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
+  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: 0.7276
+- Answer: {'precision': 0.7205240174672489, 'recall': 0.8158220024721878, 'f1': 0.7652173913043478, 'number': 809}
+- Header: {'precision': 0.2903225806451613, 'recall': 0.3025210084033613, 'f1': 0.2962962962962963, 'number': 119}
+- Question: {'precision': 0.7903508771929825, 'recall': 0.8460093896713615, 'f1': 0.8172335600907029, 'number': 1065}
+- Overall Precision: 0.7326
+- Overall Recall: 0.8013
+- Overall F1: 0.7654
+- Overall Accuracy: 0.8111
+
+## 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 |
+|:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.735         | 1.0   | 10   | 1.5211          | {'precision': 0.04572098475967175, 'recall': 0.048207663782447466, 'f1': 0.04693140794223826, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.2578616352201258, 'recall': 0.26948356807511736, 'f1': 0.26354453627180896, 'number': 1065} | 0.1658            | 0.1636         | 0.1647     | 0.4315           |
+| 1.353         | 2.0   | 20   | 1.1828          | {'precision': 0.18625954198473282, 'recall': 0.1508034610630408, 'f1': 0.16666666666666669, 'number': 809}   | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.48657445077298617, 'recall': 0.5615023474178403, 'f1': 0.5213600697471664, 'number': 1065}  | 0.3808            | 0.3613         | 0.3708     | 0.5930           |
+| 1.027         | 3.0   | 30   | 0.9100          | {'precision': 0.53, 'recall': 0.5241038318912238, 'f1': 0.5270354257302672, 'number': 809}                   | {'precision': 0.16981132075471697, 'recall': 0.07563025210084033, 'f1': 0.10465116279069768, 'number': 119} | {'precision': 0.6330434782608696, 'recall': 0.6835680751173709, 'f1': 0.6573363431151242, 'number': 1065}   | 0.5796            | 0.5825         | 0.5811     | 0.7286           |
+| 0.7694        | 4.0   | 40   | 0.7622          | {'precision': 0.6295546558704453, 'recall': 0.7688504326328801, 'f1': 0.6922648859209795, 'number': 809}     | {'precision': 0.22727272727272727, 'recall': 0.12605042016806722, 'f1': 0.16216216216216214, 'number': 119} | {'precision': 0.704, 'recall': 0.7436619718309859, 'f1': 0.7232876712328766, 'number': 1065}                | 0.6558            | 0.7170         | 0.6850     | 0.7708           |
+| 0.6233        | 5.0   | 50   | 0.7113          | {'precision': 0.6527196652719666, 'recall': 0.7713226205191595, 'f1': 0.7070821529745043, 'number': 809}     | {'precision': 0.26136363636363635, 'recall': 0.19327731092436976, 'f1': 0.22222222222222224, 'number': 119} | {'precision': 0.6977309562398704, 'recall': 0.8084507042253521, 'f1': 0.7490213136146151, 'number': 1065}   | 0.6620            | 0.7566         | 0.7062     | 0.7895           |
+| 0.531         | 6.0   | 60   | 0.6976          | {'precision': 0.6386138613861386, 'recall': 0.7972805933250927, 'f1': 0.709180868609126, 'number': 809}      | {'precision': 0.22972972972972974, 'recall': 0.14285714285714285, 'f1': 0.17616580310880825, 'number': 119} | {'precision': 0.7175572519083969, 'recall': 0.7943661971830986, 'f1': 0.7540106951871658, 'number': 1065}   | 0.6664            | 0.7566         | 0.7086     | 0.7866           |
+| 0.4577        | 7.0   | 70   | 0.6823          | {'precision': 0.675531914893617, 'recall': 0.7849196538936959, 'f1': 0.7261292166952545, 'number': 809}      | {'precision': 0.21951219512195122, 'recall': 0.226890756302521, 'f1': 0.2231404958677686, 'number': 119}    | {'precision': 0.7434819175777965, 'recall': 0.8300469483568075, 'f1': 0.7843833185448092, 'number': 1065}   | 0.6865            | 0.7757         | 0.7284     | 0.8001           |
