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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: 0.7034
- Answer: {'precision': 0.6974697469746974, 'recall': 0.7836835599505563, 'f1': 0.7380675203725262, 'number': 809}
- Header: {'precision': 0.3106060606060606, 'recall': 0.3445378151260504, 'f1': 0.32669322709163345, 'number': 119}
- Question: {'precision': 0.7759226713532513, 'recall': 0.8291079812206573, 'f1': 0.8016341352700863, 'number': 1065}
- Overall Precision: 0.7150
- Overall Recall: 0.7817
- Overall F1: 0.7469
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:----------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.817         | 1.0   | 10   | 1.6092          | {'precision': 0.002197802197802198, 'recall': 0.0012360939431396785, 'f1': 0.0015822784810126582, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                | {'precision': 0.22, 'recall': 0.07230046948356808, 'f1': 0.10883392226148411, 'number': 1065}             | 0.0968            | 0.0391         | 0.0557     | 0.3198           |
| 1.4741        | 2.0   | 20   | 1.2390          | {'precision': 0.22916666666666666, 'recall': 0.24474660074165636, 'f1': 0.23670053795576806, 'number': 809}      | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                | {'precision': 0.4344262295081967, 'recall': 0.5474178403755868, 'f1': 0.4844204403822185, 'number': 1065} | 0.3540            | 0.3919         | 0.3720     | 0.6109           |
| 1.1045        | 3.0   | 30   | 0.9397          | {'precision': 0.493006993006993, 'recall': 0.522867737948084, 'f1': 0.5074985002999399, 'number': 809}           | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                | {'precision': 0.5923515052888527, 'recall': 0.6835680751173709, 'f1': 0.6346992153443767, 'number': 1065} | 0.5473            | 0.5775         | 0.5620     | 0.7110           |
| 0.8369        | 4.0   | 40   | 0.7900          | {'precision': 0.6087408949011447, 'recall': 0.723114956736712, 'f1': 0.6610169491525424, 'number': 809}          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                | {'precision': 0.6610025488530161, 'recall': 0.7305164319248826, 'f1': 0.6940231935771632, 'number': 1065} | 0.6229            | 0.6839         | 0.6520     | 0.7567           |
| 0.6855        | 5.0   | 50   | 0.7185          | {'precision': 0.6293706293706294, 'recall': 0.7787391841779975, 'f1': 0.696132596685083, 'number': 809}          | {'precision': 0.1, 'recall': 0.06722689075630252, 'f1': 0.08040201005025126, 'number': 119}                | {'precision': 0.6999125109361329, 'recall': 0.7511737089201878, 'f1': 0.7246376811594203, 'number': 1065} | 0.6466            | 0.7215         | 0.6820     | 0.7805           |
| 0.5663        | 6.0   | 60   | 0.6795          | {'precision': 0.6485655737704918, 'recall': 0.7824474660074165, 'f1': 0.7092436974789915, 'number': 809}         | {'precision': 0.1728395061728395, 'recall': 0.11764705882352941, 'f1': 0.13999999999999999, 'number': 119} | {'precision': 0.6875502008032128, 'recall': 0.8037558685446009, 'f1': 0.7411255411255411, 'number': 1065} | 0.6529            | 0.7541         | 0.6999     | 0.7940           |
| 0.4995        | 7.0   | 70   | 0.6814          | {'precision': 0.6630552546045504, 'recall': 0.7564894932014833, 'f1': 0.7066974595842955, 'number': 809}         | {'precision': 0.21929824561403508, 'recall': 0.21008403361344538, 'f1': 0.2145922746781116, 'number': 119} | {'precision': 0.721465076660988, 'recall': 0.7953051643192488, 'f1': 0.7565877623939258, 'number': 1065}  | 0.6712            | 0.7446         | 0.7060     | 0.7994           |
| 0.454         | 8.0   | 80   | 0.6688          | {'precision': 0.6716738197424893, 'recall': 0.7737948084054388, 'f1': 0.7191269385410684, 'number': 809}         | {'precision': 0.24324324324324326, 'recall': 0.226890756302521, 'f1': 0.23478260869565218, 'number': 119}  | {'precision': 0.7363481228668942, 'recall': 0.8103286384976526, 'f1': 0.7715690657130085, 'number': 1065} | 0.6844            | 0.7607         | 0.7205     | 0.8080           |
