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layoutlm-funsd

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  1. README.md +112 -27
  2. logs/events.out.tfevents.1667810141.real.engr.tamu.edu +2 -2
  3. pytorch_model.bin +1 -1
  4. tokenizer.json +2 -16
  5. tokenizer_config.json +1 -1
README.md CHANGED
@@ -13,16 +13,16 @@ 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: 0.8261
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- - Answer: {'precision': 0.5727482678983834, 'recall': 0.6131025957972805, 'f1': 0.5922388059701492, 'number': 809}
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- - Header: {'precision': 0.09302325581395349, 'recall': 0.03361344537815126, 'f1': 0.04938271604938272, 'number': 119}
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- - Question: {'precision': 0.6384228187919463, 'recall': 0.7145539906103286, 'f1': 0.6743464776251661, 'number': 1065}
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- - Overall Precision: 0.6002
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- - Overall Recall: 0.6327
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- - Overall F1: 0.6160
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- - Overall Accuracy: 0.7523
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  ## Model description
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@@ -47,28 +47,113 @@ The following hyperparameters were used during training:
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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: 15
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  - mixed_precision_training: Native AMP
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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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- | 1.9264 | 1.0 | 3 | 1.7763 | {'precision': 0.011029411764705883, 'recall': 0.022249690976514216, 'f1': 0.01474805407619828, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.09483960948396095, 'recall': 0.12769953051643193, 'f1': 0.108843537414966, 'number': 1065} | 0.0483 | 0.0773 | 0.0595 | 0.3277 |
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- | 1.7361 | 2.0 | 6 | 1.6376 | {'precision': 0.0064754856614246065, 'recall': 0.00865265760197775, 'f1': 0.007407407407407408, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.17735470941883769, 'recall': 0.16619718309859155, 'f1': 0.17159476490547748, 'number': 1065} | 0.0885 | 0.0923 | 0.0904 | 0.3852 |
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- | 1.6212 | 3.0 | 9 | 1.5225 | {'precision': 0.02002002002002002, 'recall': 0.024721878862793572, 'f1': 0.022123893805309734, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.27049180327868855, 'recall': 0.27887323943661974, 'f1': 0.27461858529819694, 'number': 1065} | 0.1512 | 0.1591 | 0.1550 | 0.4422 |
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- | 1.5178 | 4.0 | 12 | 1.4133 | {'precision': 0.05408388520971302, 'recall': 0.06056860321384425, 'f1': 0.05714285714285715, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.313953488372093, 'recall': 0.38028169014084506, 'f1': 0.34394904458598724, 'number': 1065} | 0.2067 | 0.2278 | 0.2168 | 0.5062 |
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- | 1.3853 | 5.0 | 15 | 1.3086 | {'precision': 0.08221024258760108, 'recall': 0.0754017305315204, 'f1': 0.07865892972275951, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.3780202650038971, 'recall': 0.45539906103286387, 'f1': 0.4131175468483816, 'number': 1065} | 0.2696 | 0.2740 | 0.2718 | 0.5453 |
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- | 1.2546 | 6.0 | 18 | 1.2110 | {'precision': 0.1463768115942029, 'recall': 0.12484548825710753, 'f1': 0.13475650433622416, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.4551282051282051, 'recall': 0.5333333333333333, 'f1': 0.49113705144833547, 'number': 1065} | 0.3452 | 0.3357 | 0.3404 | 0.5822 |
