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--- |
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base_model: microsoft/layoutlm-base-uncased |
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tags: |
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- generated_from_trainer |
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datasets: |
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- funsd |
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model-index: |
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- name: layoutlm-funsd |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# layoutlm-funsd |
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This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the funsd dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.8174 |
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- Answer: {'precision': 0.7233333333333334, 'recall': 0.8046971569839307, 'f1': 0.7618490345231129, 'number': 809} |
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- Header: {'precision': 0.35766423357664234, 'recall': 0.4117647058823529, 'f1': 0.3828125, 'number': 119} |
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- Question: {'precision': 0.7904085257548845, 'recall': 0.8356807511737089, 'f1': 0.8124144226380648, 'number': 1065} |
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- Overall Precision: 0.7351 |
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- Overall Recall: 0.7978 |
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- Overall F1: 0.7652 |
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- Overall Accuracy: 0.8019 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 3e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 20 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:----------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:| |
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| 1.3435 | 1.0 | 10 | 1.1455 | {'precision': 0.29554655870445345, 'recall': 0.27070457354758964, 'f1': 0.2825806451612903, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.43828125, 'recall': 0.5267605633802817, 'f1': 0.47846481876332625, 'number': 1065} | 0.3858 | 0.3914 | 0.3885 | 0.6180 | |
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| 0.9706 | 2.0 | 20 | 0.8933 | {'precision': 0.5545454545454546, 'recall': 0.6786155747836835, 'f1': 0.6103390772651472, 'number': 809} | {'precision': 0.08695652173913043, 'recall': 0.03361344537815126, 'f1': 0.048484848484848485, 'number': 119} | {'precision': 0.6115916955017301, 'recall': 0.6638497652582159, 'f1': 0.6366501575866726, 'number': 1065} | 0.5748 | 0.6322 | 0.6022 | 0.7308 | |
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| 0.7426 | 3.0 | 30 | 0.7478 | {'precision': 0.6294058408862034, 'recall': 0.7725587144622992, 'f1': 0.6936736958934517, 'number': 809} | {'precision': 0.1891891891891892, 'recall': 0.11764705882352941, 'f1': 0.1450777202072539, 'number': 119} | {'precision': 0.6858333333333333, 'recall': 0.7727699530516432, 'f1': 0.726710816777042, 'number': 1065} | 0.6449 | 0.7336 | 0.6864 | 0.7770 | |
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| 0.6123 | 4.0 | 40 | 0.6950 | {'precision': 0.6286266924564797, 'recall': 0.8034610630407911, 'f1': 0.705371676614216, 'number': 809} | {'precision': 0.19387755102040816, 'recall': 0.15966386554621848, 'f1': 0.17511520737327188, 'number': 119} | {'precision': 0.6943268416596104, 'recall': 0.7699530516431925, 'f1': 0.730186999109528, 'number': 1065} | 0.6438 | 0.7471 | 0.6916 | 0.7886 | |
