End of training

README.md

@@ -0,0 +1,81 @@
+---
+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: 0.7099
+- Answer: {'precision': 0.7126948775055679, 'recall': 0.7911001236093943, 'f1': 0.7498535442296427, 'number': 809}
+- Header: {'precision': 0.3793103448275862, 'recall': 0.3697478991596639, 'f1': 0.374468085106383, 'number': 119}
+- Question: {'precision': 0.7863397548161121, 'recall': 0.8431924882629108, 'f1': 0.813774354327141, 'number': 1065}
+- Overall Precision: 0.7338
+- Overall Recall: 0.7938
+- Overall F1: 0.7626
+- Overall Accuracy: 0.8008
+
+## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- 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 |
+|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.8413        | 1.0   | 10   | 1.6504          | {'precision': 0.011467889908256881, 'recall': 0.006180469715698393, 'f1': 0.008032128514056226, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.2831168831168831, 'recall': 0.10234741784037558, 'f1': 0.15034482758620688, 'number': 1065} | 0.1389            | 0.0572         | 0.0810     | 0.3247           |
+| 1.5029        | 2.0   | 20   | 1.3220          | {'precision': 0.1353811149032992, 'recall': 0.14709517923362175, 'f1': 0.1409952606635071, 'number': 809}      | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.40875370919881304, 'recall': 0.5173708920187794, 'f1': 0.4566929133858268, 'number': 1065}  | 0.3009            | 0.3362         | 0.3175     | 0.5584           |
+| 1.1608        | 3.0   | 30   | 1.0033          | {'precision': 0.4221267454350161, 'recall': 0.4857849196538937, 'f1': 0.45172413793103444, 'number': 809}      | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.5333333333333333, 'recall': 0.6910798122065728, 'f1': 0.6020449897750512, 'number': 1065}   | 0.4871            | 0.5665         | 0.5238     | 0.6833           |
+| 0.9008        | 4.0   | 40   | 0.8415          | {'precision': 0.5449101796407185, 'recall': 0.6749072929542645, 'f1': 0.6029817780231916, 'number': 809}       | {'precision': 0.11904761904761904, 'recall': 0.04201680672268908, 'f1': 0.06211180124223603, 'number': 119} | {'precision': 0.6373355263157895, 'recall': 0.7276995305164319, 'f1': 0.6795265234546252, 'number': 1065}   | 0.5867            | 0.6653         | 0.6236     | 0.7396           |
+| 0.7085        | 5.0   | 50   | 0.7531          | {'precision': 0.6281628162816282, 'recall': 0.7058096415327565, 'f1': 0.6647264260768335, 'number': 809}       | {'precision': 0.16, 'recall': 0.10084033613445378, 'f1': 0.12371134020618556, 'number': 119}                | {'precision': 0.6728110599078341, 'recall': 0.8225352112676056, 'f1': 0.7401774397972117, 'number': 1065}   | 0.6382            | 0.7321         | 0.6819     | 0.7708           |
+| 0.6016        | 6.0   | 60   | 0.7178          | {'precision': 0.6607515657620042, 'recall': 0.7824474660074165, 'f1': 0.7164685908319186, 'number': 809}       | {'precision': 0.2235294117647059, 'recall': 0.15966386554621848, 'f1': 0.18627450980392157, 'number': 119}  | {'precision': 0.7422145328719724, 'recall': 0.8056338028169014, 'f1': 0.7726249437190456, 'number': 1065}   | 0.6867            | 0.7577         | 0.7204     | 0.7819           |
+| 0.5255        | 7.0   | 70   | 0.6773          | {'precision': 0.6886688668866887, 'recall': 0.7737948084054388, 'f1': 0.7287543655413271, 'number': 809}       | {'precision': 0.3235294117647059, 'recall': 0.2773109243697479, 'f1': 0.2986425339366516, 'number': 119}    | {'precision': 0.748932536293766, 'recall': 0.8234741784037559, 'f1': 0.7844364937388193, 'number': 1065}    | 0.7039            | 0.7707         | 0.7358     | 0.7985           |
