
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# 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: 0.6735
- Answer: {'precision': 0.7215601300108342, 'recall': 0.823238566131026, 'f1': 0.76905311778291, 'number': 809}
- Header: {'precision': 0.3046875, 'recall': 0.3277310924369748, 'f1': 0.31578947368421056, 'number': 119}
- Question: {'precision': 0.7800175284837861, 'recall': 0.8356807511737089, 'f1': 0.8068902991840435, 'number': 1065}
- Overall Precision: 0.7276
- Overall Recall: 0.8003
- Overall F1: 0.7622
- Overall Accuracy: 0.8080

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.7753        | 1.0   | 10   | 1.5651          | {'precision': 0.01791713325867861, 'recall': 0.019777503090234856, 'f1': 0.018801410105757928, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.253315649867374, 'recall': 0.17934272300469484, 'f1': 0.21000549752611322, 'number': 1065} | 0.1257            | 0.1039         | 0.1137     | 0.3966           |
| 1.4505        | 2.0   | 20   | 1.2385          | {'precision': 0.2100456621004566, 'recall': 0.22744128553770088, 'f1': 0.21839762611275965, 'number': 809}    | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.46676197283774123, 'recall': 0.6131455399061033, 'f1': 0.5300324675324676, 'number': 1065} | 0.3657            | 0.4200         | 0.3909     | 0.6293           |
| 1.0869        | 3.0   | 30   | 0.8993          | {'precision': 0.5091496232508074, 'recall': 0.584672435105068, 'f1': 0.5443037974683544, 'number': 809}       | {'precision': 0.046511627906976744, 'recall': 0.01680672268907563, 'f1': 0.02469135802469136, 'number': 119} | {'precision': 0.5931254996003198, 'recall': 0.6967136150234742, 'f1': 0.6407599309153713, 'number': 1065}  | 0.5475            | 0.6106         | 0.5773     | 0.7210           |
| 0.8144        | 4.0   | 40   | 0.7685          | {'precision': 0.5755755755755756, 'recall': 0.7107540173053152, 'f1': 0.6360619469026548, 'number': 809}      | {'precision': 0.15625, 'recall': 0.08403361344537816, 'f1': 0.10928961748633881, 'number': 119}              | {'precision': 0.6641350210970464, 'recall': 0.7389671361502348, 'f1': 0.6995555555555556, 'number': 1065}  | 0.6103            | 0.6884         | 0.6470     | 0.7562           |
| 0.6642        | 5.0   | 50   | 0.6960          | {'precision': 0.6472424557752341, 'recall': 0.7688504326328801, 'f1': 0.7028248587570621, 'number': 809}      | {'precision': 0.19607843137254902, 'recall': 0.16806722689075632, 'f1': 0.18099547511312217, 'number': 119}  | {'precision': 0.6795201371036846, 'recall': 0.7446009389671362, 'f1': 0.7105734767025091, 'number': 1065}  | 0.6435            | 0.7200         | 0.6796     | 0.7773           |
| 0.5578        | 6.0   | 60   | 0.6555          | {'precision': 0.6557377049180327, 'recall': 0.7911001236093943, 'f1': 0.7170868347338936, 'number': 809}      | {'precision': 0.19327731092436976, 'recall': 0.19327731092436976, 'f1': 0.19327731092436978, 'number': 119}  | {'precision': 0.7009038619556286, 'recall': 0.8009389671361502, 'f1': 0.7475898334794041, 'number': 1065}  | 0.6557            | 0.7607         | 0.7043     | 0.7920           |
| 0.484         | 7.0   | 70   | 0.6448          | {'precision': 0.6560574948665298, 'recall': 0.7898640296662547, 'f1': 0.7167694896242288, 'number': 809}      | {'precision': 0.24509803921568626, 'recall': 0.21008403361344538, 'f1': 0.22624434389140272, 'number': 119}  | {'precision': 0.7357859531772575, 'recall': 0.8262910798122066, 'f1': 0.7784166298098186, 'number': 1065}  | 0.6796            | 0.7747         | 0.7240     | 0.8003           |
| 0.4248        | 8.0   | 80   | 0.6501          | {'precision': 0.6865828092243187, 'recall': 0.8096415327564895, 'f1': 0.7430516165626773, 'number': 809}      | {'precision': 0.23972602739726026, 'recall': 0.29411764705882354, 'f1': 0.2641509433962264, 'number': 119}   | {'precision': 0.7493403693931399, 'recall': 0.8, 'f1': 0.7738419618528609, 'number': 1065}                 | 0.6893            | 0.7737         | 0.7291     | 0.7993           |