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- | 1.1842 | 7.0 | 21 | 1.1217 | {'precision': 0.2563739376770538, 'recall': 0.22373300370828184, 'f1': 0.23894389438943894, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.4908512330946698, 'recall': 0.5793427230046948, 'f1': 0.5314384151593453, 'number': 1065} | 0.4055 | 0.4004 | 0.4029 | 0.6223 |
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- | 1.0564 | 8.0 | 24 | 1.0490 | {'precision': 0.364461738002594, 'recall': 0.3473423980222497, 'f1': 0.3556962025316456, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.509976057462091, 'recall': 0.6, 'f1': 0.551337359792925, 'number': 1065} | 0.4523 | 0.4616 | 0.4569 | 0.6679 |
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- | 0.9865 | 9.0 | 27 | 0.9863 | {'precision': 0.4305555555555556, 'recall': 0.4215080346106304, 'f1': 0.42598376014990635, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.5528726061615321, 'recall': 0.6234741784037559, 'f1': 0.5860547219770521, 'number': 1065} | 0.4978 | 0.5043 | 0.5010 | 0.6986 |
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- | 0.9281 | 10.0 | 30 | 0.9357 | {'precision': 0.49454545454545457, 'recall': 0.5043263288009888, 'f1': 0.49938800489596086, 'number': 809} | {'precision': 0.034482758620689655, 'recall': 0.008403361344537815, 'f1': 0.013513513513513513, 'number': 119} | {'precision': 0.5873287671232876, 'recall': 0.644131455399061, 'f1': 0.6144200626959248, 'number': 1065} | 0.5415 | 0.5494 | 0.5455 | 0.7197 |
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- | 0.8646 | 11.0 | 33 | 0.8968 | {'precision': 0.5333333333333333, 'recall': 0.5438813349814586, 'f1': 0.5385556915544676, 'number': 809} | {'precision': 0.0625, 'recall': 0.01680672268907563, 'f1': 0.026490066225165563, 'number': 119} | {'precision': 0.6031746031746031, 'recall': 0.6779342723004694, 'f1': 0.6383731211317418, 'number': 1065} | 0.5667 | 0.5840 | 0.5752 | 0.7344 |
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- | 0.828 | 12.0 | 36 | 0.8653 | {'precision': 0.5617577197149644, 'recall': 0.584672435105068, 'f1': 0.5729860690490611, 'number': 809} | {'precision': 0.07692307692307693, 'recall': 0.025210084033613446, 'f1': 0.0379746835443038, 'number': 119} | {'precision': 0.6204013377926422, 'recall': 0.6967136150234742, 'f1': 0.6563467492260062, 'number': 1065} | 0.5864 | 0.6111 | 0.5985 | 0.7442 |
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- | 0.7803 | 13.0 | 39 | 0.8442 | {'precision': 0.5667828106852497, 'recall': 0.6032138442521632, 'f1': 0.5844311377245508, 'number': 809} | {'precision': 0.07142857142857142, 'recall': 0.025210084033613446, 'f1': 0.037267080745341616, 'number': 119} | {'precision': 0.6343906510851419, 'recall': 0.7136150234741784, 'f1': 0.6716747680070703, 'number': 1065} | 0.5954 | 0.6277 | 0.6111 | 0.7504 |
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- | 0.771 | 14.0 | 42 | 0.8312 | {'precision': 0.5679723502304147, 'recall': 0.6093943139678616, 'f1': 0.5879546809779368, 'number': 809} | {'precision': 0.09302325581395349, 'recall': 0.03361344537815126, 'f1': 0.04938271604938272, 'number': 119} | {'precision': 0.6376569037656904, 'recall': 0.7154929577464789, 'f1': 0.6743362831858407, 'number': 1065} | 0.5978 | 0.6317 | 0.6143 | 0.7516 |
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- | 0.7843 | 15.0 | 45 | 0.8261 | {'precision': 0.5727482678983834, 'recall': 0.6131025957972805, 'f1': 0.5922388059701492, 'number': 809} | {'precision': 0.09302325581395349, 'recall': 0.03361344537815126, 'f1': 0.04938271604938272, 'number': 119} | {'precision': 0.6384228187919463, 'recall': 0.7145539906103286, 'f1': 0.6743464776251661, 'number': 1065} | 0.6002 | 0.6327 | 0.6160 | 0.7523 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  # layoutlm-funsd
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16
+ This model is a fine-tuned version of [pvbhanuteja/layoutlm-funsd](https://huggingface.co/pvbhanuteja/layoutlm-funsd) on the funsd dataset.
