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---
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: 1.3057
- Answer: {'precision': 0.09480519480519481, 'recall': 0.09023485784919653, 'f1': 0.09246358454718177, 'number': 809}
- Header: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}
- Question: {'precision': 0.4032534246575342, 'recall': 0.4422535211267606, 'f1': 0.4218540080609046, 'number': 1065}
- Overall Precision: 0.2807
- Overall Recall: 0.2730
- Overall F1: 0.2768
- Overall Accuracy: 0.5691
## 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: 5e-06
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.9048 | 1.0 | 10 | 1.8492 | {'precision': 0.02683982683982684, 'recall': 0.07663782447466007, 'f1': 0.039756332157742866, 'number': 809} | {'precision': 0.003424657534246575, 'recall': 0.008403361344537815, 'f1': 0.004866180048661801, 'number': 119} | {'precision': 0.08558262014483213, 'recall': 0.12206572769953052, 'f1': 0.10061919504643962, 'number': 1065} | 0.0468 | 0.0968 | 0.0631 | 0.2625 |
| 1.8261 | 2.0 | 20 | 1.7805 | {'precision': 0.02488425925925926, 'recall': 0.05315203955500618, 'f1': 0.03389830508474576, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.11639344262295082, 'recall': 0.13333333333333333, 'f1': 0.12428884026258205, 'number': 1065} | 0.0620 | 0.0928 | 0.0744 | 0.3314 |
| 1.7557 | 3.0 | 30 | 1.7197 | {'precision': 0.018808777429467086, 'recall': 0.029666254635352288, 'f1': 0.02302158273381295, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.15336134453781514, 'recall': 0.13708920187793427, 'f1': 0.14476945959345563, 'number': 1065} | 0.0763 | 0.0853 | 0.0805 | 0.3579 |
| 1.7002 | 4.0 | 40 | 1.6648 | {'precision': 0.019029495718363463, 'recall': 0.024721878862793572, 'f1': 0.02150537634408602, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.19602977667493796, 'recall': 0.14835680751173708, 'f1': 0.16889363976483165, 'number': 1065} | 0.0959 | 0.0893 | 0.0925 | 0.3775 |
| 1.645 | 5.0 | 50 | 1.6121 | {'precision': 0.019801980198019802, 'recall': 0.024721878862793572, 'f1': 0.021990104452996154, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.22172452407614782, 'recall': 0.18591549295774648, 'f1': 0.20224719101123598, 'number': 1065} | 0.1146 | 0.1094 | 0.1119 | 0.4091 |
| 1.5951 | 6.0 | 60 | 1.5596 | {'precision': 0.029411764705882353, 'recall': 0.037082818294190356, 'f1': 0.032804811372334604, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.23694779116465864, 'recall': 0.2215962441314554, 'f1': 0.2290150412421155, 'number': 1065} | 0.1319 | 0.1335 | 0.1327 | 0.4421 |
| 1.5418 | 7.0 | 70 | 1.5109 | {'precision': 0.040755467196819085, 'recall': 0.05067985166872682, 'f1': 0.04517906336088154, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.27926267281105993, 'recall': 0.28450704225352114, 'f1': 0.2818604651162791, 'number': 1065} | 0.1645 | 0.1726 | 0.1685 | 0.4719 |
| 1.4954 | 8.0 | 80 | 1.4653 | {'precision': 0.050359712230215826, 'recall': 0.06056860321384425, 'f1': 0.05499438832772166, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.3016421780466724, 'recall': 0.3276995305164319, 'f1': 0.31413141314131415, 'number': 1065} | 0.1869 | 0.1997 | 0.1931 | 0.4973 |
| 1.4558 | 9.0 | 90 | 1.4245 | {'precision': 0.054140127388535034, 'recall': 0.0630407911001236, 'f1': 0.05825242718446602, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.3177966101694915, 'recall': 0.352112676056338, 'f1': 0.3340757238307349, 'number': 1065} | 0.2008 | 0.2137 | 0.2070 | 0.5168 |
| 1.4126 | 10.0 | 100 | 1.3893 | {'precision': 0.07432432432432433, 'recall': 0.0815822002472188, 'f1': 0.07778432527990571, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.33669185558354325, 'recall': 0.37652582159624415, 'f1': 0.3554964539007092, 'number': 1065} | 0.2246 | 0.2343 | 0.2294 | 0.5339 |
| 1.3759 | 11.0 | 110 | 1.3592 | {'precision': 0.08333333333333333, 'recall': 0.0865265760197775, 'f1': 0.08489993935718616, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.3618807724601176, 'recall': 0.40469483568075115, 'f1': 0.38209219858156024, 'number': 1065} | 0.2467 | 0.2514 | 0.2490 | 0.5470 |
| 1.3663 | 12.0 | 120 | 1.3358 | {'precision': 0.08531994981179424, 'recall': 0.08405438813349815, 'f1': 0.08468244084682441, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.37638062871707734, 'recall': 0.415962441314554, 'f1': 0.39518287243532557, 'number': 1065} | 0.2589 | 0.2564 | 0.2576 | 0.5545 |
| 1.3323 | 13.0 | 130 | 1.3192 | {'precision': 0.0916030534351145, 'recall': 0.08899876390605686, 'f1': 0.090282131661442, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.38649789029535864, 'recall': 0.4300469483568075, 'f1': 0.40711111111111115, 'number': 1065} | 0.2689 | 0.2659 | 0.2674 | 0.5635 |
| 1.3268 | 14.0 | 140 | 1.3094 | {'precision': 0.09585492227979274, 'recall': 0.09147095179233622, 'f1': 0.09361163820366855, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.3974358974358974, 'recall': 0.43661971830985913, 'f1': 0.4161073825503355, 'number': 1065} | 0.2775 | 0.2704 | 0.2740 | 0.5671 |
| 1.2988 | 15.0 | 150 | 1.3057 | {'precision': 0.09480519480519481, 'recall': 0.09023485784919653, 'f1': 0.09246358454718177, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.4032534246575342, 'recall': 0.4422535211267606, 'f1': 0.4218540080609046, 'number': 1065} | 0.2807 | 0.2730 | 0.2768 | 0.5691 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3
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