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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 +25 -25
  2. logs/events.out.tfevents.1717743681.6dbfcd2bfee2.3273.0 +2 -2
  3. logs/events.out.tfevents.1717745469.6dbfcd2bfee2.3273.1 +3 -0
  4. model.safetensors +1 -1
  5. tokenizer.json +16 -2
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: 1.7177
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- - Answer: {'precision': 0.8729411764705882, 'recall': 0.9082007343941249, 'f1': 0.8902219556088783, 'number': 817}
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- - Header: {'precision': 0.6421052631578947, 'recall': 0.5126050420168067, 'f1': 0.5700934579439252, 'number': 119}
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- - Question: {'precision': 0.906021897810219, 'recall': 0.9220055710306406, 'f1': 0.9139438564196963, 'number': 1077}
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- - Overall Precision: 0.8800
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- - Overall Recall: 0.8922
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- - Overall F1: 0.8860
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- - Overall Accuracy: 0.8064
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  ## Model description
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@@ -52,25 +52,25 @@ 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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- | 0.4044 | 10.5263 | 200 | 1.1266 | {'precision': 0.8246606334841629, 'recall': 0.8922888616891065, 'f1': 0.8571428571428571, 'number': 817} | {'precision': 0.3495575221238938, 'recall': 0.6638655462184874, 'f1': 0.4579710144927537, 'number': 119} | {'precision': 0.8747609942638623, 'recall': 0.8495821727019499, 'f1': 0.8619877531794631, 'number': 1077} | 0.7992 | 0.8559 | 0.8266 | 0.7671 |
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- | 0.0518 | 21.0526 | 400 | 1.2142 | {'precision': 0.8138006571741512, 'recall': 0.9094247246022031, 'f1': 0.8589595375722543, 'number': 817} | {'precision': 0.49612403100775193, 'recall': 0.5378151260504201, 'f1': 0.5161290322580645, 'number': 119} | {'precision': 0.8705234159779615, 'recall': 0.8802228412256268, 'f1': 0.8753462603878117, 'number': 1077} | 0.8236 | 0.8718 | 0.8470 | 0.8011 |
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- | 0.0137 | 31.5789 | 600 | 1.5789 | {'precision': 0.8478513356562137, 'recall': 0.8935128518971848, 'f1': 0.8700834326579261, 'number': 817} | {'precision': 0.5588235294117647, 'recall': 0.4789915966386555, 'f1': 0.5158371040723981, 'number': 119} | {'precision': 0.8839528558476881, 'recall': 0.9052924791086351, 'f1': 0.8944954128440367, 'number': 1077} | 0.8529 | 0.8753 | 0.8639 | 0.7932 |
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- | 0.008 | 42.1053 | 800 | 1.5466 | {'precision': 0.8540478905359179, 'recall': 0.9167686658506732, 'f1': 0.8842975206611571, 'number': 817} | {'precision': 0.528169014084507, 'recall': 0.6302521008403361, 'f1': 0.5747126436781609, 'number': 119} | {'precision': 0.899074074074074, 'recall': 0.9015784586815228, 'f1': 0.9003245248029671, 'number': 1077} | 0.8552 | 0.8917 | 0.8731 | 0.7876 |
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- | 0.0047 | 52.6316 | 1000 | 1.5218 | {'precision': 0.8712029161603888, 'recall': 0.8776009791921665, 'f1': 0.874390243902439, 'number': 817} | {'precision': 0.5882352941176471, 'recall': 0.5042016806722689, 'f1': 0.5429864253393665, 'number': 119} | {'precision': 0.9080459770114943, 'recall': 0.8802228412256268, 'f1': 0.8939179632248938, 'number': 1077} | 0.8761 | 0.8569 | 0.8664 | 0.8023 |
