End of training

README.md

@@ -0,0 +1,79 @@
+---
+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: 0.6866
+- Answer: {'precision': 0.7205720572057206, 'recall': 0.8096415327564895, 'f1': 0.7625145518044238, 'number': 809}
+- Header: {'precision': 0.3181818181818182, 'recall': 0.35294117647058826, 'f1': 0.3346613545816733, 'number': 119}
+- Question: {'precision': 0.7839506172839507, 'recall': 0.8347417840375587, 'f1': 0.8085493406093679, 'number': 1065}
+- Overall Precision: 0.7292
+- Overall Recall: 0.7958
+- Overall F1: 0.7610
+- Overall Accuracy: 0.8048
+
+## 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 |
+|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.8292        | 1.0   | 10   | 1.6076          | {'precision': 0.014943960149439602, 'recall': 0.014833127317676144, 'f1': 0.01488833746898263, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                | {'precision': 0.2132564841498559, 'recall': 0.13896713615023473, 'f1': 0.16827743035815804, 'number': 1065} | 0.1069            | 0.0803         | 0.0917     | 0.3499           |
+| 1.4992        | 2.0   | 20   | 1.2649          | {'precision': 0.15553121577217963, 'recall': 0.17552533992583436, 'f1': 0.16492450638792103, 'number': 809}   | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                | {'precision': 0.45795107033639143, 'recall': 0.5624413145539906, 'f1': 0.5048461862621155, 'number': 1065}  | 0.3336            | 0.3718         | 0.3517     | 0.5779           |
+| 1.127         | 3.0   | 30   | 0.9502          | {'precision': 0.4658981748318924, 'recall': 0.5995055624227441, 'f1': 0.5243243243243242, 'number': 809}      | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                | {'precision': 0.6047904191616766, 'recall': 0.6638497652582159, 'f1': 0.6329453894359892, 'number': 1065}   | 0.5379            | 0.5981         | 0.5664     | 0.6835           |
+| 0.8468        | 4.0   | 40   | 0.8052          | {'precision': 0.5737240075614367, 'recall': 0.7503090234857849, 'f1': 0.6502410283877879, 'number': 809}      | {'precision': 0.15, 'recall': 0.05042016806722689, 'f1': 0.07547169811320754, 'number': 119}               | {'precision': 0.672231985940246, 'recall': 0.7183098591549296, 'f1': 0.6945074897866546, 'number': 1065}    | 0.6163            | 0.6914         | 0.6517     | 0.7343           |
+| 0.706         | 5.0   | 50   | 0.7191          | {'precision': 0.6361655773420479, 'recall': 0.7218788627935723, 'f1': 0.6763173132599883, 'number': 809}      | {'precision': 0.18055555555555555, 'recall': 0.1092436974789916, 'f1': 0.13612565445026178, 'number': 119} | {'precision': 0.6922435362802335, 'recall': 0.7793427230046949, 'f1': 0.7332155477031801, 'number': 1065}   | 0.6519            | 0.7160         | 0.6824     | 0.7683           |
+| 0.5832        | 6.0   | 60   | 0.6846          | {'precision': 0.6547231270358306, 'recall': 0.7453646477132262, 'f1': 0.6971098265895953, 'number': 809}      | {'precision': 0.21951219512195122, 'recall': 0.15126050420168066, 'f1': 0.1791044776119403, 'number': 119} | {'precision': 0.6936866718628215, 'recall': 0.8356807511737089, 'f1': 0.75809199318569, 'number': 1065}     | 0.6610            | 0.7582         | 0.7062     | 0.7898           |
+| 0.5197        | 7.0   | 70   | 0.6586          | {'precision': 0.6821621621621622, 'recall': 0.7799752781211372, 'f1': 0.7277970011534025, 'number': 809}      | {'precision': 0.23469387755102042, 'recall': 0.19327731092436976, 'f1': 0.2119815668202765, 'number': 119} | {'precision': 0.7302363488182559, 'recall': 0.8413145539906103, 'f1': 0.7818499127399652, 'number': 1065}   | 0.6889            | 0.7777         | 0.7306     | 0.7931           |
