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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: 0.6791
- Answer: {'precision': 0.6752411575562701, 'recall': 0.7787391841779975, 'f1': 0.7233065442020666, 'number': 809}
- Header: {'precision': 0.25742574257425743, 'recall': 0.2184873949579832, 'f1': 0.23636363636363636, 'number': 119}
- Question: {'precision': 0.7172995780590717, 'recall': 0.7981220657276995, 'f1': 0.7555555555555554, 'number': 1065}
- Overall Precision: 0.6787
- Overall Recall: 0.7556
- Overall F1: 0.7151
- Overall Accuracy: 0.7962
## 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: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Use 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: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.8311 | 1.0 | 5 | 1.7018 | {'precision': 0.015086206896551725, 'recall': 0.02595797280593325, 'f1': 0.01908223534756929, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.09192692987625221, 'recall': 0.14647887323943662, 'f1': 0.11296162201303404, 'number': 1065} | 0.0573 | 0.0888 | 0.0696 | 0.3364 |
| 1.6261 | 2.0 | 10 | 1.5278 | {'precision': 0.018244013683010263, 'recall': 0.019777503090234856, 'f1': 0.018979833926453145, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.24725943970767356, 'recall': 0.19061032863849764, 'f1': 0.21527041357370094, 'number': 1065} | 0.1290 | 0.1099 | 0.1187 | 0.4110 |
| 1.4654 | 3.0 | 15 | 1.3491 | {'precision': 0.07093023255813953, 'recall': 0.0754017305315204, 'f1': 0.07309766327142002, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.3611111111111111, 'recall': 0.3539906103286385, 'f1': 0.35751541014698907, 'number': 1065} | 0.2300 | 0.2198 | 0.2248 | 0.5293 |
| 1.2722 | 4.0 | 20 | 1.1745 | {'precision': 0.2922222222222222, 'recall': 0.32509270704573545, 'f1': 0.30778232884727913, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.4624, 'recall': 0.5427230046948357, 'f1': 0.49935205183585313, 'number': 1065} | 0.3901 | 0.4220 | 0.4054 | 0.6268 |
| 1.0874 | 5.0 | 25 | 1.0226 | {'precision': 0.4374331550802139, 'recall': 0.5055624227441285, 'f1': 0.4690366972477064, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.5391849529780565, 'recall': 0.6460093896713615, 'f1': 0.5877829987184965, 'number': 1065} | 0.4897 | 0.5504 | 0.5183 | 0.6874 |
| 0.9491 | 6.0 | 30 | 0.8969 | {'precision': 0.5340022296544036, 'recall': 0.5920889987639061, 'f1': 0.5615474794841734, 'number': 809} | {'precision': 0.07317073170731707, 'recall': 0.025210084033613446, 'f1': 0.0375, 'number': 119} | {'precision': 0.6014376996805112, 'recall': 0.7070422535211267, 'f1': 0.6499784203711697, 'number': 1065} | 0.5639 | 0.6197 | 0.5905 | 0.7330 |
| 0.8302 | 7.0 | 35 | 0.8232 | {'precision': 0.5977482088024565, 'recall': 0.7218788627935723, 'f1': 0.6539753639417694, 'number': 809} | {'precision': 0.1568627450980392, 'recall': 0.06722689075630252, 'f1': 0.09411764705882353, 'number': 119} | {'precision': 0.6558669001751314, 'recall': 0.7032863849765258, 'f1': 0.678749433620299, 'number': 1065} | 0.6180 | 0.6729 | 0.6442 | 0.7457 |