17
  It achieves the following results on the evaluation set:
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+ - Loss: 1.1395
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+ - Answer: {'precision': 0.733934611048478, 'recall': 0.8046971569839307, 'f1': 0.767688679245283, 'number': 809}
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+ - Header: {'precision': 0.4066666666666667, 'recall': 0.5126050420168067, 'f1': 0.4535315985130111, 'number': 119}
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+ - Question: {'precision': 0.7980339588918678, 'recall': 0.8384976525821596, 'f1': 0.8177655677655677, 'number': 1065}
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+ - Overall Precision: 0.7444
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+ - Overall Recall: 0.8053
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+ - Overall F1: 0.7737
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+ - Overall Accuracy: 0.8054
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27
  ## Model description
28
 
 
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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: 100
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  - mixed_precision_training: Native AMP
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53
  ### Training results
54
 
55
+ | 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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+ | 0.7854 | 1.0 | 3 | 0.7718 | {'precision': 0.5876826722338204, 'recall': 0.695920889987639, 'f1': 0.6372382569326542, 'number': 809} | {'precision': 0.1509433962264151, 'recall': 0.06722689075630252, 'f1': 0.09302325581395347, 'number': 119} | {'precision': 0.6840731070496083, 'recall': 0.7380281690140845, 'f1': 0.7100271002710027, 'number': 1065} | 0.6282 | 0.6809 | 0.6535 | 0.7603 |
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+ | 0.6636 | 2.0 | 6 | 0.7427 | {'precision': 0.6303418803418803, 'recall': 0.7292954264524104, 'f1': 0.67621776504298, 'number': 809} | {'precision': 0.2289156626506024, 'recall': 0.15966386554621848, 'f1': 0.18811881188118812, 'number': 119} | {'precision': 0.6728295819935691, 'recall': 0.7859154929577464, 'f1': 0.7249891728020789, 'number': 1065} | 0.6390 | 0.7255 | 0.6795 | 0.7810 |
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+ | 0.5784 | 3.0 | 9 | 0.6996 | {'precision': 0.6334033613445378, 'recall': 0.7453646477132262, 'f1': 0.6848381601362863, 'number': 809} | {'precision': 0.23529411764705882, 'recall': 0.13445378151260504, 'f1': 0.17112299465240638, 'number': 119} | {'precision': 0.705161854768154, 'recall': 0.7568075117370892, 'f1': 0.7300724637681159, 'number': 1065} | 0.6588 | 0.7150 | 0.6858 | 0.7843 |
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+ | 0.5096 | 4.0 | 12 | 0.7029 | {'precision': 0.6794150731158605, 'recall': 0.7466007416563659, 'f1': 0.7114252061248527, 'number': 809} | {'precision': 0.27380952380952384, 'recall': 0.19327731092436976, 'f1': 0.2266009852216749, 'number': 119} | {'precision': 0.7071029529130087, 'recall': 0.831924882629108, 'f1': 0.7644521138912856, 'number': 1065} | 0.6797 | 0.7592 | 0.7172 | 0.7928 |
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+ | 0.4932 | 5.0 | 15 | 0.6843 | {'precision': 0.6684266103484688, 'recall': 0.7824474660074165, 'f1': 0.7209567198177677, 'number': 809} | {'precision': 0.2653061224489796, 'recall': 0.2184873949579832, 'f1': 0.23963133640552997, 'number': 119} | {'precision': 0.7347114556416882, 'recall': 0.8009389671361502, 'f1': 0.7663971248876907, 'number': 1065} | 0.6854 | 0.7587 | 0.7202 | 0.7976 |
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+ | 0.4558 | 6.0 | 18 | 0.6809 | {'precision': 0.6920492721164614, 'recall': 0.7639060568603214, 'f1': 0.7262044653349001, 'number': 809} | {'precision': 0.29245283018867924, 'recall': 0.2605042016806723, 'f1': 0.27555555555555555, 'number': 119} | {'precision': 0.7493495229835212, 'recall': 0.8112676056338028, 'f1': 0.7790802524797115, 'number': 1065} | 0.7031 | 0.7592 | 0.7300 | 0.8022 |
