End of training

README.md

@@ -0,0 +1,82 @@
+---
+library_name: transformers
+license: mit
+base_model: microsoft/layoutlm-base-uncased
+tags:
+- generated_from_trainer
+datasets:
+- funsd
+model-index:
+- name: layoutlm-FUNSD-only
+  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-only
+
+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: 0.6561
+- Eader: {'precision': 0.3770491803278688, 'recall': 0.27710843373493976, 'f1': 0.3194444444444444, 'number': 83}
+- Nswer: {'precision': 0.5213675213675214, 'recall': 0.5951219512195122, 'f1': 0.5558086560364465, 'number': 205}
+- Uestion: {'precision': 0.3722627737226277, 'recall': 0.44155844155844154, 'f1': 0.40396039603960393, 'number': 231}
+- Overall Precision: 0.4341
+- Overall Recall: 0.4759
+- Overall F1: 0.4540
+- Overall Accuracy: 0.7927
+
+## 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 | Eader                                                                                                      | Nswer                                                                                                      | Uestion                                                                                                     | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:----------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.2996        | 1.0   | 8    | 1.0787          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 83}                                                 | {'precision': 0.0748502994011976, 'recall': 0.24390243902439024, 'f1': 0.11454753722794961, 'number': 205} | {'precision': 0.0704647676161919, 'recall': 0.20346320346320346, 'f1': 0.10467706013363029, 'number': 231}  | 0.0727            | 0.1869         | 0.1046     | 0.6075           |
+| 1.0224        | 2.0   | 16   | 0.8740          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 83}                                                 | {'precision': 0.20823798627002288, 'recall': 0.44390243902439025, 'f1': 0.2834890965732087, 'number': 205} | {'precision': 0.17551963048498845, 'recall': 0.329004329004329, 'f1': 0.2289156626506024, 'number': 231}    | 0.1920            | 0.3218         | 0.2405     | 0.7001           |
+| 0.8193        | 3.0   | 24   | 0.6924          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 83}                                                 | {'precision': 0.310126582278481, 'recall': 0.47804878048780486, 'f1': 0.3761996161228407, 'number': 205}   | {'precision': 0.25671641791044775, 'recall': 0.3722943722943723, 'f1': 0.30388692579505294, 'number': 231}  | 0.2759            | 0.3545         | 0.3103     | 0.7519           |
+| 0.6764        | 4.0   | 32   | 0.6312          | {'precision': 0.14285714285714285, 'recall': 0.04819277108433735, 'f1': 0.07207207207207209, 'number': 83} | {'precision': 0.3914590747330961, 'recall': 0.5365853658536586, 'f1': 0.45267489711934156, 'number': 205}  | {'precision': 0.36, 'recall': 0.42857142857142855, 'f1': 0.391304347826087, 'number': 231}                  | 0.3647            | 0.4104         | 0.3862     | 0.7634           |
+| 0.5456        | 5.0   | 40   | 0.5856          | {'precision': 0.3, 'recall': 0.18072289156626506, 'f1': 0.2255639097744361, 'number': 83}                  | {'precision': 0.42424242424242425, 'recall': 0.5463414634146342, 'f1': 0.47761194029850745, 'number': 205} | {'precision': 0.38022813688212925, 'recall': 0.4329004329004329, 'f1': 0.4048582995951417, 'number': 231}   | 0.3934            | 0.4374         | 0.4142     | 0.7846           |
+| 0.4547        | 6.0   | 48   | 0.5899          | {'precision': 0.4074074074074074, 'recall': 0.26506024096385544, 'f1': 0.32116788321167883, 'number': 83}  | {'precision': 0.44129554655870445, 'recall': 0.5317073170731708, 'f1': 0.4823008849557522, 'number': 205}  | {'precision': 0.35094339622641507, 'recall': 0.4025974025974026, 'f1': 0.37500000000000006, 'number': 231}  | 0.3958            | 0.4316         | 0.4129     | 0.7827           |
+| 0.3815        | 7.0   | 56   | 0.5921          | {'precision': 0.3888888888888889, 'recall': 0.25301204819277107, 'f1': 0.3065693430656934, 'number': 83}   | {'precision': 0.483739837398374, 'recall': 0.5804878048780487, 'f1': 0.5277161862527716, 'number': 205}    | {'precision': 0.34657039711191334, 'recall': 0.4155844155844156, 'f1': 0.3779527559055118, 'number': 231}   | 0.4090            | 0.4547         | 0.4307     | 0.7893           |