+| 0.4664        | 8.0   | 80   | 0.6865          | {'precision': 0.6846652267818575, 'recall': 0.7836835599505563, 'f1': 0.730835734870317, 'number': 809}        | {'precision': 0.24299065420560748, 'recall': 0.2184873949579832, 'f1': 0.2300884955752212, 'number': 119}   | {'precision': 0.7593397046046916, 'recall': 0.8206572769953052, 'f1': 0.7888086642599278, 'number': 1065}   | 0.7024            | 0.7697         | 0.7345     | 0.7950           |
+| 0.4092        | 9.0   | 90   | 0.6843          | {'precision': 0.6929046563192904, 'recall': 0.7725587144622992, 'f1': 0.7305669199298657, 'number': 809}       | {'precision': 0.3050847457627119, 'recall': 0.3025210084033613, 'f1': 0.3037974683544304, 'number': 119}    | {'precision': 0.7587085811384877, 'recall': 0.8384976525821596, 'f1': 0.7966101694915255, 'number': 1065}   | 0.7073            | 0.7797         | 0.7418     | 0.8013           |
+| 0.4007        | 10.0  | 100  | 0.6826          | {'precision': 0.6887921653971708, 'recall': 0.7824474660074165, 'f1': 0.7326388888888888, 'number': 809}       | {'precision': 0.3142857142857143, 'recall': 0.2773109243697479, 'f1': 0.29464285714285715, 'number': 119}   | {'precision': 0.7761578044596913, 'recall': 0.8497652582159625, 'f1': 0.811295383236217, 'number': 1065}    | 0.7174            | 0.7883         | 0.7511     | 0.8001           |
+| 0.3396        | 11.0  | 110  | 0.6904          | {'precision': 0.6922246220302376, 'recall': 0.792336217552534, 'f1': 0.7389048991354467, 'number': 809}        | {'precision': 0.32231404958677684, 'recall': 0.3277310924369748, 'f1': 0.32499999999999996, 'number': 119}  | {'precision': 0.7778745644599303, 'recall': 0.8384976525821596, 'f1': 0.8070492544057841, 'number': 1065}   | 0.7166            | 0.7893         | 0.7512     | 0.8036           |
+| 0.3223        | 12.0  | 120  | 0.7032          | {'precision': 0.7138084632516704, 'recall': 0.792336217552534, 'f1': 0.7510251903925014, 'number': 809}        | {'precision': 0.3669724770642202, 'recall': 0.33613445378151263, 'f1': 0.3508771929824562, 'number': 119}   | {'precision': 0.788546255506608, 'recall': 0.8403755868544601, 'f1': 0.8136363636363636, 'number': 1065}    | 0.7358            | 0.7908         | 0.7623     | 0.8012           |
+| 0.3079        | 13.0  | 130  | 0.7098          | {'precision': 0.6950431034482759, 'recall': 0.7972805933250927, 'f1': 0.7426597582037997, 'number': 809}       | {'precision': 0.3652173913043478, 'recall': 0.35294117647058826, 'f1': 0.35897435897435903, 'number': 119}  | {'precision': 0.7906360424028268, 'recall': 0.8403755868544601, 'f1': 0.81474738279472, 'number': 1065}     | 0.7274            | 0.7938         | 0.7591     | 0.8027           |
+| 0.2866        | 14.0  | 140  | 0.7096          | {'precision': 0.7103218645948945, 'recall': 0.7911001236093943, 'f1': 0.7485380116959064, 'number': 809}       | {'precision': 0.36065573770491804, 'recall': 0.3697478991596639, 'f1': 0.36514522821576767, 'number': 119}  | {'precision': 0.787719298245614, 'recall': 0.8431924882629108, 'f1': 0.8145124716553288, 'number': 1065}    | 0.7314            | 0.7938         | 0.7613     | 0.8007           |
+| 0.2847        | 15.0  | 150  | 0.7099          | {'precision': 0.7126948775055679, 'recall': 0.7911001236093943, 'f1': 0.7498535442296427, 'number': 809}       | {'precision': 0.3793103448275862, 'recall': 0.3697478991596639, 'f1': 0.374468085106383, 'number': 119}     | {'precision': 0.7863397548161121, 'recall': 0.8431924882629108, 'f1': 0.813774354327141, 'number': 1065}    | 0.7338            | 0.7938         | 0.7626     | 0.8008           |
+
+
+### Framework versions
+
+- Transformers 4.41.2
+- Pytorch 2.3.0+cu121
+- Datasets 2.20.0
+- Tokenizers 0.19.1

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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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+}