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: 0.7211
+- Answer: {'precision': 0.7268722466960352, 'recall': 0.8158220024721878, 'f1': 0.7687827606290041, 'number': 809}
+- Header: {'precision': 0.31343283582089554, 'recall': 0.35294117647058826, 'f1': 0.3320158102766798, 'number': 119}
+- Question: {'precision': 0.7878521126760564, 'recall': 0.8403755868544601, 'f1': 0.8132666969559291, 'number': 1065}
+- Overall Precision: 0.7332
+- Overall Recall: 0.8013
+- Overall F1: 0.7658
+- Overall Accuracy: 0.7963
+
+## 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 |
+|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.7901        | 1.0   | 10   | 1.5938          | {'precision': 0.017361111111111112, 'recall': 0.012360939431396786, 'f1': 0.014440433212996389, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.32525252525252524, 'recall': 0.1511737089201878, 'f1': 0.20641025641025643, 'number': 1065} | 0.1597            | 0.0858         | 0.1116     | 0.3415           |
+| 1.4447        | 2.0   | 20   | 1.2469          | {'precision': 0.22497522299306244, 'recall': 0.28059332509270707, 'f1': 0.24972497249724973, 'number': 809}    | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.4132890365448505, 'recall': 0.584037558685446, 'f1': 0.4840466926070039, 'number': 1065}    | 0.3377            | 0.4260         | 0.3767     | 0.5933           |
+| 1.0816        | 3.0   | 30   | 0.9331          | {'precision': 0.5004995004995005, 'recall': 0.619283065512979, 'f1': 0.5535911602209945, 'number': 809}        | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.5584415584415584, 'recall': 0.7267605633802817, 'f1': 0.631578947368421, 'number': 1065}    | 0.5260            | 0.6397         | 0.5773     | 0.7095           |
+| 0.8262        | 4.0   | 40   | 0.7964          | {'precision': 0.5772357723577236, 'recall': 0.7021013597033374, 'f1': 0.633575013943112, 'number': 809}        | {'precision': 0.15625, 'recall': 0.08403361344537816, 'f1': 0.10928961748633881, 'number': 119}             | {'precision': 0.6492659053833605, 'recall': 0.7474178403755869, 'f1': 0.6948930597992143, 'number': 1065}   | 0.6042            | 0.6894         | 0.6440     | 0.7450           |
+| 0.6674        | 5.0   | 50   | 0.7441          | {'precision': 0.6445182724252492, 'recall': 0.7194066749072929, 'f1': 0.6799065420560747, 'number': 809}       | {'precision': 0.22105263157894736, 'recall': 0.17647058823529413, 'f1': 0.19626168224299065, 'number': 119} | {'precision': 0.6424870466321243, 'recall': 0.8150234741784037, 'f1': 0.7185430463576157, 'number': 1065}   | 0.6262            | 0.7381         | 0.6776     | 0.7673           |
+| 0.5736        | 6.0   | 60   | 0.7005          | {'precision': 0.6451942740286298, 'recall': 0.7799752781211372, 'f1': 0.7062115277000558, 'number': 809}       | {'precision': 0.20454545454545456, 'recall': 0.15126050420168066, 'f1': 0.17391304347826086, 'number': 119} | {'precision': 0.7412891986062717, 'recall': 0.7990610328638498, 'f1': 0.7690917306823317, 'number': 1065}   | 0.6775            | 0.7526         | 0.7131     | 0.7755           |
+| 0.5042        | 7.0   | 70   | 0.6801          | {'precision': 0.6768743400211193, 'recall': 0.792336217552534, 'f1': 0.7300683371298405, 'number': 809}        | {'precision': 0.22018348623853212, 'recall': 0.20168067226890757, 'f1': 0.21052631578947367, 'number': 119} | {'precision': 0.7412765957446809, 'recall': 0.8178403755868544, 'f1': 0.7776785714285714, 'number': 1065}   | 0.6885            | 0.7707         | 0.7273     | 0.7841           |
