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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: 0.7189
- Answer: {'precision': 0.6983783783783784, 'recall': 0.7985166872682324, 'f1': 0.7450980392156863, 'number': 809}
- Header: {'precision': 0.28368794326241137, 'recall': 0.33613445378151263, 'f1': 0.3076923076923077, 'number': 119}
- Question: {'precision': 0.7754199823165341, 'recall': 0.8234741784037559, 'f1': 0.7987249544626595, 'number': 1065}
- Overall Precision: 0.7114
- Overall Recall: 0.7842
- Overall F1: 0.7461
- Overall Accuracy: 0.8074

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.561         | 1.0   | 10   | 1.3641          | {'precision': 0.05998125585754452, 'recall': 0.07911001236093942, 'f1': 0.06823027718550106, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.39123867069486407, 'recall': 0.4863849765258216, 'f1': 0.43365424863959817, 'number': 1065} | 0.2434            | 0.2920         | 0.2655     | 0.4879           |
| 1.1891        | 2.0   | 20   | 0.9802          | {'precision': 0.43872778297474274, 'recall': 0.5797280593325093, 'f1': 0.4994675186368477, 'number': 809}   | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.5793269230769231, 'recall': 0.6788732394366197, 'f1': 0.6251621271076524, 'number': 1065}   | 0.5062            | 0.5981         | 0.5483     | 0.6922           |
| 0.8513        | 3.0   | 30   | 0.8015          | {'precision': 0.5782520325203252, 'recall': 0.7033374536464772, 'f1': 0.6346904629113218, 'number': 809}    | {'precision': 0.10204081632653061, 'recall': 0.04201680672268908, 'f1': 0.05952380952380952, 'number': 119} | {'precision': 0.6831421006178288, 'recall': 0.7267605633802817, 'f1': 0.7042766151046406, 'number': 1065}   | 0.6223            | 0.6764         | 0.6482     | 0.7473           |
| 0.702         | 4.0   | 40   | 0.7279          | {'precision': 0.6275303643724697, 'recall': 0.7663782447466008, 'f1': 0.6900389538119087, 'number': 809}    | {'precision': 0.1411764705882353, 'recall': 0.10084033613445378, 'f1': 0.11764705882352941, 'number': 119}  | {'precision': 0.6978354978354978, 'recall': 0.7568075117370892, 'f1': 0.726126126126126, 'number': 1065}    | 0.6454            | 0.7215         | 0.6814     | 0.7727           |
| 0.6059        | 5.0   | 50   | 0.7065          | {'precision': 0.6370530877573131, 'recall': 0.7268232385661311, 'f1': 0.6789838337182448, 'number': 809}    | {'precision': 0.19607843137254902, 'recall': 0.16806722689075632, 'f1': 0.18099547511312217, 'number': 119} | {'precision': 0.7024106400665004, 'recall': 0.7934272300469484, 'f1': 0.7451499118165786, 'number': 1065}   | 0.6522            | 0.7291         | 0.6885     | 0.7809           |
| 0.5133        | 6.0   | 60   | 0.6761          | {'precision': 0.6592592592592592, 'recall': 0.7700865265760197, 'f1': 0.7103762827822121, 'number': 809}    | {'precision': 0.19791666666666666, 'recall': 0.15966386554621848, 'f1': 0.17674418604651165, 'number': 119} | {'precision': 0.7100638977635783, 'recall': 0.8347417840375587, 'f1': 0.7673716012084592, 'number': 1065}   | 0.6677            | 0.7682         | 0.7144     | 0.7927           |
| 0.4539        | 7.0   | 70   | 0.6811          | {'precision': 0.6793893129770993, 'recall': 0.7700865265760197, 'f1': 0.7219003476245655, 'number': 809}    | {'precision': 0.23387096774193547, 'recall': 0.24369747899159663, 'f1': 0.23868312757201646, 'number': 119} | {'precision': 0.7506361323155216, 'recall': 0.8309859154929577, 'f1': 0.7887700534759358, 'number': 1065}   | 0.6923            | 0.7712         | 0.7296     | 0.7970           |
| 0.4175        | 8.0   | 80   | 0.6604          | {'precision': 0.6727664155005382, 'recall': 0.7725587144622992, 'f1': 0.7192174913693901, 'number': 809}    | {'precision': 0.26785714285714285, 'recall': 0.25210084033613445, 'f1': 0.2597402597402597, 'number': 119}  | {'precision': 0.7596899224806202, 'recall': 0.828169014084507, 'f1': 0.7924528301886793, 'number': 1065}    | 0.6980            | 0.7712         | 0.7328     | 0.8022           |