| 0.3833        | 9.0   | 90   | 0.6427          | {'precision': 0.7062706270627063, 'recall': 0.7935723114956736, 'f1': 0.7473806752037252, 'number': 809}      | {'precision': 0.2777777777777778, 'recall': 0.29411764705882354, 'f1': 0.28571428571428575, 'number': 119}   | {'precision': 0.7600685518423308, 'recall': 0.8328638497652582, 'f1': 0.7948028673835125, 'number': 1065}  | 0.7103            | 0.7847         | 0.7456     | 0.8069           |
| 0.3435        | 10.0  | 100  | 0.6499          | {'precision': 0.7076271186440678, 'recall': 0.8257107540173053, 'f1': 0.7621220764403879, 'number': 809}      | {'precision': 0.3217391304347826, 'recall': 0.31092436974789917, 'f1': 0.3162393162393162, 'number': 119}    | {'precision': 0.7789566755083996, 'recall': 0.8272300469483568, 'f1': 0.802367941712204, 'number': 1065}   | 0.7242            | 0.7958         | 0.7583     | 0.8088           |
| 0.3157        | 11.0  | 110  | 0.6661          | {'precision': 0.7183406113537117, 'recall': 0.8133498145859085, 'f1': 0.7628985507246376, 'number': 809}      | {'precision': 0.32231404958677684, 'recall': 0.3277310924369748, 'f1': 0.32499999999999996, 'number': 119}   | {'precision': 0.7774846086191732, 'recall': 0.8300469483568075, 'f1': 0.8029064486830154, 'number': 1065}  | 0.7272            | 0.7933         | 0.7588     | 0.8052           |
| 0.2921        | 12.0  | 120  | 0.6645          | {'precision': 0.7142857142857143, 'recall': 0.8281829419035847, 'f1': 0.767029192902118, 'number': 809}       | {'precision': 0.30158730158730157, 'recall': 0.31932773109243695, 'f1': 0.310204081632653, 'number': 119}    | {'precision': 0.7777777777777778, 'recall': 0.8347417840375587, 'f1': 0.8052536231884059, 'number': 1065}  | 0.7236            | 0.8013         | 0.7605     | 0.8075           |
| 0.2805        | 13.0  | 130  | 0.6742          | {'precision': 0.7270742358078602, 'recall': 0.823238566131026, 'f1': 0.7721739130434783, 'number': 809}       | {'precision': 0.29850746268656714, 'recall': 0.33613445378151263, 'f1': 0.31620553359683795, 'number': 119}  | {'precision': 0.7802101576182137, 'recall': 0.8366197183098592, 'f1': 0.8074309016764839, 'number': 1065}  | 0.7286            | 0.8013         | 0.7632     | 0.8074           |
| 0.2676        | 14.0  | 140  | 0.6739          | {'precision': 0.720173535791757, 'recall': 0.8207663782447466, 'f1': 0.7671865973425764, 'number': 809}       | {'precision': 0.2932330827067669, 'recall': 0.3277310924369748, 'f1': 0.30952380952380953, 'number': 119}    | {'precision': 0.7730434782608696, 'recall': 0.8347417840375587, 'f1': 0.8027088036117382, 'number': 1065}  | 0.7220            | 0.7988         | 0.7585     | 0.8066           |
| 0.2731        | 15.0  | 150  | 0.6735          | {'precision': 0.7215601300108342, 'recall': 0.823238566131026, 'f1': 0.76905311778291, 'number': 809}         | {'precision': 0.3046875, 'recall': 0.3277310924369748, 'f1': 0.31578947368421056, 'number': 119}             | {'precision': 0.7800175284837861, 'recall': 0.8356807511737089, 'f1': 0.8068902991840435, 'number': 1065}  | 0.7276            | 0.8003         | 0.7622     | 0.8080           |


### Framework versions

- Transformers 4.33.0.dev0
- Pytorch 2.0.1+cpu
- Datasets 2.14.4
- Tokenizers 0.13.3

{"tags": ["generated_from_trainer"], "datasets": ["funsd"], "base_model": "microsoft/layoutlm-base-uncased", "model-index": [{"name": "layoutlm-funsd", "results": []}]}
transformers pytorch layoutlm token-classification generated_from_trainer dataset:funsd base_model:microsoft/layoutlm-base-uncased autotrain_compatible endpoints_compatible region:us