| --- |
| 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 |
|
|
