, 'f1': 0.8130755064456722, 'number': 1065}   | 0.7338            | 0.7953         | 0.7633     | 0.8122           |
| 0.2841        | 14.0  | 140  | 0.6848          | {'precision': 0.7150537634408602, 'recall': 0.8220024721878862, 'f1': 0.7648073605520415, 'number': 809}      | {'precision': 0.26666666666666666, 'recall': 0.33613445378151263, 'f1': 0.2973977695167286, 'number': 119}    | {'precision': 0.7841726618705036, 'recall': 0.8187793427230047, 'f1': 0.8011024345429489, 'number': 1065}   | 0.7194            | 0.7913         | 0.7536     | 0.8127           |
| 0.2793        | 15.0  | 150  | 0.6857          | {'precision': 0.7176981541802389, 'recall': 0.8170580964153276, 'f1': 0.7641618497109827, 'number': 809}      | {'precision': 0.28368794326241137, 'recall': 0.33613445378151263, 'f1': 0.3076923076923077, 'number': 119}    | {'precision': 0.7773820124666073, 'recall': 0.819718309859155, 'f1': 0.7979890310786105, 'number': 1065}    | 0.7204            | 0.7898         | 0.7535     | 0.8139           |


### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 1.8.0+cu101
- Tokenizers 0.13.2

{"tags": ["generated_from_trainer"], "model-index": [{"name": "layoutlm-funsd", "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-funsd
==============


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: 0.6857
* Answer: {'precision': 0.7176981541802389, 'recall': 0.8170580964153276, 'f1': 0.7641618497109827, 'number': 809}
* Header: {'precision': 0.28368794326241137, 'recall': 0.33613445378151263, 'f1': 0.3076923076923077, 'number': 119}
* Question: {'precision': 0.7773820124666073, 'recall': 0.819718309859155, 'f1': 0.7979890310786105, 'number': 1065}
* Overall Precision: 0.7204
* Overall Recall: 0.7898
* Overall F1: 0.7535
* Overall Accuracy: 0.8139


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 4.27.0.dev0
* Pytorch 1.8.0+cu101
* 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 4.27.0.dev0
* Pytorch 1.8.0+cu101
* 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: 15
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.8.0+cu101
* 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: 15
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.8.0+cu101
* Tokenizers 0.13.2