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lilt-en-funsd

Token Classification
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lilt
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End of training

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  1. README.md +22 -22
  2. logs/events.out.tfevents.1716809082.c190ecb2bcd6.9598.0 +2 -2
  3. logs/events.out.tfevents.1716809466.c190ecb2bcd6.9598.1 +3 -0
  4. model.safetensors +1 -1
README.md CHANGED
@@ -15,14 +15,14 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.6046
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- - Answer: {'precision': 0.8879518072289156, 'recall': 0.9020807833537332, 'f1': 0.8949605343047966, 'number': 817}
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- - Header: {'precision': 0.6761904761904762, 'recall': 0.5966386554621849, 'f1': 0.6339285714285715, 'number': 119}
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- - Question: {'precision': 0.8968609865470852, 'recall': 0.9285051067780873, 'f1': 0.9124087591240877, 'number': 1077}
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- - Overall Precision: 0.8820
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- - Overall Recall: 0.8982
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- - Overall F1: 0.8900
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- - Overall Accuracy: 0.8187
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  ## Model description
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@@ -52,20 +52,20 @@ The following hyperparameters were used during training:
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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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- | 0.3984 | 10.5263 | 200 | 0.9319 | {'precision': 0.837129840546697, 'recall': 0.8996328029375765, 'f1': 0.8672566371681416, 'number': 817} | {'precision': 0.5238095238095238, 'recall': 0.46218487394957986, 'f1': 0.4910714285714286, 'number': 119} | {'precision': 0.8606060606060606, 'recall': 0.9229340761374187, 'f1': 0.8906810035842293, 'number': 1077} | 0.8344 | 0.8862 | 0.8596 | 0.7926 |
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- | 0.0543 | 21.0526 | 400 | 1.1018 | {'precision': 0.8492520138089759, 'recall': 0.9033047735618115, 'f1': 0.8754448398576511, 'number': 817} | {'precision': 0.5688073394495413, 'recall': 0.5210084033613446, 'f1': 0.543859649122807, 'number': 119} | {'precision': 0.8731277533039647, 'recall': 0.9201485608170845, 'f1': 0.8960216998191681, 'number': 1077} | 0.8476 | 0.8897 | 0.8682 | 0.8133 |
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- | 0.0134 | 31.5789 | 600 | 1.5231 | {'precision': 0.838963963963964, 'recall': 0.9118727050183598, 'f1': 0.873900293255132, 'number': 817} | {'precision': 0.5727272727272728, 'recall': 0.5294117647058824, 'f1': 0.5502183406113538, 'number': 119} | {'precision': 0.9149338374291115, 'recall': 0.8987929433611885, 'f1': 0.9067915690866512, 'number': 1077} | 0.8638 | 0.8823 | 0.8729 | 0.8046 |
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- | 0.0101 | 42.1053 | 800 | 1.5678 | {'precision': 0.8509895227008148, 'recall': 0.8947368421052632, 'f1': 0.8723150357995225, 'number': 817} | {'precision': 0.5700934579439252, 'recall': 0.5126050420168067, 'f1': 0.5398230088495575, 'number': 119} | {'precision': 0.8781770376862401, 'recall': 0.9303621169916435, 'f1': 0.9035166816952209, 'number': 1077} | 0.8514 | 0.8912 | 0.8709 | 0.8052 |
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- | 0.0041 | 52.6316 | 1000 | 1.6538 | {'precision': 0.8399558498896247, 'recall': 0.9314565483476133, 'f1': 0.8833430063842136, 'number': 817} | {'precision': 0.6705882352941176, 'recall': 0.4789915966386555, 'f1': 0.5588235294117647, 'number': 119} | {'precision': 0.9063386944181646, 'recall': 0.8895078922934077, 'f1': 0.8978444236176195, 'number': 1077} | 0.8672 | 0.8823 | 0.8747 | 0.7979 |
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- | 0.0033 | 63.1579 | 1200 | 1.4464 | {'precision': 0.875, 'recall': 0.9167686658506732, 'f1': 0.895397489539749, 'number': 817} | {'precision': 0.6049382716049383, 'recall': 0.4117647058823529, 'f1': 0.49000000000000005, 'number': 119} | {'precision': 0.8777292576419214, 'recall': 0.9331476323119777, 'f1': 0.9045904590459046, 'number': 1077} | 0.8660 | 0.8957 | 0.8806 | 0.8152 |
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- | 0.0015 | 73.6842 | 1400 | 1.5128 | {'precision': 0.8679906542056075, 'recall': 0.9094247246022031, 'f1': 0.8882247459653317, 'number': 817} | {'precision': 0.6511627906976745, 'recall': 0.47058823529411764, 'f1': 0.5463414634146342, 'number': 119} | {'precision': 0.8906810035842294, 'recall': 0.9229340761374187, 'f1': 0.9065207478340173, 'number': 1077} | 0.8712 | 0.8907 | 0.8809 | 0.8213 |
