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---
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: 0.6866
- Answer: {'precision': 0.7130339539978094, 'recall': 0.8046971569839307, 'f1': 0.7560975609756098, 'number': 809}
- Header: {'precision': 0.3384615384615385, 'recall': 0.3697478991596639, 'f1': 0.35341365461847385, 'number': 119}
- Question: {'precision': 0.7763975155279503, 'recall': 0.8215962441314554, 'f1': 0.7983576642335766, 'number': 1065}
- Overall Precision: 0.7235
- Overall Recall: 0.7878
- Overall F1: 0.7543
- Overall Accuracy: 0.8126
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.7968 | 1.0 | 10 | 1.5972 | {'precision': 0.011235955056179775, 'recall': 0.011124845488257108, 'f1': 0.011180124223602483, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.1959544879898862, 'recall': 0.14553990610328638, 'f1': 0.1670258620689655, 'number': 1065} | 0.1030 | 0.0823 | 0.0915 | 0.3535 |
| 1.4694 | 2.0 | 20 | 1.2467 | {'precision': 0.2002053388090349, 'recall': 0.24103831891223734, 'f1': 0.21873247335950646, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.43186895011169024, 'recall': 0.5446009389671361, 'f1': 0.48172757475083056, 'number': 1065} | 0.3345 | 0.3889 | 0.3596 | 0.6093 |
| 1.0892 | 3.0 | 30 | 0.9301 | {'precision': 0.49691991786447637, 'recall': 0.5982694684796045, 'f1': 0.5429052159282108, 'number': 809} | {'precision': 0.08108108108108109, 'recall': 0.025210084033613446, 'f1': 0.038461538461538464, 'number': 119} | {'precision': 0.5869205298013245, 'recall': 0.6657276995305165, 'f1': 0.62384513858337, 'number': 1065} | 0.5390 | 0.6001 | 0.5679 | 0.7041 |
| 0.8148 | 4.0 | 40 | 0.7921 | {'precision': 0.5805243445692884, 'recall': 0.7663782447466008, 'f1': 0.660628662759723, 'number': 809} | {'precision': 0.2, 'recall': 0.12605042016806722, 'f1': 0.15463917525773196, 'number': 119} | {'precision': 0.6657534246575343, 'recall': 0.6845070422535211, 'f1': 0.6749999999999999, 'number': 1065} | 0.6095 | 0.6844 | 0.6448 | 0.7498 |
| 0.6789 | 5.0 | 50 | 0.7126 | {'precision': 0.6466942148760331, 'recall': 0.7737948084054388, 'f1': 0.7045582442318515, 'number': 809} | {'precision': 0.23809523809523808, 'recall': 0.21008403361344538, 'f1': 0.22321428571428573, 'number': 119} | {'precision': 0.6851535836177475, 'recall': 0.7539906103286385, 'f1': 0.7179257934734019, 'number': 1065} | 0.6477 | 0.7296 | 0.6862 | 0.7822 |
| 0.5701 | 6.0 | 60 | 0.6734 | {'precision': 0.6524390243902439, 'recall': 0.7935723114956736, 'f1': 0.7161182375906302, 'number': 809} | {'precision': 0.25, 'recall': 0.18487394957983194, 'f1': 0.21256038647342995, 'number': 119} | {'precision': 0.6886564762670957, 'recall': 0.8037558685446009, 'f1': 0.7417677642980937, 'number': 1065} | 0.6566 | 0.7627 | 0.7057 | 0.7949 |
| 0.497 | 7.0 | 70 | 0.6688 | {'precision': 0.6719745222929936, 'recall': 0.7824474660074165, 'f1': 0.7230154197601371, 'number': 809} | {'precision': 0.2857142857142857, 'recall': 0.2689075630252101, 'f1': 0.277056277056277, 'number': 119} | {'precision': 0.7403267411865864, 'recall': 0.8084507042253521, 'f1': 0.7728904847396768, 'number': 1065} | 0.6883 | 0.7657 | 0.7249 | 0.7976 |