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+ | 0.4103 | 7.0 | 21 | 0.6674 | {'precision': 0.6886486486486486, 'recall': 0.7873918417799752, 'f1': 0.7347174163783161, 'number': 809} | {'precision': 0.3, 'recall': 0.25210084033613445, 'f1': 0.273972602739726, 'number': 119} | {'precision': 0.7483221476510067, 'recall': 0.8375586854460094, 'f1': 0.7904297740363314, 'number': 1065} | 0.7032 | 0.7822 | 0.7406 | 0.8006 |
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+ | 0.3867 | 8.0 | 24 | 0.6610 | {'precision': 0.7100115074798619, 'recall': 0.7626699629171817, 'f1': 0.735399284862932, 'number': 809} | {'precision': 0.30303030303030304, 'recall': 0.33613445378151263, 'f1': 0.3187250996015936, 'number': 119} | {'precision': 0.759825327510917, 'recall': 0.8169014084507042, 'f1': 0.7873303167420814, 'number': 1065} | 0.7116 | 0.7662 | 0.7379 | 0.8094 |
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+ | 0.3632 | 9.0 | 27 | 0.6517 | {'precision': 0.7088465845464725, 'recall': 0.7824474660074165, 'f1': 0.7438307873090482, 'number': 809} | {'precision': 0.29357798165137616, 'recall': 0.2689075630252101, 'f1': 0.28070175438596495, 'number': 119} | {'precision': 0.768695652173913, 'recall': 0.8300469483568075, 'f1': 0.798194130925508, 'number': 1065} | 0.7198 | 0.7772 | 0.7474 | 0.8168 |
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+ | 0.3275 | 10.0 | 30 | 0.6857 | {'precision': 0.7166666666666667, 'recall': 0.7972805933250927, 'f1': 0.7548273844353424, 'number': 809} | {'precision': 0.2695035460992908, 'recall': 0.31932773109243695, 'f1': 0.2923076923076923, 'number': 119} | {'precision': 0.766695576756288, 'recall': 0.8300469483568075, 'f1': 0.7971145175834085, 'number': 1065} | 0.7142 | 0.7863 | 0.7485 | 0.8043 |
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+ | 0.2981 | 11.0 | 33 | 0.6884 | {'precision': 0.7378190255220418, 'recall': 0.7861557478368356, 'f1': 0.7612208258527828, 'number': 809} | {'precision': 0.34959349593495936, 'recall': 0.36134453781512604, 'f1': 0.35537190082644626, 'number': 119} | {'precision': 0.7722513089005235, 'recall': 0.8309859154929577, 'f1': 0.8005427408412483, 'number': 1065} | 0.7339 | 0.7847 | 0.7585 | 0.8067 |
68
+ | 0.2841 | 12.0 | 36 | 0.6951 | {'precision': 0.7202718006795017, 'recall': 0.7861557478368356, 'f1': 0.7517730496453902, 'number': 809} | {'precision': 0.336283185840708, 'recall': 0.31932773109243695, 'f1': 0.32758620689655166, 'number': 119} | {'precision': 0.7684943429068756, 'recall': 0.8291079812206573, 'f1': 0.7976513098464317, 'number': 1065} | 0.7259 | 0.7812 | 0.7525 | 0.8109 |
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+ | 0.2505 | 13.0 | 39 | 0.7074 | {'precision': 0.7242152466367713, 'recall': 0.7985166872682324, 'f1': 0.7595532039976484, 'number': 809} | {'precision': 0.3049645390070922, 'recall': 0.36134453781512604, 'f1': 0.3307692307692308, 'number': 119} | {'precision': 0.7823371989295272, 'recall': 0.8234741784037559, 'f1': 0.8023787740164685, 'number': 1065} | 0.7270 | 0.7858 | 0.7552 | 0.8054 |
70
+ | 0.2416 | 14.0 | 42 | 0.7138 | {'precision': 0.7323462414578588, 'recall': 0.7948084054388134, 'f1': 0.7622999407231773, 'number': 809} | {'precision': 0.328, 'recall': 0.3445378151260504, 'f1': 0.33606557377049184, 'number': 119} | {'precision': 0.7828773168578994, 'recall': 0.8328638497652582, 'f1': 0.8070973612374885, 'number': 1065} | 0.7355 | 0.7883 | 0.7610 | 0.8116 |
71
+ | 0.2138 | 15.0 | 45 | 0.7212 | {'precision': 0.7307692307692307, 'recall': 0.7985166872682324, 'f1': 0.7631423508564678, 'number': 809} | {'precision': 0.3333333333333333, 'recall': 0.35294117647058826, 'f1': 0.34285714285714286, 'number': 119} | {'precision': 0.7901785714285714, 'recall': 0.8309859154929577, 'f1': 0.8100686498855836, 'number': 1065} | 0.7385 | 0.7893 | 0.7630 | 0.8155 |