| 0.3711        | 9.0   | 90   | 0.6827          | {'precision': 0.7034559643255296, 'recall': 0.7799752781211372, 'f1': 0.7397420867526378, 'number': 809}    | {'precision': 0.2482758620689655, 'recall': 0.3025210084033613, 'f1': 0.2727272727272727, 'number': 119}    | {'precision': 0.7497872340425532, 'recall': 0.8272300469483568, 'f1': 0.7866071428571428, 'number': 1065}   | 0.6982            | 0.7767         | 0.7354     | 0.8049           |
| 0.3346        | 10.0  | 100  | 0.6881          | {'precision': 0.688367129135539, 'recall': 0.7972805933250927, 'f1': 0.738831615120275, 'number': 809}      | {'precision': 0.2845528455284553, 'recall': 0.29411764705882354, 'f1': 0.2892561983471075, 'number': 119}   | {'precision': 0.7693661971830986, 'recall': 0.8206572769953052, 'f1': 0.79418446160836, 'number': 1065}     | 0.7077            | 0.7797         | 0.7419     | 0.8076           |
| 0.3003        | 11.0  | 110  | 0.7039          | {'precision': 0.6928104575163399, 'recall': 0.7861557478368356, 'f1': 0.7365373480023161, 'number': 809}    | {'precision': 0.3008130081300813, 'recall': 0.31092436974789917, 'f1': 0.3057851239669422, 'number': 119}   | {'precision': 0.7776801405975395, 'recall': 0.8309859154929577, 'f1': 0.8034498411257376, 'number': 1065}   | 0.7150            | 0.7817         | 0.7469     | 0.8095           |
| 0.2878        | 12.0  | 120  | 0.7100          | {'precision': 0.6923913043478261, 'recall': 0.7873918417799752, 'f1': 0.736842105263158, 'number': 809}     | {'precision': 0.2826086956521739, 'recall': 0.3277310924369748, 'f1': 0.3035019455252918, 'number': 119}    | {'precision': 0.780053428317008, 'recall': 0.8225352112676056, 'f1': 0.8007312614259597, 'number': 1065}    | 0.7116            | 0.7787         | 0.7437     | 0.8066           |
| 0.2724        | 13.0  | 130  | 0.7137          | {'precision': 0.6792249730893434, 'recall': 0.7799752781211372, 'f1': 0.7261219792865363, 'number': 809}    | {'precision': 0.2846715328467153, 'recall': 0.3277310924369748, 'f1': 0.3046875, 'number': 119}             | {'precision': 0.7838565022421524, 'recall': 0.8206572769953052, 'f1': 0.8018348623853212, 'number': 1065}   | 0.7079            | 0.7747         | 0.7398     | 0.8028           |
| 0.2582        | 14.0  | 140  | 0.7174          | {'precision': 0.6964477933261571, 'recall': 0.799752781211372, 'f1': 0.7445339470655927, 'number': 809}     | {'precision': 0.28368794326241137, 'recall': 0.33613445378151263, 'f1': 0.3076923076923077, 'number': 119}  | {'precision': 0.7772848269742679, 'recall': 0.8225352112676056, 'f1': 0.7992700729927008, 'number': 1065}   | 0.7114            | 0.7842         | 0.7461     | 0.8073           |
| 0.2569        | 15.0  | 150  | 0.7189          | {'precision': 0.6983783783783784, 'recall': 0.7985166872682324, 'f1': 0.7450980392156863, 'number': 809}    | {'precision': 0.28368794326241137, 'recall': 0.33613445378151263, 'f1': 0.3076923076923077, 'number': 119}  | {'precision': 0.7754199823165341, 'recall': 0.8234741784037559, 'f1': 0.7987249544626595, 'number': 1065}   | 0.7114            | 0.7842         | 0.7461     | 0.8074           |


### Framework versions

- Transformers 4.29.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"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: 0.7189
* Answer: {'precision': 0.6983783783783784, 'recall': 0.7985166872682324, 'f1': 0.7450980392156863, 'number': 809}
* Header: {'precision': 0.28368794326241137, 'recall': 0.33613445378151263, 'f1': 0.3076923076923077, 'number': 119}
* Question: {'precision': 0.7754199823165341, 'recall': 0.8234741784037559, 'f1': 0.7987249544626595, 'number': 1065}
* Overall Precision: 0.7114
* Overall Recall: 0.7842
* Overall F1: 0.7461
* Overall Accuracy: 0.8074


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 4.29.0
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### 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 4.29.0
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
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 4.29.0
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3