+| 0.324         | 8.0   | 64   | 0.5872          | {'precision': 0.5, 'recall': 0.26506024096385544, 'f1': 0.3464566929133859, 'number': 83}                  | {'precision': 0.4957983193277311, 'recall': 0.5756097560975609, 'f1': 0.5327313769751693, 'number': 205}   | {'precision': 0.39543726235741444, 'recall': 0.45021645021645024, 'f1': 0.42105263157894735, 'number': 231} | 0.4477            | 0.4701         | 0.4586     | 0.7989           |
+| 0.284         | 9.0   | 72   | 0.6026          | {'precision': 0.3968253968253968, 'recall': 0.30120481927710846, 'f1': 0.34246575342465757, 'number': 83}  | {'precision': 0.4717741935483871, 'recall': 0.5707317073170731, 'f1': 0.5165562913907285, 'number': 205}   | {'precision': 0.35789473684210527, 'recall': 0.44155844155844154, 'f1': 0.39534883720930236, 'number': 231} | 0.4094            | 0.4701         | 0.4377     | 0.7897           |
+| 0.249         | 10.0  | 80   | 0.6137          | {'precision': 0.4423076923076923, 'recall': 0.27710843373493976, 'f1': 0.34074074074074073, 'number': 83}  | {'precision': 0.5041322314049587, 'recall': 0.5951219512195122, 'f1': 0.5458612975391499, 'number': 205}   | {'precision': 0.3607142857142857, 'recall': 0.43722943722943725, 'f1': 0.39530332681017616, 'number': 231}  | 0.4286            | 0.4740         | 0.4501     | 0.7981           |
+| 0.2288        | 11.0  | 88   | 0.6367          | {'precision': 0.38571428571428573, 'recall': 0.3253012048192771, 'f1': 0.35294117647058826, 'number': 83}  | {'precision': 0.48717948717948717, 'recall': 0.5560975609756098, 'f1': 0.5193621867881548, 'number': 205}  | {'precision': 0.38267148014440433, 'recall': 0.4588744588744589, 'f1': 0.4173228346456693, 'number': 231}   | 0.4251            | 0.4759         | 0.4491     | 0.7912           |
+| 0.2031        | 12.0  | 96   | 0.6401          | {'precision': 0.46, 'recall': 0.27710843373493976, 'f1': 0.3458646616541353, 'number': 83}                 | {'precision': 0.497907949790795, 'recall': 0.5804878048780487, 'f1': 0.536036036036036, 'number': 205}     | {'precision': 0.3800738007380074, 'recall': 0.4458874458874459, 'f1': 0.4103585657370518, 'number': 231}    | 0.4375            | 0.4721         | 0.4541     | 0.7985           |
+| 0.193         | 13.0  | 104  | 0.6539          | {'precision': 0.37142857142857144, 'recall': 0.3132530120481928, 'f1': 0.33986928104575165, 'number': 83}  | {'precision': 0.5321100917431193, 'recall': 0.5658536585365853, 'f1': 0.5484633569739952, 'number': 205}   | {'precision': 0.3969465648854962, 'recall': 0.45021645021645024, 'f1': 0.4219066937119675, 'number': 231}   | 0.4473            | 0.4740         | 0.4602     | 0.7904           |
+| 0.1895        | 14.0  | 112  | 0.6557          | {'precision': 0.39344262295081966, 'recall': 0.2891566265060241, 'f1': 0.3333333333333333, 'number': 83}   | {'precision': 0.5429864253393665, 'recall': 0.5853658536585366, 'f1': 0.5633802816901408, 'number': 205}   | {'precision': 0.3916349809885932, 'recall': 0.4458874458874459, 'f1': 0.41700404858299595, 'number': 231}   | 0.4532            | 0.4759         | 0.4643     | 0.7904           |
+| 0.1775        | 15.0  | 120  | 0.6561          | {'precision': 0.3770491803278688, 'recall': 0.27710843373493976, 'f1': 0.3194444444444444, 'number': 83}   | {'precision': 0.5213675213675214, 'recall': 0.5951219512195122, 'f1': 0.5558086560364465, 'number': 205}   | {'precision': 0.3722627737226277, 'recall': 0.44155844155844154, 'f1': 0.40396039603960393, 'number': 231}  | 0.4341            | 0.4759         | 0.4540     | 0.7927           |
+
+
+### Framework versions
+
+- Transformers 4.49.0
+- Pytorch 2.6.0+cu124
+- Datasets 3.3.2
+- Tokenizers 0.21.0

logs/events.out.tfevents.1741598052.DESKTOP-HA84SVN.75042.6

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preprocessor_config.json

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special_tokens_map.json

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tokenizer_config.json

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+}