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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: 0.6724
- Answer: {'precision': 0.7072368421052632, 'recall': 0.7972805933250927, 'f1': 0.7495642068564788, 'number': 809}
- Header: {'precision': 0.3333333333333333, 'recall': 0.3697478991596639, 'f1': 0.350597609561753, 'number': 119}
- Question: {'precision': 0.7901785714285714, 'recall': 0.8309859154929577, 'f1': 0.8100686498855836, 'number': 1065}
- Overall Precision: 0.7274
- Overall Recall: 0.7898
- Overall F1: 0.7573
- Overall Accuracy: 0.8170

## 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

### Training results

| Training Loss | Epoch | Step | Validation Loss | Answer                                                                                                       | Header                                                                                                     | Question                                                                                                     | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.7705        | 1.0   | 10   | 1.5739          | {'precision': 0.010057471264367816, 'recall': 0.00865265760197775, 'f1': 0.00930232558139535, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                | {'precision': 0.26591760299625467, 'recall': 0.13333333333333333, 'f1': 0.17761100687929957, 'number': 1065} | 0.1211            | 0.0748         | 0.0925     | 0.3598           |
| 1.4468        | 2.0   | 20   | 1.2327          | {'precision': 0.25151148730350664, 'recall': 0.25710754017305315, 'f1': 0.2542787286063569, 'number': 809}   | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                | {'precision': 0.4251316779533484, 'recall': 0.5305164319248826, 'f1': 0.4720133667502089, 'number': 1065}    | 0.3585            | 0.3879         | 0.3726     | 0.5921           |
| 1.1103        | 3.0   | 30   | 0.9608          | {'precision': 0.4880694143167028, 'recall': 0.5562422744128553, 'f1': 0.5199306759098786, 'number': 809}     | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                | {'precision': 0.5613598673300166, 'recall': 0.6356807511737089, 'f1': 0.5962131219726994, 'number': 1065}    | 0.5257            | 0.5655         | 0.5448     | 0.7058           |
| 0.8476        | 4.0   | 40   | 0.7875          | {'precision': 0.5821325648414986, 'recall': 0.7490729295426453, 'f1': 0.6551351351351351, 'number': 809}     | {'precision': 0.1702127659574468, 'recall': 0.06722689075630252, 'f1': 0.09638554216867469, 'number': 119} | {'precision': 0.6254071661237784, 'recall': 0.7211267605633803, 'f1': 0.6698648059310947, 'number': 1065}    | 0.5967            | 0.6934         | 0.6414     | 0.7538           |
| 0.6698        | 5.0   | 50   | 0.6948          | {'precision': 0.6421923474663909, 'recall': 0.7676143386897404, 'f1': 0.6993243243243243, 'number': 809}     | {'precision': 0.3424657534246575, 'recall': 0.21008403361344538, 'f1': 0.2604166666666667, 'number': 119}  | {'precision': 0.6873977086743044, 'recall': 0.7887323943661971, 'f1': 0.7345867949278532, 'number': 1065}    | 0.6569            | 0.7456         | 0.6985     | 0.7860           |
| 0.554         | 6.0   | 60   | 0.6717          | {'precision': 0.6506410256410257, 'recall': 0.7527812113720643, 'f1': 0.6979942693409743, 'number': 809}     | {'precision': 0.3448275862068966, 'recall': 0.25210084033613445, 'f1': 0.2912621359223301, 'number': 119}  | {'precision': 0.716821639898563, 'recall': 0.7962441314553991, 'f1': 0.7544483985765124, 'number': 1065}     | 0.6741            | 0.7461         | 0.7083     | 0.7920           |
| 0.4787        | 7.0   | 70   | 0.6462          | {'precision': 0.6666666666666666, 'recall': 0.7935723114956736, 'f1': 0.7246049661399547, 'number': 809}     | {'precision': 0.3017241379310345, 'recall': 0.29411764705882354, 'f1': 0.29787234042553185, 'number': 119} | {'precision': 0.7368421052631579, 'recall': 0.8018779342723005, 'f1': 0.7679856115107914, 'number': 1065}    | 0.6841            | 0.7682         | 0.7237     | 0.8038           |
| 0.4182        | 8.0   | 80   | 0.6516          | {'precision': 0.6790890269151139, 'recall': 0.8108776266996292, 'f1': 0.7391549295774646, 'number': 809}     | {'precision': 0.3063063063063063, 'recall': 0.2857142857142857, 'f1': 0.2956521739130435, 'number': 119}   | {'precision': 0.7450643776824034, 'recall': 0.8150234741784037, 'f1': 0.77847533632287, 'number': 1065}      | 0.6949            | 0.7817         | 0.7358     | 0.8025           |