| 0.4132        | 9.0   | 90   | 0.6665          | {'precision': 0.6782231852654388, 'recall': 0.7737948084054388, 'f1': 0.7228637413394918, 'number': 809}         | {'precision': 0.29508196721311475, 'recall': 0.3025210084033613, 'f1': 0.2987551867219917, 'number': 119}  | {'precision': 0.739460370994941, 'recall': 0.8234741784037559, 'f1': 0.7792092403376277, 'number': 1065}  | 0.6898            | 0.7722         | 0.7287     | 0.8095           |
| 0.3671        | 10.0  | 100  | 0.6719          | {'precision': 0.6879049676025918, 'recall': 0.7873918417799752, 'f1': 0.7342939481268012, 'number': 809}         | {'precision': 0.319672131147541, 'recall': 0.3277310924369748, 'f1': 0.32365145228215775, 'number': 119}   | {'precision': 0.7710526315789473, 'recall': 0.8253521126760563, 'f1': 0.7972789115646259, 'number': 1065} | 0.7107            | 0.7802         | 0.7438     | 0.8165           |
| 0.334         | 11.0  | 110  | 0.6857          | {'precision': 0.6862955032119914, 'recall': 0.792336217552534, 'f1': 0.7355134825014343, 'number': 809}          | {'precision': 0.3584905660377358, 'recall': 0.31932773109243695, 'f1': 0.3377777777777778, 'number': 119}  | {'precision': 0.7785778577857786, 'recall': 0.812206572769953, 'f1': 0.7950367647058822, 'number': 1065}  | 0.7178            | 0.7747         | 0.7452     | 0.8160           |
| 0.3234        | 12.0  | 120  | 0.6966          | {'precision': 0.6926454445664105, 'recall': 0.7799752781211372, 'f1': 0.7337209302325581, 'number': 809}         | {'precision': 0.3125, 'recall': 0.33613445378151263, 'f1': 0.3238866396761134, 'number': 119}              | {'precision': 0.7707786526684165, 'recall': 0.8272300469483568, 'f1': 0.7980072463768116, 'number': 1065} | 0.7113            | 0.7787         | 0.7435     | 0.8154           |
| 0.3019        | 13.0  | 130  | 0.6940          | {'precision': 0.7010869565217391, 'recall': 0.7972805933250927, 'f1': 0.746096009253904, 'number': 809}          | {'precision': 0.3252032520325203, 'recall': 0.33613445378151263, 'f1': 0.3305785123966942, 'number': 119}  | {'precision': 0.7724444444444445, 'recall': 0.815962441314554, 'f1': 0.7936073059360731, 'number': 1065}  | 0.7168            | 0.7797         | 0.7469     | 0.8166           |
| 0.2888        | 14.0  | 140  | 0.7011          | {'precision': 0.6946564885496184, 'recall': 0.7873918417799752, 'f1': 0.7381228273464657, 'number': 809}         | {'precision': 0.29927007299270075, 'recall': 0.3445378151260504, 'f1': 0.3203125, 'number': 119}           | {'precision': 0.7726872246696035, 'recall': 0.8234741784037559, 'f1': 0.7972727272727272, 'number': 1065} | 0.7104            | 0.7802         | 0.7437     | 0.8170           |
| 0.2838        | 15.0  | 150  | 0.7034          | {'precision': 0.6974697469746974, 'recall': 0.7836835599505563, 'f1': 0.7380675203725262, 'number': 809}         | {'precision': 0.3106060606060606, 'recall': 0.3445378151260504, 'f1': 0.32669322709163345, 'number': 119}  | {'precision': 0.7759226713532513, 'recall': 0.8291079812206573, 'f1': 0.8016341352700863, 'number': 1065} | 0.7150            | 0.7817         | 0.7469     | 0.8170           |


### Framework versions

- Transformers 4.27.4
- Pytorch 1.13.1
- Datasets 2.11.0
- 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: 0.7034
* Answer: {'precision': 0.6974697469746974, 'recall': 0.7836835599505563, 'f1': 0.7380675203725262, 'number': 809}
* Header: {'precision': 0.3106060606060606, 'recall': 0.3445378151260504, 'f1': 0.32669322709163345, 'number': 119}
* Question: {'precision': 0.7759226713532513, 'recall': 0.8291079812206573, 'f1': 0.8016341352700863, 'number': 1065}
* Overall Precision: 0.7150
* Overall Recall: 0.7817
* Overall F1: 0.7469
* 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 4.27.4
* Pytorch 1.13.1
* Datasets 2.11.0
* 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 ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.1
* Datasets 2.11.0
* 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 ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.1
* Datasets 2.11.0
* 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### Training results### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.1
* Datasets 2.11.0
* Tokenizers 0.13.3