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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: 0.7010
- Answer: {'precision': 0.6882416396979504, 'recall': 0.788627935723115, 'f1': 0.7350230414746545, 'number': 809}
- Header: {'precision': 0.2857142857142857, 'recall': 0.3025210084033613, 'f1': 0.2938775510204082, 'number': 119}
- Question: {'precision': 0.7771836007130125, 'recall': 0.8187793427230047, 'f1': 0.7974394147233654, 'number': 1065}
- Overall Precision: 0.7108
- Overall Recall: 0.7757
- Overall F1: 0.7418
- Overall Accuracy: 0.8054
## 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 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.8001 | 1.0 | 10 | 1.6097 | {'precision': 0.015299026425591099, 'recall': 0.013597033374536464, 'f1': 0.014397905759162303, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.2779291553133515, 'recall': 0.19154929577464788, 'f1': 0.226792662590328, 'number': 1065} | 0.1480 | 0.1079 | 0.1248 | 0.3542 |
| 1.4627 | 2.0 | 20 | 1.2809 | {'precision': 0.15977175463623394, 'recall': 0.138442521631644, 'f1': 0.14834437086092714, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.42467948717948717, 'recall': 0.49765258215962443, 'f1': 0.45827929096411585, 'number': 1065} | 0.3294 | 0.3221 | 0.3257 | 0.5862 |
| 1.1306 | 3.0 | 30 | 1.0105 | {'precision': 0.41445783132530123, 'recall': 0.4252163164400494, 'f1': 0.4197681513117754, 'number': 809} | {'precision': 0.11764705882352941, 'recall': 0.03361344537815126, 'f1': 0.05228758169934641, 'number': 119} | {'precision': 0.5563607085346216, 'recall': 0.6488262910798122, 'f1': 0.5990463805808409, 'number': 1065} | 0.4934 | 0.5213 | 0.5070 | 0.6955 |
| 0.8744 | 4.0 | 40 | 0.8187 | {'precision': 0.5656565656565656, 'recall': 0.622991347342398, 'f1': 0.5929411764705883, 'number': 809} | {'precision': 0.2641509433962264, 'recall': 0.11764705882352941, 'f1': 0.16279069767441862, 'number': 119} | {'precision': 0.6538789428815004, 'recall': 0.72018779342723, 'f1': 0.6854334226988382, 'number': 1065} | 0.6070 | 0.6448 | 0.6253 | 0.7427 |
| 0.6901 | 5.0 | 50 | 0.7311 | {'precision': 0.635091496232508, 'recall': 0.7292954264524104, 'f1': 0.6789413118527043, 'number': 809} | {'precision': 0.25925925925925924, 'recall': 0.17647058823529413, 'f1': 0.21, 'number': 119} | {'precision': 0.6711675933280381, 'recall': 0.7934272300469484, 'f1': 0.7271944922547332, 'number': 1065} | 0.6417 | 0.7306 | 0.6832 | 0.7707 |
| 0.5703 | 6.0 | 60 | 0.7127 | {'precision': 0.6548856548856549, 'recall': 0.7787391841779975, 'f1': 0.7114624505928854, 'number': 809} | {'precision': 0.25555555555555554, 'recall': 0.19327731092436976, 'f1': 0.22009569377990432, 'number': 119} | {'precision': 0.7043701799485861, 'recall': 0.7718309859154929, 'f1': 0.7365591397849461, 'number': 1065} | 0.6647 | 0.7401 | 0.7004 | 0.7837 |
| 0.4964 | 7.0 | 70 | 0.6823 | {'precision': 0.6729758149316509, 'recall': 0.7911001236093943, 'f1': 0.7272727272727274, 'number': 809} | {'precision': 0.2647058823529412, 'recall': 0.226890756302521, 'f1': 0.24434389140271492, 'number': 119} | {'precision': 0.751099384344767, 'recall': 0.8018779342723005, 'f1': 0.7756584922797457, 'number': 1065} | 0.6945 | 0.7632 | 0.7272 | 0.7962 |