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- | 0.0026 | 63.1579 | 1200 | 1.6588 | {'precision': 0.8784596871239471, 'recall': 0.8935128518971848, 'f1': 0.8859223300970873, 'number': 817} | {'precision': 0.532258064516129, 'recall': 0.5546218487394958, 'f1': 0.54320987654321, 'number': 119} | {'precision': 0.8739946380697051, 'recall': 0.9080779944289693, 'f1': 0.8907103825136613, 'number': 1077} | 0.8554 | 0.8813 | 0.8681 | 0.7971 |
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- | 0.0013 | 73.6842 | 1400 | 1.6428 | {'precision': 0.903822441430333, 'recall': 0.8971848225214198, 'f1': 0.9004914004914006, 'number': 817} | {'precision': 0.6166666666666667, 'recall': 0.6218487394957983, 'f1': 0.6192468619246863, 'number': 119} | {'precision': 0.9017132551848512, 'recall': 0.9285051067780873, 'f1': 0.9149130832570905, 'number': 1077} | 0.8858 | 0.8977 | 0.8917 | 0.8127 |
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- | 0.0009 | 84.2105 | 1600 | 1.6516 | {'precision': 0.8909090909090909, 'recall': 0.8996328029375765, 'f1': 0.8952496954933008, 'number': 817} | {'precision': 0.6132075471698113, 'recall': 0.5462184873949579, 'f1': 0.5777777777777778, 'number': 119} | {'precision': 0.9070837166513339, 'recall': 0.9155060352831941, 'f1': 0.911275415896488, 'number': 1077} | 0.8850 | 0.8872 | 0.8861 | 0.8116 |
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- | 0.0007 | 94.7368 | 1800 | 1.7017 | {'precision': 0.8470319634703196, 'recall': 0.9082007343941249, 'f1': 0.8765505020673362, 'number': 817} | {'precision': 0.6521739130434783, 'recall': 0.5042016806722689, 'f1': 0.5687203791469194, 'number': 119} | {'precision': 0.8938547486033519, 'recall': 0.8913649025069638, 'f1': 0.8926080892608089, 'number': 1077} | 0.8629 | 0.8753 | 0.8691 | 0.8004 |
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- | 0.0004 | 105.2632 | 2000 | 1.7304 | {'precision': 0.8624708624708625, 'recall': 0.9057527539779682, 'f1': 0.8835820895522388, 'number': 817} | {'precision': 0.6095238095238096, 'recall': 0.5378151260504201, 'f1': 0.5714285714285715, 'number': 119} | {'precision': 0.906046511627907, 'recall': 0.904363974001857, 'f1': 0.9052044609665427, 'number': 1077} | 0.8724 | 0.8833 | 0.8778 | 0.8019 |
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- | 0.0003 | 115.7895 | 2200 | 1.7230 | {'precision': 0.8723404255319149, 'recall': 0.9033047735618115, 'f1': 0.8875526157546603, 'number': 817} | {'precision': 0.6702127659574468, 'recall': 0.5294117647058824, 'f1': 0.5915492957746479, 'number': 119} | {'precision': 0.8992740471869328, 'recall': 0.9201485608170845, 'f1': 0.9095915557595228, 'number': 1077} | 0.8776 | 0.8902 | 0.8838 | 0.8049 |
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- | 0.0002 | 126.3158 | 2400 | 1.7177 | {'precision': 0.8729411764705882, 'recall': 0.9082007343941249, 'f1': 0.8902219556088783, 'number': 817} | {'precision': 0.6421052631578947, 'recall': 0.5126050420168067, 'f1': 0.5700934579439252, 'number': 119} | {'precision': 0.906021897810219, 'recall': 0.9220055710306406, 'f1': 0.9139438564196963, 'number': 1077} | 0.8800 | 0.8922 | 0.8860 | 0.8064 |
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  ### Framework versions
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- - Transformers 4.40.2
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- - Pytorch 2.2.1+cu121
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- - Datasets 2.19.1
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  - Tokenizers 0.19.1
 
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: 1.7110
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+ - Answer: {'precision': 0.8460661345496009, 'recall': 0.9082007343941249, 'f1': 0.8760330578512396, 'number': 817}