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| 0.5267 | 5.0 | 50 | 0.6804 | {'precision': 0.6574172892209178, 'recall': 0.761433868974042, 'f1': 0.7056128293241695, 'number': 809} | {'precision': 0.21818181818181817, 'recall': 0.20168067226890757, 'f1': 0.2096069868995633, 'number': 119} | {'precision': 0.7246496290189612, 'recall': 0.8253521126760563, 'f1': 0.771729587357331, 'number': 1065} | 0.6721 | 0.7622 | 0.7143 | 0.8013 | |
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| 0.4587 | 6.0 | 60 | 0.6701 | {'precision': 0.670490093847758, 'recall': 0.7948084054388134, 'f1': 0.7273755656108597, 'number': 809} | {'precision': 0.2108843537414966, 'recall': 0.2605042016806723, 'f1': 0.2330827067669173, 'number': 119} | {'precision': 0.7309602649006622, 'recall': 0.8291079812206573, 'f1': 0.7769467663880335, 'number': 1065} | 0.6729 | 0.7812 | 0.7230 | 0.7977 | |
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| 0.3981 | 7.0 | 70 | 0.6637 | {'precision': 0.7029063509149623, 'recall': 0.8071693448702101, 'f1': 0.7514384349827388, 'number': 809} | {'precision': 0.2698412698412698, 'recall': 0.2857142857142857, 'f1': 0.27755102040816326, 'number': 119} | {'precision': 0.7621483375959079, 'recall': 0.8394366197183099, 'f1': 0.7989276139410187, 'number': 1065} | 0.7096 | 0.7933 | 0.7491 | 0.8062 | |
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| 0.3608 | 8.0 | 80 | 0.6778 | {'precision': 0.7083333333333334, 'recall': 0.7985166872682324, 'f1': 0.7507263219058687, 'number': 809} | {'precision': 0.25874125874125875, 'recall': 0.31092436974789917, 'f1': 0.2824427480916031, 'number': 119} | {'precision': 0.7633851468048359, 'recall': 0.8300469483568075, 'f1': 0.7953216374269007, 'number': 1065} | 0.7081 | 0.7863 | 0.7451 | 0.8003 | |
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| 0.311 | 9.0 | 90 | 0.6931 | {'precision': 0.6991247264770241, 'recall': 0.7898640296662547, 'f1': 0.7417295414973882, 'number': 809} | {'precision': 0.2835820895522388, 'recall': 0.31932773109243695, 'f1': 0.30039525691699603, 'number': 119} | {'precision': 0.7606244579358196, 'recall': 0.8234741784037559, 'f1': 0.7908025247971145, 'number': 1065} | 0.7060 | 0.7797 | 0.7411 | 0.8055 | |
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| 0.276 | 10.0 | 100 | 0.7144 | {'precision': 0.7298787210584344, 'recall': 0.8182941903584673, 'f1': 0.7715617715617716, 'number': 809} | {'precision': 0.3103448275862069, 'recall': 0.37815126050420167, 'f1': 0.34090909090909094, 'number': 119} | {'precision': 0.7814159292035399, 'recall': 0.8291079812206573, 'f1': 0.8045558086560365, 'number': 1065} | 0.7287 | 0.7978 | 0.7617 | 0.8062 | |
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| 0.2393 | 11.0 | 110 | 0.7342 | {'precision': 0.7155555555555555, 'recall': 0.796044499381953, 'f1': 0.7536571094207138, 'number': 809} | {'precision': 0.296551724137931, 'recall': 0.36134453781512604, 'f1': 0.32575757575757575, 'number': 119} | {'precision': 0.774869109947644, 'recall': 0.8338028169014085, 'f1': 0.8032564450474899, 'number': 1065} | 0.7188 | 0.7903 | 0.7529 | 0.8042 | |
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| 0.2227 | 12.0 | 120 | 0.7539 | {'precision': 0.7054945054945055, 'recall': 0.7935723114956736, 'f1': 0.7469458987783596, 'number': 809} | {'precision': 0.33884297520661155, 'recall': 0.3445378151260504, 'f1': 0.3416666666666667, 'number': 119} | {'precision': 0.7686440677966102, 'recall': 0.8516431924882629, 'f1': 0.8080178173719377, 'number': 1065} | 0.7191 | 0.7978 | 0.7564 | 0.8006 | |