| 0.4347 | 8.0 | 80 | 0.6763 | {'precision': 0.6754478398314014, 'recall': 0.792336217552534, 'f1': 0.7292377701934016, 'number': 809} | {'precision': 0.23529411764705882, 'recall': 0.23529411764705882, 'f1': 0.23529411764705882, 'number': 119} | {'precision': 0.7530434782608696, 'recall': 0.8131455399061033, 'f1': 0.781941309255079, 'number': 1065} | 0.6921 | 0.7702 | 0.7290 | 0.8003 |
| 0.386 | 9.0 | 90 | 0.6695 | {'precision': 0.6803013993541442, 'recall': 0.7812113720642769, 'f1': 0.7272727272727273, 'number': 809} | {'precision': 0.29411764705882354, 'recall': 0.25210084033613445, 'f1': 0.27149321266968324, 'number': 119} | {'precision': 0.762157382847038, 'recall': 0.8093896713615023, 'f1': 0.785063752276867, 'number': 1065} | 0.7049 | 0.7647 | 0.7336 | 0.8091 |
| 0.3668 | 10.0 | 100 | 0.6898 | {'precision': 0.6729559748427673, 'recall': 0.7935723114956736, 'f1': 0.7283040272263188, 'number': 809} | {'precision': 0.29357798165137616, 'recall': 0.2689075630252101, 'f1': 0.28070175438596495, 'number': 119} | {'precision': 0.7695729537366548, 'recall': 0.812206572769953, 'f1': 0.7903152124257652, 'number': 1065} | 0.7037 | 0.7722 | 0.7364 | 0.8064 |
| 0.3174 | 11.0 | 110 | 0.6929 | {'precision': 0.6782700421940928, 'recall': 0.7948084054388134, 'f1': 0.7319294251565168, 'number': 809} | {'precision': 0.2903225806451613, 'recall': 0.3025210084033613, 'f1': 0.2962962962962963, 'number': 119} | {'precision': 0.7741364038972542, 'recall': 0.8206572769953052, 'f1': 0.796718322698268, 'number': 1065} | 0.7056 | 0.7792 | 0.7406 | 0.8031 |
| 0.3134 | 12.0 | 120 | 0.6977 | {'precision': 0.6860215053763441, 'recall': 0.788627935723115, 'f1': 0.7337550316273721, 'number': 809} | {'precision': 0.29411764705882354, 'recall': 0.29411764705882354, 'f1': 0.29411764705882354, 'number': 119} | {'precision': 0.7796762589928058, 'recall': 0.8140845070422535, 'f1': 0.7965089572806615, 'number': 1065} | 0.7126 | 0.7727 | 0.7415 | 0.8053 |
| 0.2851 | 13.0 | 130 | 0.7022 | {'precision': 0.6894679695982627, 'recall': 0.7849196538936959, 'f1': 0.7341040462427746, 'number': 809} | {'precision': 0.2773109243697479, 'recall': 0.2773109243697479, 'f1': 0.2773109243697479, 'number': 119} | {'precision': 0.7772848269742679, 'recall': 0.8225352112676056, 'f1': 0.7992700729927008, 'number': 1065} | 0.7125 | 0.7747 | 0.7423 | 0.8056 |
| 0.2693 | 14.0 | 140 | 0.7003 | {'precision': 0.6897297297297297, 'recall': 0.788627935723115, 'f1': 0.7358708189158016, 'number': 809} | {'precision': 0.28688524590163933, 'recall': 0.29411764705882354, 'f1': 0.2904564315352697, 'number': 119} | {'precision': 0.7755102040816326, 'recall': 0.8206572769953052, 'f1': 0.7974452554744526, 'number': 1065} | 0.7116 | 0.7762 | 0.7425 | 0.8048 |
| 0.2636 | 15.0 | 150 | 0.7010 | {'precision': 0.6882416396979504, 'recall': 0.788627935723115, 'f1': 0.7350230414746545, 'number': 809} | {'precision': 0.2857142857142857, 'recall': 0.3025210084033613, 'f1': 0.2938775510204082, 'number': 119} | {'precision': 0.7771836007130125, 'recall': 0.8187793427230047, 'f1': 0.7974394147233654, 'number': 1065} | 0.7108 | 0.7757 | 0.7418 | 0.8054 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
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