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- | 0.0014 | 84.2105 | 1600 | 1.6089 | {'precision': 0.8555176336746303, 'recall': 0.9204406364749081, 'f1': 0.8867924528301887, 'number': 817} | {'precision': 0.6074766355140186, 'recall': 0.5462184873949579, 'f1': 0.575221238938053, 'number': 119} | {'precision': 0.8891820580474934, 'recall': 0.9387186629526463, 'f1': 0.913279132791328, 'number': 1077} | 0.8610 | 0.9081 | 0.8839 | 0.8172 |
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- | 0.0005 | 94.7368 | 1800 | 1.6500 | {'precision': 0.865967365967366, 'recall': 0.9094247246022031, 'f1': 0.8871641791044775, 'number': 817} | {'precision': 0.6513761467889908, 'recall': 0.5966386554621849, 'f1': 0.6228070175438596, 'number': 119} | {'precision': 0.9027522935779817, 'recall': 0.9136490250696379, 'f1': 0.9081679741578219, 'number': 1077} | 0.8741 | 0.8932 | 0.8835 | 0.8135 |
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- | 0.0005 | 105.2632 | 2000 | 1.6204 | {'precision': 0.8909090909090909, 'recall': 0.8996328029375765, 'f1': 0.8952496954933008, 'number': 817} | {'precision': 0.6403508771929824, 'recall': 0.6134453781512605, 'f1': 0.6266094420600858, 'number': 119} | {'precision': 0.8925399644760214, 'recall': 0.9331476323119777, 'f1': 0.9123921924648207, 'number': 1077} | 0.8780 | 0.9006 | 0.8892 | 0.8177 |
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- | 0.0004 | 115.7895 | 2200 | 1.6046 | {'precision': 0.8879518072289156, 'recall': 0.9020807833537332, 'f1': 0.8949605343047966, 'number': 817} | {'precision': 0.6761904761904762, 'recall': 0.5966386554621849, 'f1': 0.6339285714285715, 'number': 119} | {'precision': 0.8968609865470852, 'recall': 0.9285051067780873, 'f1': 0.9124087591240877, 'number': 1077} | 0.8820 | 0.8982 | 0.8900 | 0.8187 |
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- | 0.0002 | 126.3158 | 2400 | 1.6270 | {'precision': 0.8790035587188612, 'recall': 0.9069767441860465, 'f1': 0.8927710843373493, 'number': 817} | {'precision': 0.6574074074074074, 'recall': 0.5966386554621849, 'f1': 0.6255506607929515, 'number': 119} | {'precision': 0.8972046889089269, 'recall': 0.9238625812441968, 'f1': 0.9103385178408051, 'number': 1077} | 0.8772 | 0.8977 | 0.8873 | 0.8193 |
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  ### Framework versions
 
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16
  This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on an unknown dataset.
17
  It achieves the following results on the evaluation set:
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+ - Loss: 1.7288
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+ - Answer: {'precision': 0.8730904817861339, 'recall': 0.9094247246022031, 'f1': 0.8908872901678656, 'number': 817}
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+ - Header: {'precision': 0.6458333333333334, 'recall': 0.5210084033613446, 'f1': 0.5767441860465117, 'number': 119}
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+ - Question: {'precision': 0.8807174887892377, 'recall': 0.9117920148560817, 'f1': 0.895985401459854, 'number': 1077}
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+ - Overall Precision: 0.8666
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+ - Overall Recall: 0.8877
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+ - Overall F1: 0.8771
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+ - Overall Accuracy: 0.7984
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  ## Model description
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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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+ | 0.404 | 10.5263 | 200 | 1.1544 | {'precision': 0.8410138248847926, 'recall': 0.8935128518971848, 'f1': 0.8664688427299703, 'number': 817} | {'precision': 0.423841059602649, 'recall': 0.5378151260504201, 'f1': 0.47407407407407404, 'number': 119} | {'precision': 0.8815165876777251, 'recall': 0.8635097493036211, 'f1': 0.8724202626641652, 'number': 1077} | 0.8312 | 0.8564 | 0.8437 | 0.7881 |
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+ | 0.0468 | 21.0526 | 400 | 1.2727 | {'precision': 0.8417508417508418, 'recall': 0.9179926560587516, 'f1': 0.8782201405152226, 'number': 817} | {'precision': 0.5739130434782609, 'recall': 0.5546218487394958, 'f1': 0.5641025641025642, 'number': 119} | {'precision': 0.9006499535747446, 'recall': 0.9006499535747446, 'f1': 0.9006499535747446, 'number': 1077} | 0.8574 | 0.8872 | 0.8721 | 0.8097 |