| 0.4549 | 8.0 | 80 | 0.6561 | {'precision': 0.6881028938906752, 'recall': 0.7935723114956736, 'f1': 0.7370838117106774, 'number': 809} | {'precision': 0.25, 'recall': 0.25210084033613445, 'f1': 0.2510460251046025, 'number': 119} | {'precision': 0.7432784041630529, 'recall': 0.8046948356807512, 'f1': 0.7727682596934174, 'number': 1065} | 0.6931 | 0.7672 | 0.7283 | 0.8045 |
| 0.4095 | 9.0 | 90 | 0.6514 | {'precision': 0.694206008583691, 'recall': 0.799752781211372, 'f1': 0.7432510051694429, 'number': 809} | {'precision': 0.29411764705882354, 'recall': 0.29411764705882354, 'f1': 0.29411764705882354, 'number': 119} | {'precision': 0.7452830188679245, 'recall': 0.815962441314554, 'f1': 0.7790228597041686, 'number': 1065} | 0.6996 | 0.7782 | 0.7368 | 0.8027 |
| 0.3629 | 10.0 | 100 | 0.6616 | {'precision': 0.7035010940919038, 'recall': 0.7948084054388134, 'f1': 0.7463726059199072, 'number': 809} | {'precision': 0.29927007299270075, 'recall': 0.3445378151260504, 'f1': 0.3203125, 'number': 119} | {'precision': 0.7564216120460585, 'recall': 0.8018779342723005, 'f1': 0.7784867821330903, 'number': 1065} | 0.7055 | 0.7717 | 0.7371 | 0.8075 |
| 0.3322 | 11.0 | 110 | 0.6668 | {'precision': 0.7112068965517241, 'recall': 0.8158220024721878, 'f1': 0.75993091537133, 'number': 809} | {'precision': 0.336283185840708, 'recall': 0.31932773109243695, 'f1': 0.32758620689655166, 'number': 119} | {'precision': 0.783273381294964, 'recall': 0.8178403755868544, 'f1': 0.8001837390904916, 'number': 1065} | 0.7288 | 0.7873 | 0.7569 | 0.8120 |
| 0.3188 | 12.0 | 120 | 0.6768 | {'precision': 0.7225305216426193, 'recall': 0.8046971569839307, 'f1': 0.7614035087719299, 'number': 809} | {'precision': 0.33076923076923076, 'recall': 0.36134453781512604, 'f1': 0.34538152610441764, 'number': 119} | {'precision': 0.7759078830823738, 'recall': 0.8225352112676056, 'f1': 0.7985414767547857, 'number': 1065} | 0.7269 | 0.7878 | 0.7561 | 0.8119 |
| 0.2936 | 13.0 | 130 | 0.6787 | {'precision': 0.7122692725298588, 'recall': 0.8108776266996292, 'f1': 0.7583815028901735, 'number': 809} | {'precision': 0.35384615384615387, 'recall': 0.3865546218487395, 'f1': 0.3694779116465864, 'number': 119} | {'precision': 0.7807486631016043, 'recall': 0.8225352112676056, 'f1': 0.8010973936899862, 'number': 1065} | 0.7262 | 0.7918 | 0.7576 | 0.8133 |
| 0.2894 | 14.0 | 140 | 0.6863 | {'precision': 0.7113289760348583, 'recall': 0.8071693448702101, 'f1': 0.7562246670526924, 'number': 809} | {'precision': 0.34108527131782945, 'recall': 0.3697478991596639, 'f1': 0.35483870967741943, 'number': 119} | {'precision': 0.7852650494159928, 'recall': 0.8206572769953052, 'f1': 0.8025711662075299, 'number': 1065} | 0.7273 | 0.7883 | 0.7566 | 0.8111 |
| 0.2813 | 15.0 | 150 | 0.6866 | {'precision': 0.7130339539978094, 'recall': 0.8046971569839307, 'f1': 0.7560975609756098, 'number': 809} | {'precision': 0.3384615384615385, 'recall': 0.3697478991596639, 'f1': 0.35341365461847385, 'number': 119} | {'precision': 0.7763975155279503, 'recall': 0.8215962441314554, 'f1': 0.7983576642335766, 'number': 1065} | 0.7235 | 0.7878 | 0.7543 | 0.8126 |
### Framework versions
- Transformers 4.27.1
- Pytorch 1.12.1
- Datasets 2.6.1
- Tokenizers 0.13.2
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