| --- |
| 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: 0.6178 |
| - Answer: {'precision': 0.6652719665271967, 'recall': 0.7861557478368356, 'f1': 0.7206798866855525, 'number': 809} |
| - Header: {'precision': 0.29133858267716534, 'recall': 0.31092436974789917, 'f1': 0.3008130081300813, 'number': 119} |
| - Question: {'precision': 0.7537248028045574, 'recall': 0.8075117370892019, 'f1': 0.7796917497733454, 'number': 1065} |
| - Overall Precision: 0.6893 |
| - Overall Recall: 0.7692 |
| - Overall F1: 0.7271 |
| - Overall Accuracy: 0.8014 |
|
|
| ## 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: 4 |
| - eval_batch_size: 4 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 5 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:| |
| | 1.5284 | 1.0 | 38 | 1.0167 | {'precision': 0.3938144329896907, 'recall': 0.4721878862793572, 'f1': 0.4294547498594716, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.5845528455284553, 'recall': 0.6751173708920187, 'f1': 0.6265795206971678, 'number': 1065} | 0.4959 | 0.5524 | 0.5227 | 0.6689 | |
| | 0.8661 | 2.0 | 76 | 0.7179 | {'precision': 0.630346232179226, 'recall': 0.765142150803461, 'f1': 0.6912339475153545, 'number': 809} | {'precision': 0.2087912087912088, 'recall': 0.15966386554621848, 'f1': 0.18095238095238092, 'number': 119} | {'precision': 0.7058823529411765, 'recall': 0.7436619718309859, 'f1': 0.7242798353909465, 'number': 1065} | 0.6515 | 0.7175 | 0.6829 | 0.7596 | |
| | 0.6265 | 3.0 | 114 | 0.6470 | {'precision': 0.6458546571136131, 'recall': 0.7799752781211372, 'f1': 0.7066069428891377, 'number': 809} | {'precision': 0.2972972972972973, 'recall': 0.2773109243697479, 'f1': 0.28695652173913044, 'number': 119} | {'precision': 0.7359649122807017, 'recall': 0.787793427230047, 'f1': 0.7609977324263038, 'number': 1065} | 0.6746 | 0.7541 | 0.7122 | 0.7879 | |
| | 0.5076 | 4.0 | 152 | 0.6207 | {'precision': 0.6680851063829787, 'recall': 0.7762669962917181, 'f1': 0.7181246426529445, 'number': 809} | {'precision': 0.28, 'recall': 0.29411764705882354, 'f1': 0.28688524590163933, 'number': 119} | {'precision': 0.7368421052631579, 'recall': 0.828169014084507, 'f1': 0.7798408488063661, 'number': 1065} | 0.6830 | 0.7752 | 0.7262 | 0.8003 | |
| | 0.4471 | 5.0 | 190 | 0.6178 | {'precision': 0.6652719665271967, 'recall': 0.7861557478368356, 'f1': 0.7206798866855525, 'number': 809} | {'precision': 0.29133858267716534, 'recall': 0.31092436974789917, 'f1': 0.3008130081300813, 'number': 119} | {'precision': 0.7537248028045574, 'recall': 0.8075117370892019, 'f1': 0.7796917497733454, 'number': 1065} | 0.6893 | 0.7692 | 0.7271 | 0.8014 | |
|
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|
| ### Framework versions |
|
|
| - Transformers 4.26.1 |
| - Pytorch 1.13.1+cu116 |
| - Datasets 2.10.1 |
| - Tokenizers 0.13.2 |
|
|