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+ | 0.0134 | 31.5789 | 600 | 1.4898 | {'precision': 0.8528389339513326, 'recall': 0.9008567931456548, 'f1': 0.8761904761904762, 'number': 817} | {'precision': 0.5892857142857143, 'recall': 0.5546218487394958, 'f1': 0.5714285714285715, 'number': 119} | {'precision': 0.8794964028776978, 'recall': 0.9080779944289693, 'f1': 0.8935587026039287, 'number': 1077} | 0.8529 | 0.8843 | 0.8683 | 0.7909 |
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+ | 0.008 | 42.1053 | 800 | 1.7131 | {'precision': 0.8675263774912075, 'recall': 0.9057527539779682, 'f1': 0.8862275449101797, 'number': 817} | {'precision': 0.5528455284552846, 'recall': 0.5714285714285714, 'f1': 0.5619834710743802, 'number': 119} | {'precision': 0.899624765478424, 'recall': 0.8904363974001857, 'f1': 0.8950069995333644, 'number': 1077} | 0.8653 | 0.8778 | 0.8715 | 0.7865 |
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+ | 0.0053 | 52.6316 | 1000 | 1.5916 | {'precision': 0.8553386911595867, 'recall': 0.9118727050183598, 'f1': 0.8827014218009479, 'number': 817} | {'precision': 0.5163934426229508, 'recall': 0.5294117647058824, 'f1': 0.5228215767634855, 'number': 119} | {'precision': 0.8995391705069125, 'recall': 0.9062209842154132, 'f1': 0.902867715078631, 'number': 1077} | 0.8585 | 0.8862 | 0.8722 | 0.7891 |
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+ | 0.0026 | 63.1579 | 1200 | 1.5475 | {'precision': 0.8837772397094431, 'recall': 0.8935128518971848, 'f1': 0.8886183810103468, 'number': 817} | {'precision': 0.5294117647058824, 'recall': 0.6050420168067226, 'f1': 0.5647058823529412, 'number': 119} | {'precision': 0.8887884267631103, 'recall': 0.9127205199628597, 'f1': 0.9005955107650022, 'number': 1077} | 0.8632 | 0.8867 | 0.8748 | 0.7981 |
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+ | 0.0024 | 73.6842 | 1400 | 1.7288 | {'precision': 0.8730904817861339, 'recall': 0.9094247246022031, 'f1': 0.8908872901678656, 'number': 817} | {'precision': 0.6458333333333334, 'recall': 0.5210084033613446, 'f1': 0.5767441860465117, 'number': 119} | {'precision': 0.8807174887892377, 'recall': 0.9117920148560817, 'f1': 0.895985401459854, 'number': 1077} | 0.8666 | 0.8877 | 0.8771 | 0.7984 |
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+ | 0.0012 | 84.2105 | 1600 | 1.6515 | {'precision': 0.8591549295774648, 'recall': 0.8959608323133414, 'f1': 0.8771719592570402, 'number': 817} | {'precision': 0.5929203539823009, 'recall': 0.5630252100840336, 'f1': 0.5775862068965517, 'number': 119} | {'precision': 0.879746835443038, 'recall': 0.903435468895079, 'f1': 0.891433806688044, 'number': 1077} | 0.8556 | 0.8803 | 0.8678 | 0.7986 |
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+ | 0.0005 | 94.7368 | 1800 | 1.7500 | {'precision': 0.8662790697674418, 'recall': 0.9118727050183598, 'f1': 0.8884913536076327, 'number': 817} | {'precision': 0.6504854368932039, 'recall': 0.5630252100840336, 'f1': 0.6036036036036037, 'number': 119} | {'precision': 0.8945420906567992, 'recall': 0.8978644382544104, 'f1': 0.8962001853568119, 'number': 1077} | 0.8704 | 0.8838 | 0.8770 | 0.7948 |
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+ | 0.0003 | 105.2632 | 2000 | 1.7407 | {'precision': 0.8776722090261283, 'recall': 0.9045287637698899, 'f1': 0.8908981314044605, 'number': 817} | {'precision': 0.5929203539823009, 'recall': 0.5630252100840336, 'f1': 0.5775862068965517, 'number': 119} | {'precision': 0.8937153419593346, 'recall': 0.8978644382544104, 'f1': 0.8957850856878184, 'number': 1077} | 0.8704 | 0.8808 | 0.8756 | 0.7957 |
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+ | 0.0003 | 115.7895 | 2200 | 1.7708 | {'precision': 0.8667439165701043, 'recall': 0.9155446756425949, 'f1': 0.8904761904761905, 'number': 817} | {'precision': 0.5641025641025641, 'recall': 0.5546218487394958, 'f1': 0.559322033898305, 'number': 119} | {'precision': 0.89322191272052, 'recall': 0.89322191272052, 'f1': 0.89322191272052, 'number': 1077} | 0.8634 | 0.8823 | 0.8727 | 0.7916 |
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+ | 0.0002 | 126.3158 | 2400 | 1.7680 | {'precision': 0.8663594470046083, 'recall': 0.9204406364749081, 'f1': 0.8925816023738872, 'number': 817} | {'precision': 0.5726495726495726, 'recall': 0.5630252100840336, 'f1': 0.5677966101694915, 'number': 119} | {'precision': 0.8944444444444445, 'recall': 0.8969359331476323, 'f1': 0.8956884561891516, 'number': 1077} | 0.8644 | 0.8867 | 0.8754 | 0.7925 |
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  ### Framework versions
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