| | --- |
| | license: mit |
| | base_model: microsoft/layoutlm-base-uncased |
| | tags: |
| | - generated_from_trainer |
| | datasets: |
| | - funsd |
| | model-index: |
| | - name: layoutlm-funsd1 |
| | 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-funsd1 |
| |
|
| | 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: 0.6653 |
| | - Answer: {'precision': 0.6705756929637526, 'recall': 0.7775030902348579, 'f1': 0.7200915855752718, 'number': 809} |
| | - Header: {'precision': 0.30275229357798167, 'recall': 0.2773109243697479, 'f1': 0.28947368421052627, 'number': 119} |
| | - Question: {'precision': 0.7173732335827099, 'recall': 0.8103286384976526, 'f1': 0.7610229276895942, 'number': 1065} |
| | - Overall Precision: 0.6778 |
| | - Overall Recall: 0.7652 |
| | - Overall F1: 0.7188 |
| | - Overall Accuracy: 0.7992 |
| |
|
| | ## 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: 10 |
| | - mixed_precision_training: Native AMP |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |
| | |:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:| |
| | | 1.8388 | 1.0 | 10 | 1.6345 | {'precision': 0.010158013544018058, 'recall': 0.011124845488257108, 'f1': 0.010619469026548672, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.12983770287141075, 'recall': 0.09765258215962441, 'f1': 0.11146838156484459, 'number': 1065} | 0.0670 | 0.0567 | 0.0614 | 0.3424 | |
| | | 1.5101 | 2.0 | 20 | 1.3279 | {'precision': 0.10227272727272728, 'recall': 0.08899876390605686, 'f1': 0.09517514871116987, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.3082191780821918, 'recall': 0.4225352112676056, 'f1': 0.3564356435643564, 'number': 1065} | 0.2412 | 0.2619 | 0.2511 | 0.5546 | |
| | | 1.196 | 3.0 | 30 | 1.0812 | {'precision': 0.33375, 'recall': 0.3300370828182942, 'f1': 0.3318831572405221, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.4708233413269384, 'recall': 0.5530516431924882, 'f1': 0.5086355785837651, 'number': 1065} | 0.4153 | 0.4295 | 0.4223 | 0.6283 | |
| | | 0.957 | 4.0 | 40 | 0.8960 | {'precision': 0.5760082730093071, 'recall': 0.688504326328801, 'f1': 0.6272522522522522, 'number': 809} | {'precision': 0.027777777777777776, 'recall': 0.008403361344537815, 'f1': 0.012903225806451613, 'number': 119} | {'precision': 0.6268939393939394, 'recall': 0.6215962441314554, 'f1': 0.6242338519566243, 'number': 1065} | 0.5925 | 0.6121 | 0.6022 | 0.7315 | |
| | | 0.7609 | 5.0 | 50 | 0.7756 | {'precision': 0.608955223880597, 'recall': 0.7564894932014833, 'f1': 0.6747519294377067, 'number': 809} | {'precision': 0.11428571428571428, 'recall': 0.06722689075630252, 'f1': 0.08465608465608465, 'number': 119} | {'precision': 0.6362098138747885, 'recall': 0.7061032863849765, 'f1': 0.669336893635959, 'number': 1065} | 0.6079 | 0.6884 | 0.6456 | 0.7649 | |
| | | 0.634 | 6.0 | 60 | 0.7261 | {'precision': 0.6207951070336392, 'recall': 0.7527812113720643, 'f1': 0.6804469273743018, 'number': 809} | {'precision': 0.24, 'recall': 0.15126050420168066, 'f1': 0.18556701030927833, 'number': 119} | {'precision': 0.6666666666666666, 'recall': 0.7380281690140845, 'f1': 0.7005347593582888, 'number': 1065} | 0.6322 | 0.7090 | 0.6684 | 0.7783 | |
| | | 0.5815 | 7.0 | 70 | 0.6992 | {'precision': 0.6612377850162866, 'recall': 0.7527812113720643, 'f1': 0.7040462427745664, 'number': 809} | {'precision': 0.27586206896551724, 'recall': 0.20168067226890757, 'f1': 0.23300970873786409, 'number': 119} | {'precision': 0.6899841017488076, 'recall': 0.8150234741784037, 'f1': 0.7473095135600517, 'number': 1065} | 0.6624 | 0.7531 | 0.7049 | 0.7906 | |
| | | 0.5279 | 8.0 | 80 | 0.6827 | {'precision': 0.6687631027253669, 'recall': 0.788627935723115, 'f1': 0.7237663074305162, 'number': 809} | {'precision': 0.3010752688172043, 'recall': 0.23529411764705882, 'f1': 0.2641509433962264, 'number': 119} | {'precision': 0.7285464098073555, 'recall': 0.7812206572769953, 'f1': 0.7539646579066607, 'number': 1065} | 0.6843 | 0.7516 | 0.7164 | 0.7973 | |
| | | 0.4907 | 9.0 | 90 | 0.6732 | {'precision': 0.6609442060085837, 'recall': 0.761433868974042, 'f1': 0.707639287765652, 'number': 809} | {'precision': 0.3148148148148148, 'recall': 0.2857142857142857, 'f1': 0.29955947136563876, 'number': 119} | {'precision': 0.7145214521452146, 'recall': 0.8131455399061033, 'f1': 0.7606499780412823, 'number': 1065} | 0.6732 | 0.7607 | 0.7143 | 0.7971 | |
| | | 0.4734 | 10.0 | 100 | 0.6653 | {'precision': 0.6705756929637526, 'recall': 0.7775030902348579, 'f1': 0.7200915855752718, 'number': 809} | {'precision': 0.30275229357798167, 'recall': 0.2773109243697479, 'f1': 0.28947368421052627, 'number': 119} | {'precision': 0.7173732335827099, 'recall': 0.8103286384976526, 'f1': 0.7610229276895942, 'number': 1065} | 0.6778 | 0.7652 | 0.7188 | 0.7992 | |
| |
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| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.41.2 |
| | - Pytorch 2.3.1+cu121 |
| | - Datasets 2.19.2 |
| | - Tokenizers 0.19.1 |
| |
|