+| 0.4479        | 8.0   | 80   | 0.6712          | {'precision': 0.6687565308254964, 'recall': 0.7911001236093943, 'f1': 0.7248018120045301, 'number': 809}       | {'precision': 0.20610687022900764, 'recall': 0.226890756302521, 'f1': 0.21600000000000003, 'number': 119}   | {'precision': 0.7404006677796328, 'recall': 0.8328638497652582, 'f1': 0.7839151568714097, 'number': 1065}   | 0.6798            | 0.7797         | 0.7263     | 0.7900           |
+| 0.3931        | 9.0   | 90   | 0.6806          | {'precision': 0.7054263565891473, 'recall': 0.7873918417799752, 'f1': 0.7441588785046728, 'number': 809}       | {'precision': 0.2809917355371901, 'recall': 0.2857142857142857, 'f1': 0.2833333333333333, 'number': 119}    | {'precision': 0.7510620220900595, 'recall': 0.8300469483568075, 'f1': 0.7885816235504014, 'number': 1065}   | 0.7065            | 0.7802         | 0.7415     | 0.7955           |
+| 0.3875        | 10.0  | 100  | 0.6819          | {'precision': 0.7014767932489452, 'recall': 0.8220024721878862, 'f1': 0.7569721115537849, 'number': 809}       | {'precision': 0.3, 'recall': 0.3025210084033613, 'f1': 0.301255230125523, 'number': 119}                    | {'precision': 0.7633851468048359, 'recall': 0.8300469483568075, 'f1': 0.7953216374269007, 'number': 1065}   | 0.7120            | 0.7953         | 0.7514     | 0.7952           |
+| 0.3309        | 11.0  | 110  | 0.7016          | {'precision': 0.7204419889502762, 'recall': 0.8059332509270705, 'f1': 0.7607934655775962, 'number': 809}       | {'precision': 0.2949640287769784, 'recall': 0.3445378151260504, 'f1': 0.31782945736434104, 'number': 119}   | {'precision': 0.7535864978902953, 'recall': 0.8384976525821596, 'f1': 0.7937777777777778, 'number': 1065}   | 0.7115            | 0.7958         | 0.7513     | 0.7969           |
+| 0.3142        | 12.0  | 120  | 0.7081          | {'precision': 0.7178924259055982, 'recall': 0.8084054388133498, 'f1': 0.7604651162790698, 'number': 809}       | {'precision': 0.31007751937984496, 'recall': 0.33613445378151263, 'f1': 0.3225806451612903, 'number': 119}  | {'precision': 0.7768014059753954, 'recall': 0.8300469483568075, 'f1': 0.8025419881979118, 'number': 1065}   | 0.7245            | 0.7918         | 0.7567     | 0.7993           |
+| 0.2992        | 13.0  | 130  | 0.7160          | {'precision': 0.716304347826087, 'recall': 0.8145859085290482, 'f1': 0.7622903412377097, 'number': 809}        | {'precision': 0.304, 'recall': 0.31932773109243695, 'f1': 0.31147540983606553, 'number': 119}               | {'precision': 0.7796167247386759, 'recall': 0.8403755868544601, 'f1': 0.8088567555354722, 'number': 1065}   | 0.7259            | 0.7988         | 0.7606     | 0.7938           |
+| 0.2746        | 14.0  | 140  | 0.7194          | {'precision': 0.7238723872387238, 'recall': 0.8133498145859085, 'f1': 0.7660069848661233, 'number': 809}       | {'precision': 0.32061068702290074, 'recall': 0.35294117647058826, 'f1': 0.336, 'number': 119}               | {'precision': 0.7859030837004405, 'recall': 0.8375586854460094, 'f1': 0.8109090909090909, 'number': 1065}   | 0.7320            | 0.7988         | 0.7639     | 0.7957           |
+| 0.2735        | 15.0  | 150  | 0.7211          | {'precision': 0.7268722466960352, 'recall': 0.8158220024721878, 'f1': 0.7687827606290041, 'number': 809}       | {'precision': 0.31343283582089554, 'recall': 0.35294117647058826, 'f1': 0.3320158102766798, 'number': 119}  | {'precision': 0.7878521126760564, 'recall': 0.8403755868544601, 'f1': 0.8132666969559291, 'number': 1065}   | 0.7332            | 0.8013         | 0.7658     | 0.7963           |
+
+
+### Framework versions
+
+- Transformers 4.41.2
+- Pytorch 2.3.0+cu121
+- Datasets 2.19.1
+- 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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+}