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license: mit
base_model: microsoft/layoutlm-base-uncased
tags:
- generated_from_trainer
datasets:
- funsd
model-index:
- name: layoutlm-funsd
results: []
---
<!-- 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: 0.6932
- Answer: {'precision': 0.6896186440677966, 'recall': 0.8046971569839307, 'f1': 0.7427267541357673, 'number': 809}
- Header: {'precision': 0.3305785123966942, 'recall': 0.33613445378151263, 'f1': 0.33333333333333337, 'number': 119}
- Question: {'precision': 0.766107678729038, 'recall': 0.8150234741784037, 'f1': 0.7898089171974523, 'number': 1065}
- Overall Precision: 0.7093
- Overall Recall: 0.7822
- Overall F1: 0.7440
- Overall Accuracy: 0.8018
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.8301 | 1.0 | 10 | 1.5866 | {'precision': 0.006765899864682003, 'recall': 0.006180469715698393, 'f1': 0.006459948320413437, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.2246153846153846, 'recall': 0.13708920187793427, 'f1': 0.17026239067055393, 'number': 1065} | 0.1087 | 0.0758 | 0.0893 | 0.3526 |
| 1.4768 | 2.0 | 20 | 1.2757 | {'precision': 0.280557834290402, 'recall': 0.4227441285537701, 'f1': 0.3372781065088757, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.3888491779842745, 'recall': 0.5107981220657277, 'f1': 0.44155844155844154, 'number': 1065} | 0.3380 | 0.4446 | 0.3840 | 0.6011 |
| 1.1406 | 3.0 | 30 | 0.9524 | {'precision': 0.46350710900473935, 'recall': 0.6044499381953028, 'f1': 0.5246781115879828, 'number': 809} | {'precision': 0.06382978723404255, 'recall': 0.025210084033613446, 'f1': 0.03614457831325301, 'number': 119} | {'precision': 0.53671875, 'recall': 0.6450704225352113, 'f1': 0.5859275053304905, 'number': 1065} | 0.4950 | 0.5916 | 0.5390 | 0.6937 |
| 0.8606 | 4.0 | 40 | 0.7865 | {'precision': 0.5620437956204379, 'recall': 0.761433868974042, 'f1': 0.6467191601049869, 'number': 809} | {'precision': 0.16666666666666666, 'recall': 0.10084033613445378, 'f1': 0.1256544502617801, 'number': 119} | {'precision': 0.6464285714285715, 'recall': 0.67981220657277, 'f1': 0.662700228832952, 'number': 1065} | 0.5909 | 0.6784 | 0.6316 | 0.7552 |
| 0.6873 | 5.0 | 50 | 0.7157 | {'precision': 0.6341719077568134, 'recall': 0.7478368355995055, 'f1': 0.6863301191151445, 'number': 809} | {'precision': 0.375, 'recall': 0.25210084033613445, 'f1': 0.3015075376884422, 'number': 119} | {'precision': 0.6704730831973899, 'recall': 0.7718309859154929, 'f1': 0.7175905718027062, 'number': 1065} | 0.6447 | 0.7311 | 0.6852 | 0.7767 |
| 0.5888 | 6.0 | 60 | 0.6909 | {'precision': 0.6243949661181026, 'recall': 0.7972805933250927, 'f1': 0.7003257328990228, 'number': 809} | {'precision': 0.35064935064935066, 'recall': 0.226890756302521, 'f1': 0.2755102040816326, 'number': 119} | {'precision': 0.7193923145665773, 'recall': 0.755868544600939, 'f1': 0.7371794871794871, 'number': 1065} | 0.6626 | 0.7411 | 0.6997 | 0.7806 |
| 0.5097 | 7.0 | 70 | 0.6576 | {'precision': 0.6656050955414012, 'recall': 0.7750309023485785, 'f1': 0.7161621930325527, 'number': 809} | {'precision': 0.32323232323232326, 'recall': 0.2689075630252101, 'f1': 0.29357798165137616, 'number': 119} | {'precision': 0.7382198952879581, 'recall': 0.7943661971830986, 'f1': 0.7652645861601085, 'number': 1065} | 0.6882 | 0.7551 | 0.7201 | 0.7963 |