+| 0.4622        | 8.0   | 80   | 0.6479          | {'precision': 0.6830601092896175, 'recall': 0.7725587144622992, 'f1': 0.7250580046403712, 'number': 809}      | {'precision': 0.2540983606557377, 'recall': 0.2605042016806723, 'f1': 0.2572614107883818, 'number': 119}   | {'precision': 0.7464553794829024, 'recall': 0.8403755868544601, 'f1': 0.7906360424028268, 'number': 1065}   | 0.6936            | 0.7782         | 0.7335     | 0.7983           |
+| 0.4162        | 9.0   | 90   | 0.6500          | {'precision': 0.6911447084233261, 'recall': 0.7911001236093943, 'f1': 0.7377521613832853, 'number': 809}      | {'precision': 0.3008130081300813, 'recall': 0.31092436974789917, 'f1': 0.3057851239669422, 'number': 119}  | {'precision': 0.7659574468085106, 'recall': 0.8450704225352113, 'f1': 0.8035714285714286, 'number': 1065}   | 0.7091            | 0.7913         | 0.7479     | 0.8010           |
+| 0.3644        | 10.0  | 100  | 0.6503          | {'precision': 0.7041484716157205, 'recall': 0.7972805933250927, 'f1': 0.7478260869565218, 'number': 809}      | {'precision': 0.3247863247863248, 'recall': 0.31932773109243695, 'f1': 0.3220338983050848, 'number': 119}  | {'precision': 0.7757885763000852, 'recall': 0.8544600938967136, 'f1': 0.8132260947274352, 'number': 1065}   | 0.7221            | 0.7993         | 0.7588     | 0.8078           |
+| 0.3255        | 11.0  | 110  | 0.6716          | {'precision': 0.7108953613807982, 'recall': 0.8145859085290482, 'f1': 0.7592165898617511, 'number': 809}      | {'precision': 0.3333333333333333, 'recall': 0.3277310924369748, 'f1': 0.3305084745762712, 'number': 119}   | {'precision': 0.7852112676056338, 'recall': 0.8375586854460094, 'f1': 0.8105406633348478, 'number': 1065}   | 0.7294            | 0.7978         | 0.7620     | 0.8026           |
+| 0.3154        | 12.0  | 120  | 0.6760          | {'precision': 0.7207505518763797, 'recall': 0.8071693448702101, 'f1': 0.7615160349854228, 'number': 809}      | {'precision': 0.3140495867768595, 'recall': 0.31932773109243695, 'f1': 0.31666666666666665, 'number': 119} | {'precision': 0.7812773403324584, 'recall': 0.8384976525821596, 'f1': 0.8088768115942028, 'number': 1065}   | 0.7300            | 0.7948         | 0.7610     | 0.8039           |
+| 0.2872        | 13.0  | 130  | 0.6777          | {'precision': 0.7232635060639471, 'recall': 0.8108776266996292, 'f1': 0.7645687645687645, 'number': 809}      | {'precision': 0.31451612903225806, 'recall': 0.3277310924369748, 'f1': 0.32098765432098764, 'number': 119} | {'precision': 0.7841601392515231, 'recall': 0.8460093896713615, 'f1': 0.8139114724480578, 'number': 1065}   | 0.7321            | 0.8008         | 0.7649     | 0.8055           |
+| 0.2775        | 14.0  | 140  | 0.6824          | {'precision': 0.7250821467688937, 'recall': 0.8182941903584673, 'f1': 0.7688734030197445, 'number': 809}      | {'precision': 0.31007751937984496, 'recall': 0.33613445378151263, 'f1': 0.3225806451612903, 'number': 119} | {'precision': 0.7839506172839507, 'recall': 0.8347417840375587, 'f1': 0.8085493406093679, 'number': 1065}   | 0.7312            | 0.7983         | 0.7633     | 0.8048           |
+| 0.2744        | 15.0  | 150  | 0.6866          | {'precision': 0.7205720572057206, 'recall': 0.8096415327564895, 'f1': 0.7625145518044238, 'number': 809}      | {'precision': 0.3181818181818182, 'recall': 0.35294117647058826, 'f1': 0.3346613545816733, 'number': 119}  | {'precision': 0.7839506172839507, 'recall': 0.8347417840375587, 'f1': 0.8085493406093679, 'number': 1065}   | 0.7292            | 0.7958         | 0.7610     | 0.8048           |
+
+
+### Framework versions
+
+- Transformers 4.29.2
+- Pytorch 2.0.1+cu118
+- Datasets 2.12.0
+- Tokenizers 0.13.3

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preprocessor_config.json

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