+| 0.3982        | 8.0   | 80   | 0.6871          | {'precision': 0.6847710330138446, 'recall': 0.7948084054388134, 'f1': 0.7356979405034326, 'number': 809}     | {'precision': 0.2621359223300971, 'recall': 0.226890756302521, 'f1': 0.24324324324324326, 'number': 119}    | {'precision': 0.7569386038687973, 'recall': 0.8450704225352113, 'f1': 0.7985803016858917, 'number': 1065}   | 0.7037            | 0.7878         | 0.7434     | 0.8091           |
+| 0.3614        | 9.0   | 90   | 0.6850          | {'precision': 0.7039045553145337, 'recall': 0.8022249690976514, 'f1': 0.7498555748122473, 'number': 809}     | {'precision': 0.2692307692307692, 'recall': 0.23529411764705882, 'f1': 0.25112107623318386, 'number': 119}  | {'precision': 0.7635593220338983, 'recall': 0.8460093896713615, 'f1': 0.8026726057906458, 'number': 1065}   | 0.7153            | 0.7918         | 0.7516     | 0.8101           |
+| 0.354         | 10.0  | 100  | 0.6937          | {'precision': 0.7171270718232045, 'recall': 0.8022249690976514, 'f1': 0.7572928821470245, 'number': 809}     | {'precision': 0.30275229357798167, 'recall': 0.2773109243697479, 'f1': 0.28947368421052627, 'number': 119}  | {'precision': 0.7840616966580977, 'recall': 0.8591549295774648, 'f1': 0.8198924731182796, 'number': 1065}   | 0.7322            | 0.8013         | 0.7652     | 0.8140           |
+| 0.2994        | 11.0  | 110  | 0.7161          | {'precision': 0.7063236870310825, 'recall': 0.8145859085290482, 'f1': 0.7566016073478761, 'number': 809}     | {'precision': 0.2631578947368421, 'recall': 0.29411764705882354, 'f1': 0.27777777777777773, 'number': 119}  | {'precision': 0.7885816235504014, 'recall': 0.8300469483568075, 'f1': 0.808783165599268, 'number': 1065}    | 0.7215            | 0.7918         | 0.7550     | 0.8067           |
+| 0.2908        | 12.0  | 120  | 0.7068          | {'precision': 0.7208287895310797, 'recall': 0.8170580964153276, 'f1': 0.7659327925840093, 'number': 809}     | {'precision': 0.3, 'recall': 0.2773109243697479, 'f1': 0.28820960698689957, 'number': 119}                  | {'precision': 0.7865266841644795, 'recall': 0.844131455399061, 'f1': 0.8143115942028986, 'number': 1065}    | 0.7341            | 0.7993         | 0.7653     | 0.8134           |
+| 0.2689        | 13.0  | 130  | 0.7206          | {'precision': 0.7186477644492911, 'recall': 0.8145859085290482, 'f1': 0.7636152954808806, 'number': 809}     | {'precision': 0.272, 'recall': 0.2857142857142857, 'f1': 0.27868852459016397, 'number': 119}                | {'precision': 0.7954345917471466, 'recall': 0.8507042253521127, 'f1': 0.822141560798548, 'number': 1065}    | 0.7331            | 0.8023         | 0.7662     | 0.8120           |
+| 0.2527        | 14.0  | 140  | 0.7260          | {'precision': 0.724972497249725, 'recall': 0.8145859085290482, 'f1': 0.7671711292200234, 'number': 809}      | {'precision': 0.2892561983471074, 'recall': 0.29411764705882354, 'f1': 0.2916666666666667, 'number': 119}   | {'precision': 0.7900696864111498, 'recall': 0.8516431924882629, 'f1': 0.8197017623136014, 'number': 1065}   | 0.7351            | 0.8033         | 0.7677     | 0.8104           |
+| 0.2511        | 15.0  | 150  | 0.7276          | {'precision': 0.7205240174672489, 'recall': 0.8158220024721878, 'f1': 0.7652173913043478, 'number': 809}     | {'precision': 0.2903225806451613, 'recall': 0.3025210084033613, 'f1': 0.2962962962962963, 'number': 119}    | {'precision': 0.7903508771929825, 'recall': 0.8460093896713615, 'f1': 0.8172335600907029, 'number': 1065}   | 0.7326            | 0.8013         | 0.7654     | 0.8111           |
+
+
+### Framework versions
+
+- Transformers 4.46.3
+- Pytorch 2.5.1+cu121
+- Datasets 3.1.0
+- Tokenizers 0.20.3

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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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