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| 0.2119 | 13.0 | 130 | 0.7774 | {'precision': 0.7263736263736263, 'recall': 0.8170580964153276, 'f1': 0.7690517742873763, 'number': 809} | {'precision': 0.28125, 'recall': 0.37815126050420167, 'f1': 0.3225806451612903, 'number': 119} | {'precision': 0.7714033539276258, 'recall': 0.8206572769953052, 'f1': 0.7952684258416743, 'number': 1065} | 0.7172 | 0.7928 | 0.7531 | 0.7952 | |
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| 0.1882 | 14.0 | 140 | 0.7688 | {'precision': 0.7270668176670442, 'recall': 0.7935723114956736, 'f1': 0.7588652482269503, 'number': 809} | {'precision': 0.3384615384615385, 'recall': 0.3697478991596639, 'f1': 0.35341365461847385, 'number': 119} | {'precision': 0.7883597883597884, 'recall': 0.8394366197183099, 'f1': 0.8130968622100955, 'number': 1065} | 0.7359 | 0.7928 | 0.7633 | 0.8024 | |
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| 0.1767 | 15.0 | 150 | 0.7717 | {'precision': 0.7244785949506037, 'recall': 0.8158220024721878, 'f1': 0.7674418604651163, 'number': 809} | {'precision': 0.3548387096774194, 'recall': 0.3697478991596639, 'f1': 0.36213991769547327, 'number': 119} | {'precision': 0.789612676056338, 'recall': 0.8422535211267606, 'f1': 0.8150840527033166, 'number': 1065} | 0.7374 | 0.8033 | 0.7690 | 0.8020 | |
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| 0.1703 | 16.0 | 160 | 0.7943 | {'precision': 0.7231638418079096, 'recall': 0.7911001236093943, 'f1': 0.755608028335301, 'number': 809} | {'precision': 0.36231884057971014, 'recall': 0.42016806722689076, 'f1': 0.38910505836575876, 'number': 119} | {'precision': 0.79185119574845, 'recall': 0.8394366197183099, 'f1': 0.8149498632634458, 'number': 1065} | 0.7361 | 0.7948 | 0.7643 | 0.8017 | |
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| 0.1643 | 17.0 | 170 | 0.8087 | {'precision': 0.7207207207207207, 'recall': 0.7911001236093943, 'f1': 0.7542722451384797, 'number': 809} | {'precision': 0.33098591549295775, 'recall': 0.3949579831932773, 'f1': 0.3601532567049809, 'number': 119} | {'precision': 0.7932263814616756, 'recall': 0.8356807511737089, 'f1': 0.8139003200731596, 'number': 1065} | 0.7328 | 0.7913 | 0.7609 | 0.7990 | |
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| 0.1443 | 18.0 | 180 | 0.8170 | {'precision': 0.7230419977298524, 'recall': 0.7873918417799752, 'f1': 0.7538461538461538, 'number': 809} | {'precision': 0.36231884057971014, 'recall': 0.42016806722689076, 'f1': 0.38910505836575876, 'number': 119} | {'precision': 0.7898936170212766, 'recall': 0.8366197183098592, 'f1': 0.8125854993160054, 'number': 1065} | 0.7350 | 0.7918 | 0.7623 | 0.7994 | |
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| 0.148 | 19.0 | 190 | 0.8169 | {'precision': 0.7245240761478163, 'recall': 0.799752781211372, 'f1': 0.7602820211515863, 'number': 809} | {'precision': 0.35766423357664234, 'recall': 0.4117647058823529, 'f1': 0.3828125, 'number': 119} | {'precision': 0.792149866190901, 'recall': 0.8338028169014085, 'f1': 0.8124428179322964, 'number': 1065} | 0.7364 | 0.7948 | 0.7645 | 0.8015 | |
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| 0.1441 | 20.0 | 200 | 0.8174 | {'precision': 0.7233333333333334, 'recall': 0.8046971569839307, 'f1': 0.7618490345231129, 'number': 809} | {'precision': 0.35766423357664234, 'recall': 0.4117647058823529, 'f1': 0.3828125, 'number': 119} | {'precision': 0.7904085257548845, 'recall': 0.8356807511737089, 'f1': 0.8124144226380648, 'number': 1065} | 0.7351 | 0.7978 | 0.7652 | 0.8019 | |
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### Framework versions |
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- Transformers 4.34.0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.14.1 |
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