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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.1716812721.c190ecb2bcd6.25029.0 +2 -2
  3. logs/events.out.tfevents.1716813104.c190ecb2bcd6.25029.1 +3 -0
  4. model.safetensors +1 -1
  5. tokenizer.json +2 -16
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: 1.7288
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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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@@ -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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- | 0.404 | 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
 
15
 
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: 1.6681
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+ - Answer: {'precision': 0.8778173190984578, 'recall': 0.9057527539779682, 'f1': 0.891566265060241, 'number': 817}
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+ - Header: {'precision': 0.6036036036036037, 'recall': 0.5630252100840336, 'f1': 0.582608695652174, 'number': 119}
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+ - Question: {'precision': 0.9024839006439742, 'recall': 0.9108635097493036, 'f1': 0.9066543438077633, 'number': 1077}
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+ - Overall Precision: 0.8760
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+ - Overall Recall: 0.8882
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+ - Overall F1: 0.8821
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+ - Overall Accuracy: 0.8030
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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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+ | 0.431 | 10.5263 | 200 | 1.0598 | {'precision': 0.8017718715393134, 'recall': 0.8861689106487148, 'f1': 0.841860465116279, 'number': 817} | {'precision': 0.4228187919463087, 'recall': 0.5294117647058824, 'f1': 0.47014925373134325, 'number': 119} | {'precision': 0.8755935422602089, 'recall': 0.8560817084493965, 'f1': 0.8657276995305164, 'number': 1077} | 0.8119 | 0.8490 | 0.8300 | 0.7774 |
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+ | 0.0542 | 21.0526 | 400 | 1.2173 | {'precision': 0.8382352941176471, 'recall': 0.9069767441860465, 'f1': 0.8712522045855379, 'number': 817} | {'precision': 0.5350877192982456, 'recall': 0.5126050420168067, 'f1': 0.5236051502145922, 'number': 119} | {'precision': 0.8882521489971347, 'recall': 0.8635097493036211, 'f1': 0.8757062146892656, 'number': 1077} | 0.8469 | 0.8604 | 0.8536 | 0.8016 |
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+ | 0.014 | 31.5789 | 600 | 1.2955 | {'precision': 0.8415051311288484, 'recall': 0.9033047735618115, 'f1': 0.8713105076741442, 'number': 817} | {'precision': 0.6210526315789474, 'recall': 0.4957983193277311, 'f1': 0.5514018691588785, 'number': 119} | {'precision': 0.8972477064220183, 'recall': 0.9080779944289693, 'f1': 0.9026303645592985, 'number': 1077} | 0.8608 | 0.8818 | 0.8712 | 0.8160 |
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+ | 0.0064 | 42.1053 | 800 | 1.2848 | {'precision': 0.8696186961869619, 'recall': 0.8653610771113831, 'f1': 0.8674846625766871, 'number': 817} | {'precision': 0.5193798449612403, 'recall': 0.5630252100840336, 'f1': 0.5403225806451614, 'number': 119} | {'precision': 0.858274647887324, 'recall': 0.9052924791086351, 'f1': 0.8811568007230005, 'number': 1077} | 0.8417 | 0.8689 | 0.8550 | 0.8222 |
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+ | 0.0037 | 52.6316 | 1000 | 1.5983 | {'precision': 0.8530751708428246, 'recall': 0.9167686658506732, 'f1': 0.8837758112094395, 'number': 817} | {'precision': 0.5658914728682171, 'recall': 0.6134453781512605, 'f1': 0.5887096774193549, 'number': 119} | {'precision': 0.8946360153256705, 'recall': 0.8672237697307336, 'f1': 0.8807166430928807, 'number': 1077} | 0.8562 | 0.8723 | 0.8642 | 0.7916 |
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+ | 0.0034 | 63.1579 | 1200 | 1.5936 | {'precision': 0.85, 'recall': 0.9155446756425949, 'f1': 0.881555686505598, 'number': 817} | {'precision': 0.5619047619047619, 'recall': 0.4957983193277311, 'f1': 0.5267857142857143, 'number': 119} | {'precision': 0.8912442396313364, 'recall': 0.8978644382544104, 'f1': 0.8945420906567992, 'number': 1077} | 0.8570 | 0.8813 | 0.8690 | 0.8102 |
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+ | 0.0021 | 73.6842 | 1400 | 1.4765 | {'precision': 0.8558139534883721, 'recall': 0.9008567931456548, 'f1': 0.877757901013715, 'number': 817} | {'precision': 0.5619047619047619, 'recall': 0.4957983193277311, 'f1': 0.5267857142857143, 'number': 119} | {'precision': 0.885036496350365, 'recall': 0.9006499535747446, 'f1': 0.892774965485504, 'number': 1077} | 0.8564 | 0.8768 | 0.8665 | 0.8010 |
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+ | 0.0009 | 84.2105 | 1600 | 1.6681 | {'precision': 0.8778173190984578, 'recall': 0.9057527539779682, 'f1': 0.891566265060241, 'number': 817} | {'precision': 0.6036036036036037, 'recall': 0.5630252100840336, 'f1': 0.582608695652174, 'number': 119} | {'precision': 0.9024839006439742, 'recall': 0.9108635097493036, 'f1': 0.9066543438077633, 'number': 1077} | 0.8760 | 0.8882 | 0.8821 | 0.8030 |
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+ | 0.0003 | 94.7368 | 1800 | 1.6379 | {'precision': 0.8595238095238096, 'recall': 0.8837209302325582, 'f1': 0.8714544357272178, 'number': 817} | {'precision': 0.5929203539823009, 'recall': 0.5630252100840336, 'f1': 0.5775862068965517, 'number': 119} | {'precision': 0.896709323583181, 'recall': 0.9108635097493036, 'f1': 0.9037309995393827, 'number': 1077} | 0.8647 | 0.8793 | 0.8719 | 0.7986 |
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+ | 0.0002 | 105.2632 | 2000 | 1.7186 | {'precision': 0.8644859813084113, 'recall': 0.9057527539779682, 'f1': 0.8846383741781233, 'number': 817} | {'precision': 0.5675675675675675, 'recall': 0.5294117647058824, 'f1': 0.5478260869565218, 'number': 119} | {'precision': 0.8921658986175115, 'recall': 0.8987929433611885, 'f1': 0.8954671600370029, 'number': 1077} | 0.8631 | 0.8798 | 0.8713 | 0.7978 |
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+ | 0.0003 | 115.7895 | 2200 | 1.6765 | {'precision': 0.8690476190476191, 'recall': 0.8935128518971848, 'f1': 0.8811104405552203, 'number': 817} | {'precision': 0.5726495726495726, 'recall': 0.5630252100840336, 'f1': 0.5677966101694915, 'number': 119} | {'precision': 0.8934802571166207, 'recall': 0.903435468895079, 'f1': 0.8984302862419206, 'number': 1077} | 0.8651 | 0.8793 | 0.8721 | 0.8000 |
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+ | 0.0003 | 126.3158 | 2400 | 1.7309 | {'precision': 0.8817852834740652, 'recall': 0.8947368421052632, 'f1': 0.888213851761847, 'number': 817} | {'precision': 0.5675675675675675, 'recall': 0.5294117647058824, 'f1': 0.5478260869565218, 'number': 119} | {'precision': 0.8914233576642335, 'recall': 0.9071494893221913, 'f1': 0.8992176714219972, 'number': 1077} | 0.8698 | 0.8798 | 0.8748 | 0.7959 |
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