| 0.3877        | 9.0   | 90   | 0.6652          | {'precision': 0.6976744186046512, 'recall': 0.7787391841779975, 'f1': 0.7359813084112149, 'number': 809}     | {'precision': 0.3194444444444444, 'recall': 0.3865546218487395, 'f1': 0.34980988593155893, 'number': 119}  | {'precision': 0.7573913043478261, 'recall': 0.8178403755868544, 'f1': 0.7864559819413092, 'number': 1065}    | 0.7041            | 0.7762         | 0.7384     | 0.8094           |
| 0.3483        | 10.0  | 100  | 0.6568          | {'precision': 0.6876332622601279, 'recall': 0.7972805933250927, 'f1': 0.7384087006296507, 'number': 809}     | {'precision': 0.3225806451612903, 'recall': 0.33613445378151263, 'f1': 0.3292181069958848, 'number': 119}  | {'precision': 0.7650602409638554, 'recall': 0.8347417840375587, 'f1': 0.7983834755276155, 'number': 1065}    | 0.7077            | 0.7898         | 0.7465     | 0.8151           |
| 0.3136        | 11.0  | 110  | 0.6698          | {'precision': 0.7006507592190889, 'recall': 0.7985166872682324, 'f1': 0.7463893703061815, 'number': 809}     | {'precision': 0.3247863247863248, 'recall': 0.31932773109243695, 'f1': 0.3220338983050848, 'number': 119}  | {'precision': 0.7803365810451727, 'recall': 0.8272300469483568, 'f1': 0.8030993618960802, 'number': 1065}    | 0.7219            | 0.7852         | 0.7522     | 0.8084           |
| 0.3044        | 12.0  | 120  | 0.6667          | {'precision': 0.7058177826564215, 'recall': 0.7948084054388134, 'f1': 0.7476744186046511, 'number': 809}     | {'precision': 0.34328358208955223, 'recall': 0.3865546218487395, 'f1': 0.36363636363636365, 'number': 119} | {'precision': 0.785204991087344, 'recall': 0.8272300469483568, 'f1': 0.8056698673982624, 'number': 1065}     | 0.7245            | 0.7878         | 0.7548     | 0.8138           |
| 0.2853        | 13.0  | 130  | 0.6699          | {'precision': 0.702819956616052, 'recall': 0.8009888751545118, 'f1': 0.7487001733102253, 'number': 809}      | {'precision': 0.3442622950819672, 'recall': 0.35294117647058826, 'f1': 0.3485477178423237, 'number': 119}  | {'precision': 0.7857769973661106, 'recall': 0.8403755868544601, 'f1': 0.8121597096188747, 'number': 1065}    | 0.7261            | 0.7953         | 0.7591     | 0.8136           |
| 0.278         | 14.0  | 140  | 0.6716          | {'precision': 0.7009750812567714, 'recall': 0.799752781211372, 'f1': 0.7471131639722863, 'number': 809}      | {'precision': 0.3233082706766917, 'recall': 0.36134453781512604, 'f1': 0.3412698412698413, 'number': 119}  | {'precision': 0.7873665480427047, 'recall': 0.8309859154929577, 'f1': 0.808588396528095, 'number': 1065}     | 0.7225            | 0.7903         | 0.7549     | 0.8154           |
| 0.2672        | 15.0  | 150  | 0.6724          | {'precision': 0.7072368421052632, 'recall': 0.7972805933250927, 'f1': 0.7495642068564788, 'number': 809}     | {'precision': 0.3333333333333333, 'recall': 0.3697478991596639, 'f1': 0.350597609561753, 'number': 119}    | {'precision': 0.7901785714285714, 'recall': 0.8309859154929577, 'f1': 0.8100686498855836, 'number': 1065}    | 0.7274            | 0.7898         | 0.7573     | 0.8170           |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.9.0
- Datasets 2.8.0
- 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: 0.6724
* Answer: {'precision': 0.7072368421052632, 'recall': 0.7972805933250927, 'f1': 0.7495642068564788, 'number': 809}
* Header: {'precision': 0.3333333333333333, 'recall': 0.3697478991596639, 'f1': 0.350597609561753, 'number': 119}
* Question: {'precision': 0.7901785714285714, 'recall': 0.8309859154929577, 'f1': 0.8100686498855836, 'number': 1065}
* Overall Precision: 0.7274
* Overall Recall: 0.7898
* Overall F1: 0.7573
* Overall Accuracy: 0.8170


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


### Training results



### Framework versions


* Transformers 4.25.1
* Pytorch 1.9.0
* Datasets 2.8.0
* 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 ### Training results ### Framework versions


* Transformers 4.25.1
* Pytorch 1.9.0
* Datasets 2.8.0
* 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 ### Training results ### Framework versions


* Transformers 4.25.1
* Pytorch 1.9.0
* Datasets 2.8.0
* 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### Training results### Framework versions


* Transformers 4.25.1
* Pytorch 1.9.0
* Datasets 2.8.0
* Tokenizers 0.13.2