| | --- |
| | 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 |
| |
|