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+ | 0.2019 | 16.0 | 48 | 0.7338 | {'precision': 0.7417893544733862, 'recall': 0.8096415327564895, 'f1': 0.7742316784869978, 'number': 809} | {'precision': 0.37209302325581395, 'recall': 0.40336134453781514, 'f1': 0.3870967741935484, 'number': 119} | {'precision': 0.7945945945945946, 'recall': 0.828169014084507, 'f1': 0.8110344827586206, 'number': 1065} | 0.7469 | 0.7953 | 0.7704 | 0.8137 |
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+ | 0.1807 | 17.0 | 51 | 0.7435 | {'precision': 0.7295454545454545, 'recall': 0.7935723114956736, 'f1': 0.7602131438721137, 'number': 809} | {'precision': 0.3787878787878788, 'recall': 0.42016806722689076, 'f1': 0.398406374501992, 'number': 119} | {'precision': 0.7898032200357782, 'recall': 0.8291079812206573, 'f1': 0.8089784699954191, 'number': 1065} | 0.7394 | 0.7903 | 0.7640 | 0.8178 |
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+ | 0.173 | 18.0 | 54 | 0.7623 | {'precision': 0.7337807606263982, 'recall': 0.8108776266996292, 'f1': 0.7704051673517321, 'number': 809} | {'precision': 0.37777777777777777, 'recall': 0.42857142857142855, 'f1': 0.40157480314960625, 'number': 119} | {'precision': 0.7865961199294532, 'recall': 0.8375586854460094, 'f1': 0.8112778535698044, 'number': 1065} | 0.7393 | 0.8023 | 0.7695 | 0.8124 |
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+ | 0.1463 | 19.0 | 57 | 0.7833 | {'precision': 0.7365470852017937, 'recall': 0.8121137206427689, 'f1': 0.7724867724867724, 'number': 809} | {'precision': 0.38235294117647056, 'recall': 0.4369747899159664, 'f1': 0.40784313725490196, 'number': 119} | {'precision': 0.7901785714285714, 'recall': 0.8309859154929577, 'f1': 0.8100686498855836, 'number': 1065} | 0.7421 | 0.7998 | 0.7699 | 0.8120 |
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+ | 0.1429 | 20.0 | 60 | 0.7812 | {'precision': 0.7403628117913832, 'recall': 0.8071693448702101, 'f1': 0.7723240685984624, 'number': 809} | {'precision': 0.3770491803278688, 'recall': 0.3865546218487395, 'f1': 0.38174273858921165, 'number': 119} | {'precision': 0.7859054415700267, 'recall': 0.8272300469483568, 'f1': 0.8060384263494967, 'number': 1065} | 0.7435 | 0.7928 | 0.7674 | 0.8153 |
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+ | 0.1263 | 21.0 | 63 | 0.7958 | {'precision': 0.7327293318233296, 'recall': 0.799752781211372, 'f1': 0.764775413711584, 'number': 809} | {'precision': 0.3986013986013986, 'recall': 0.4789915966386555, 'f1': 0.4351145038167939, 'number': 119} | {'precision': 0.8021978021978022, 'recall': 0.8225352112676056, 'f1': 0.812239221140473, 'number': 1065} | 0.7460 | 0.7928 | 0.7687 | 0.8139 |
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+ | 0.1285 | 22.0 | 66 | 0.7985 | {'precision': 0.7421171171171171, 'recall': 0.8145859085290482, 'f1': 0.7766647024160284, 'number': 809} | {'precision': 0.41732283464566927, 'recall': 0.44537815126050423, 'f1': 0.43089430894308944, 'number': 119} | {'precision': 0.7955156950672646, 'recall': 0.8328638497652582, 'f1': 0.8137614678899082, 'number': 1065} | 0.7507 | 0.8023 | 0.7756 | 0.8178 |
79
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+ | 0.0419 | 45.0 | 135 | 1.0164 | {'precision': 0.7400681044267877, 'recall': 0.8059332509270705, 'f1': 0.7715976331360946, 'number': 809} | {'precision': 0.3880597014925373, 'recall': 0.4369747899159664, 'f1': 0.41106719367588934, 'number': 119} | {'precision': 0.8136363636363636, 'recall': 0.8403755868544601, 'f1': 0.8267898383371823, 'number': 1065} | 0.7560 | 0.8023 | 0.7785 | 0.8066 |
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+ | 0.0384 | 46.0 | 138 | 1.0165 | {'precision': 0.7241379310344828, 'recall': 0.8046971569839307, 'f1': 0.7622950819672132, 'number': 809} | {'precision': 0.40939597315436244, 'recall': 0.5126050420168067, 'f1': 0.4552238805970149, 'number': 119} | {'precision': 0.8149171270718232, 'recall': 0.8309859154929577, 'f1': 0.8228730822873082, 'number': 1065} | 0.7484 | 0.8013 | 0.7739 | 0.8054 |
