| | --- |
| | library_name: transformers |
| | license: mit |
| | 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: 1.5594 |
| | - Answer: {'precision': 0.03052064631956912, 'recall': 0.042027194066749075, 'f1': 0.035361414456578255, 'number': 809} |
| | - Header: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} |
| | - Question: {'precision': 0.18327402135231316, 'recall': 0.19342723004694837, 'f1': 0.18821379625399726, 'number': 1065} |
| | - Overall Precision: 0.1072 |
| | - Overall Recall: 0.1204 |
| | - Overall F1: 0.1134 |
| | - Overall Accuracy: 0.4038 |
| |
|
| | ## 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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| | - lr_scheduler_type: linear |
| | - num_epochs: 2 |
| | - mixed_precision_training: Native AMP |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |
| | |:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:| |
| | | 1.8318 | 1.0 | 10 | 1.6681 | {'precision': 0.009746588693957114, 'recall': 0.012360939431396786, 'f1': 0.010899182561307902, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.0981169474727453, 'recall': 0.09295774647887324, 'f1': 0.09546769527483126, 'number': 1065} | 0.0535 | 0.0547 | 0.0541 | 0.3444 | |
| | | 1.5836 | 2.0 | 20 | 1.5594 | {'precision': 0.03052064631956912, 'recall': 0.042027194066749075, 'f1': 0.035361414456578255, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.18327402135231316, 'recall': 0.19342723004694837, 'f1': 0.18821379625399726, 'number': 1065} | 0.1072 | 0.1204 | 0.1134 | 0.4038 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.47.1 |
| | - Pytorch 2.5.1+cu124 |
| | - Datasets 3.2.0 |
| | - Tokenizers 0.21.0 |
| |
|