| 0.7414 | 8.0 | 40 | 0.7707 | {'precision': 0.6148300720906282, 'recall': 0.7379480840543882, 'f1': 0.6707865168539326, 'number': 809} | {'precision': 0.18333333333333332, 'recall': 0.09243697478991597, 'f1': 0.12290502793296088, 'number': 119} | {'precision': 0.6633825944170771, 'recall': 0.7586854460093897, 'f1': 0.7078405606657906, 'number': 1065} | 0.6296 | 0.7105 | 0.6676 | 0.7665 |
| 0.671 | 9.0 | 45 | 0.7335 | {'precision': 0.6334012219959266, 'recall': 0.7688504326328801, 'f1': 0.6945840312674483, 'number': 809} | {'precision': 0.2159090909090909, 'recall': 0.15966386554621848, 'f1': 0.18357487922705312, 'number': 119} | {'precision': 0.6922413793103448, 'recall': 0.7539906103286385, 'f1': 0.7217977528089887, 'number': 1065} | 0.6475 | 0.7245 | 0.6839 | 0.7742 |
| 0.6278 | 10.0 | 50 | 0.7206 | {'precision': 0.649364406779661, 'recall': 0.757725587144623, 'f1': 0.6993725042783799, 'number': 809} | {'precision': 0.21212121212121213, 'recall': 0.17647058823529413, 'f1': 0.1926605504587156, 'number': 119} | {'precision': 0.6996587030716723, 'recall': 0.7699530516431925, 'f1': 0.7331247206079572, 'number': 1065} | 0.6564 | 0.7296 | 0.6911 | 0.7847 |
| 0.5974 | 11.0 | 55 | 0.7095 | {'precision': 0.653276955602537, 'recall': 0.7639060568603214, 'f1': 0.7042735042735042, 'number': 809} | {'precision': 0.21505376344086022, 'recall': 0.16806722689075632, 'f1': 0.18867924528301888, 'number': 119} | {'precision': 0.7091531223267751, 'recall': 0.7784037558685446, 'f1': 0.7421665174574755, 'number': 1065} | 0.6644 | 0.7361 | 0.6984 | 0.7852 |
| 0.5594 | 12.0 | 60 | 0.6868 | {'precision': 0.6523076923076923, 'recall': 0.7861557478368356, 'f1': 0.7130044843049326, 'number': 809} | {'precision': 0.2619047619047619, 'recall': 0.18487394957983194, 'f1': 0.21674876847290642, 'number': 119} | {'precision': 0.7068376068376069, 'recall': 0.7765258215962442, 'f1': 0.7400447427293065, 'number': 1065} | 0.6662 | 0.7451 | 0.7035 | 0.7909 |
| 0.5374 | 13.0 | 65 | 0.6797 | {'precision': 0.655958549222798, 'recall': 0.7824474660074165, 'f1': 0.7136414881623451, 'number': 809} | {'precision': 0.25842696629213485, 'recall': 0.19327731092436976, 'f1': 0.22115384615384615, 'number': 119} | {'precision': 0.7089678510998308, 'recall': 0.7868544600938967, 'f1': 0.7458834000890076, 'number': 1065} | 0.6682 | 0.7496 | 0.7066 | 0.7926 |
| 0.5196 | 14.0 | 70 | 0.6794 | {'precision': 0.673469387755102, 'recall': 0.7750309023485785, 'f1': 0.7206896551724138, 'number': 809} | {'precision': 0.2631578947368421, 'recall': 0.21008403361344538, 'f1': 0.23364485981308414, 'number': 119} | {'precision': 0.7148864592094197, 'recall': 0.7981220657276995, 'f1': 0.7542147293700088, 'number': 1065} | 0.6781 | 0.7536 | 0.7139 | 0.7954 |
| 0.5065 | 15.0 | 75 | 0.6791 | {'precision': 0.6752411575562701, 'recall': 0.7787391841779975, 'f1': 0.7233065442020666, 'number': 809} | {'precision': 0.25742574257425743, 'recall': 0.2184873949579832, 'f1': 0.23636363636363636, 'number': 119} | {'precision': 0.7172995780590717, 'recall': 0.7981220657276995, 'f1': 0.7555555555555554, 'number': 1065} | 0.6787 | 0.7556 | 0.7151 | 0.7962 |
### Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0
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