| | ---
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| | license: mit
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| | base_model: microsoft/layoutlm-base-uncased
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| | tags:
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| | - generated_from_trainer
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| | datasets:
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| | - funsd
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| | model-index:
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| | - name: layoutlm-funsd
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| | results: []
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| | ---
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| |
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| | <!-- 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. -->
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| |
|
| | # layoutlm-funsd
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| |
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| | This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the funsd dataset.
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| | It achieves the following results on the evaluation set:
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| | - Loss: 0.7180
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| | - Answer: {'precision': 0.596, 'recall': 0.7367119901112484, 'f1': 0.6589275843007186, 'number': 809}
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| | - Header: {'precision': 0.08571428571428572, 'recall': 0.05042016806722689, 'f1': 0.06349206349206349, 'number': 119}
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| | - Question: {'precision': 0.6859706362153344, 'recall': 0.7896713615023474, 'f1': 0.7341772151898733, 'number': 1065}
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| | - Overall Precision: 0.6285
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| | - Overall Recall: 0.7240
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| | - Overall F1: 0.6729
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| | - Overall Accuracy: 0.7704
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| |
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| | ## Model description
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| |
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| | More information needed
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| |
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| | ## Intended uses & limitations
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| |
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| | More information needed
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| |
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| | ## Training and evaluation data
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| |
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| | More information needed
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| |
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| | ## Training procedure
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| |
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| | ### Training hyperparameters
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| |
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| | The following hyperparameters were used during training:
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| | - learning_rate: 3e-05
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| | - train_batch_size: 8
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| | - eval_batch_size: 4
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| | - seed: 42
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| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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| | - lr_scheduler_type: linear
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| | - num_epochs: 5
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| | - mixed_precision_training: Native AMP
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| |
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| | ### Training results
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| |
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| | | Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
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| | |:-------------:|:-----:|:----:|:---------------:|:----------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
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| | | 1.6774 | 1.0 | 19 | 1.3758 | {'precision': 0.06839378238341969, 'recall': 0.0815822002472188, 'f1': 0.07440811724915444, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.332824427480916, 'recall': 0.40938967136150234, 'f1': 0.367157894736842, 'number': 1065} | 0.2207 | 0.2519 | 0.2352 | 0.4928 |
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| | | 1.169 | 2.0 | 38 | 0.9500 | {'precision': 0.4467425025853154, 'recall': 0.5339925834363412, 'f1': 0.48648648648648646, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.557753164556962, 'recall': 0.6619718309859155, 'f1': 0.605410047230571, 'number': 1065} | 0.5076 | 0.5705 | 0.5372 | 0.6799 |
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| | | 0.8429 | 3.0 | 57 | 0.7922 | {'precision': 0.5751953125, 'recall': 0.7280593325092707, 'f1': 0.6426623022367702, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.6494676494676495, 'recall': 0.7446009389671362, 'f1': 0.6937882764654418, 'number': 1065} | 0.6051 | 0.6934 | 0.6462 | 0.7457 |
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| | | 0.6915 | 4.0 | 76 | 0.7294 | {'precision': 0.5885262116716122, 'recall': 0.7354758961681088, 'f1': 0.6538461538461539, 'number': 809} | {'precision': 0.05172413793103448, 'recall': 0.025210084033613446, 'f1': 0.03389830508474576, 'number': 119} | {'precision': 0.6642628205128205, 'recall': 0.7784037558685446, 'f1': 0.7168179853004755, 'number': 1065} | 0.6159 | 0.7160 | 0.6622 | 0.7651 |
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| | | 0.6221 | 5.0 | 95 | 0.7180 | {'precision': 0.596, 'recall': 0.7367119901112484, 'f1': 0.6589275843007186, 'number': 809} | {'precision': 0.08571428571428572, 'recall': 0.05042016806722689, 'f1': 0.06349206349206349, 'number': 119} | {'precision': 0.6859706362153344, 'recall': 0.7896713615023474, 'f1': 0.7341772151898733, 'number': 1065} | 0.6285 | 0.7240 | 0.6729 | 0.7704 |
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| |
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| | ### Framework versions
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| |
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| | - Transformers 4.43.4
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| | - Pytorch 2.4.0+cpu
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| | - Datasets 2.20.0
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| | - Tokenizers 0.19.1
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| |
|