| 0.4507 | 8.0 | 80 | 0.6668 | {'precision': 0.6615698267074414, 'recall': 0.8022249690976514, 'f1': 0.7251396648044692, 'number': 809} | {'precision': 0.28205128205128205, 'recall': 0.2773109243697479, 'f1': 0.2796610169491525, 'number': 119} | {'precision': 0.7389380530973452, 'recall': 0.784037558685446, 'f1': 0.7608200455580865, 'number': 1065} | 0.6809 | 0.7612 | 0.7188 | 0.7909 |
| 0.3998 | 9.0 | 90 | 0.6639 | {'precision': 0.6715481171548117, 'recall': 0.7935723114956736, 'f1': 0.7274787535410764, 'number': 809} | {'precision': 0.3130434782608696, 'recall': 0.3025210084033613, 'f1': 0.3076923076923077, 'number': 119} | {'precision': 0.7542448614834674, 'recall': 0.7924882629107981, 'f1': 0.7728937728937729, 'number': 1065} | 0.6950 | 0.7637 | 0.7277 | 0.7942 |
| 0.3899 | 10.0 | 100 | 0.6686 | {'precision': 0.6840981856990395, 'recall': 0.792336217552534, 'f1': 0.734249713631157, 'number': 809} | {'precision': 0.31092436974789917, 'recall': 0.31092436974789917, 'f1': 0.31092436974789917, 'number': 119} | {'precision': 0.752828546562228, 'recall': 0.812206572769953, 'f1': 0.7813911472448057, 'number': 1065} | 0.6998 | 0.7742 | 0.7351 | 0.7987 |
| 0.3345 | 11.0 | 110 | 0.6688 | {'precision': 0.6878980891719745, 'recall': 0.8009888751545118, 'f1': 0.7401484865790977, 'number': 809} | {'precision': 0.31451612903225806, 'recall': 0.3277310924369748, 'f1': 0.32098765432098764, 'number': 119} | {'precision': 0.7567332754126846, 'recall': 0.8178403755868544, 'f1': 0.7861010830324908, 'number': 1065} | 0.7028 | 0.7817 | 0.7401 | 0.8019 |
| 0.3227 | 12.0 | 120 | 0.6747 | {'precision': 0.6944444444444444, 'recall': 0.8034610630407911, 'f1': 0.7449856733524356, 'number': 809} | {'precision': 0.35714285714285715, 'recall': 0.33613445378151263, 'f1': 0.34632034632034636, 'number': 119} | {'precision': 0.7703306523681859, 'recall': 0.8093896713615023, 'f1': 0.7893772893772893, 'number': 1065} | 0.7162 | 0.7787 | 0.7462 | 0.8047 |
| 0.3068 | 13.0 | 130 | 0.6875 | {'precision': 0.6957470010905126, 'recall': 0.788627935723115, 'f1': 0.7392815758980301, 'number': 809} | {'precision': 0.3253968253968254, 'recall': 0.3445378151260504, 'f1': 0.33469387755102037, 'number': 119} | {'precision': 0.7596899224806202, 'recall': 0.828169014084507, 'f1': 0.7924528301886793, 'number': 1065} | 0.7083 | 0.7832 | 0.7439 | 0.8024 |
| 0.2826 | 14.0 | 140 | 0.6897 | {'precision': 0.6963519313304721, 'recall': 0.8022249690976514, 'f1': 0.7455485353245261, 'number': 809} | {'precision': 0.3252032520325203, 'recall': 0.33613445378151263, 'f1': 0.3305785123966942, 'number': 119} | {'precision': 0.7651183172655566, 'recall': 0.819718309859155, 'f1': 0.7914777878513146, 'number': 1065} | 0.7113 | 0.7837 | 0.7458 | 0.8007 |
| 0.2785 | 15.0 | 150 | 0.6932 | {'precision': 0.6896186440677966, 'recall': 0.8046971569839307, 'f1': 0.7427267541357673, 'number': 809} | {'precision': 0.3305785123966942, 'recall': 0.33613445378151263, 'f1': 0.33333333333333337, 'number': 119} | {'precision': 0.766107678729038, 'recall': 0.8150234741784037, 'f1': 0.7898089171974523, 'number': 1065} | 0.7093 | 0.7822 | 0.7440 | 0.8018 |
### Framework versions
- Transformers 4.44.0
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
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