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+ | 0.0284 | 50.0 | 150 | 1.0386 | {'precision': 0.7300884955752213, 'recall': 0.8158220024721878, 'f1': 0.7705779334500875, 'number': 809} | {'precision': 0.4305555555555556, 'recall': 0.5210084033613446, 'f1': 0.47148288973384034, 'number': 119} | {'precision': 0.8151875571820677, 'recall': 0.8366197183098592, 'f1': 0.8257645968489342, 'number': 1065} | 0.7534 | 0.8093 | 0.7804 | 0.8070 |
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+ | 0.0296 | 51.0 | 153 | 1.0616 | {'precision': 0.7266739846322722, 'recall': 0.8182941903584673, 'f1': 0.769767441860465, 'number': 809} | {'precision': 0.4064516129032258, 'recall': 0.5294117647058824, 'f1': 0.4598540145985402, 'number': 119} | {'precision': 0.8117001828153565, 'recall': 0.8338028169014085, 'f1': 0.8226030569708198, 'number': 1065} | 0.7468 | 0.8093 | 0.7768 | 0.8022 |
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+ | 0.0286 | 52.0 | 156 | 1.0679 | {'precision': 0.7313266443701226, 'recall': 0.8108776266996292, 'f1': 0.7690504103165299, 'number': 809} | {'precision': 0.42105263157894735, 'recall': 0.5378151260504201, 'f1': 0.47232472324723246, 'number': 119} | {'precision': 0.8092643051771117, 'recall': 0.8366197183098592, 'f1': 0.8227146814404432, 'number': 1065} | 0.7493 | 0.8083 | 0.7777 | 0.8006 |
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+ | 0.0268 | 53.0 | 159 | 1.0635 | {'precision': 0.7344632768361582, 'recall': 0.8034610630407911, 'f1': 0.7674144037780402, 'number': 809} | {'precision': 0.410958904109589, 'recall': 0.5042016806722689, 'f1': 0.4528301886792453, 'number': 119} | {'precision': 0.8054054054054054, 'recall': 0.8394366197183099, 'f1': 0.8220689655172414, 'number': 1065} | 0.7492 | 0.8048 | 0.7760 | 0.8006 |
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+ | 0.0231 | 54.0 | 162 | 1.0634 | {'precision': 0.7351290684624018, 'recall': 0.8096415327564895, 'f1': 0.7705882352941176, 'number': 809} | {'precision': 0.4161073825503356, 'recall': 0.5210084033613446, 'f1': 0.46268656716417916, 'number': 119} | {'precision': 0.8054298642533937, 'recall': 0.8356807511737089, 'f1': 0.8202764976958525, 'number': 1065} | 0.7492 | 0.8063 | 0.7767 | 0.8026 |
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+ | 0.0258 | 55.0 | 165 | 1.0696 | {'precision': 0.7307262569832402, 'recall': 0.8084054388133498, 'f1': 0.767605633802817, 'number': 809} | {'precision': 0.4155844155844156, 'recall': 0.5378151260504201, 'f1': 0.46886446886446886, 'number': 119} | {'precision': 0.8091324200913242, 'recall': 0.831924882629108, 'f1': 0.8203703703703705, 'number': 1065} | 0.7481 | 0.8048 | 0.7754 | 0.8031 |
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+ | 0.025 | 56.0 | 168 | 1.0740 | {'precision': 0.739524348810872, 'recall': 0.8071693448702101, 'f1': 0.7718676122931442, 'number': 809} | {'precision': 0.4195804195804196, 'recall': 0.5042016806722689, 'f1': 0.4580152671755725, 'number': 119} | {'precision': 0.8018099547511313, 'recall': 0.831924882629108, 'f1': 0.816589861751152, 'number': 1065} | 0.7504 | 0.8023 | 0.7755 | 0.8035 |
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+ | 0.0247 | 57.0 | 171 | 1.0777 | {'precision': 0.7391799544419134, 'recall': 0.8022249690976514, 'f1': 0.7694131594546533, 'number': 809} | {'precision': 0.41605839416058393, 'recall': 0.4789915966386555, 'f1': 0.4453125, 'number': 119} | {'precision': 0.8016157989228008, 'recall': 0.8384976525821596, 'f1': 0.8196420376319412, 'number': 1065} | 0.7511 | 0.8023 | 0.7758 | 0.8035 |
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+ | 0.0226 | 61.0 | 183 | 1.0869 | {'precision': 0.7431506849315068, 'recall': 0.8046971569839307, 'f1': 0.772700296735905, 'number': 809} | {'precision': 0.4178082191780822, 'recall': 0.5126050420168067, 'f1': 0.460377358490566, 'number': 119} | {'precision': 0.8191881918819188, 'recall': 0.8338028169014085, 'f1': 0.8264308980921358, 'number': 1065} | 0.7597 | 0.8028 | 0.7807 | 0.8068 |
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121
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