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+ - Header: {'precision': 0.6470588235294118, 'recall': 0.5546218487394958, 'f1': 0.5972850678733032, 'number': 119}
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+ - Question: {'precision': 0.9019248395967002, 'recall': 0.9136490250696379, 'f1': 0.9077490774907748, 'number': 1077}
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+ - Overall Precision: 0.8657
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+ - Overall Recall: 0.8902
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+ - Overall F1: 0.8778
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+ - Overall Accuracy: 0.7988
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27
  ## Model description
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52
 
53
  ### Training results
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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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+ | 0.4082 | 10.5263 | 200 | 1.0891 | {'precision': 0.8286384976525821, 'recall': 0.8641370869033048, 'f1': 0.8460155781905333, 'number': 817} | {'precision': 0.4550898203592814, 'recall': 0.6386554621848739, 'f1': 0.5314685314685315, 'number': 119} | {'precision': 0.8792134831460674, 'recall': 0.871866295264624, 'f1': 0.8755244755244757, 'number': 1077} | 0.8246 | 0.8549 | 0.8395 | 0.7758 |
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+ | 0.0489 | 21.0526 | 400 | 1.1864 | {'precision': 0.8470588235294118, 'recall': 0.8812729498164015, 'f1': 0.8638272345530894, 'number': 817} | {'precision': 0.6078431372549019, 'recall': 0.5210084033613446, 'f1': 0.5610859728506787, 'number': 119} | {'precision': 0.8630377524143986, 'recall': 0.9127205199628597, 'f1': 0.8871841155234657, 'number': 1077} | 0.8441 | 0.8768 | 0.8601 | 0.8014 |
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+ | 0.0135 | 31.5789 | 600 | 1.4159 | {'precision': 0.8746928746928747, 'recall': 0.8714810281517748, 'f1': 0.8730839975475169, 'number': 817} | {'precision': 0.59375, 'recall': 0.4789915966386555, 'f1': 0.5302325581395348, 'number': 119} | {'precision': 0.8701964133219471, 'recall': 0.9461467038068709, 'f1': 0.9065836298932384, 'number': 1077} | 0.8592 | 0.8882 | 0.8735 | 0.8040 |
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+ | 0.007 | 42.1053 | 800 | 1.4263 | {'precision': 0.8548199767711963, 'recall': 0.9008567931456548, 'f1': 0.8772348033373063, 'number': 817} | {'precision': 0.6138613861386139, 'recall': 0.5210084033613446, 'f1': 0.5636363636363637, 'number': 119} | {'precision': 0.8946412352406903, 'recall': 0.914577530176416, 'f1': 0.9044995408631772, 'number': 1077} | 0.8643 | 0.8857 | 0.8749 | 0.8061 |
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+ | 0.0039 | 52.6316 | 1000 | 1.6051 | {'precision': 0.8764845605700713, 'recall': 0.9033047735618115, 'f1': 0.8896925858951176, 'number': 817} | {'precision': 0.5323741007194245, 'recall': 0.6218487394957983, 'f1': 0.5736434108527132, 'number': 119} | {'precision': 0.8847209515096066, 'recall': 0.8978644382544104, 'f1': 0.8912442396313364, 'number': 1077} | 0.8578 | 0.8838 | 0.8706 | 0.7967 |
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+ | 0.0017 | 63.1579 | 1200 | 1.5147 | {'precision': 0.8608490566037735, 'recall': 0.8935128518971848, 'f1': 0.8768768768768769, 'number': 817} | {'precision': 0.6388888888888888, 'recall': 0.5798319327731093, 'f1': 0.6079295154185022, 'number': 119} | {'precision': 0.8934056007226739, 'recall': 0.9182915506035283, 'f1': 0.9056776556776556, 'number': 1077} | 0.8667 | 0.8882 | 0.8773 | 0.8087 |
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+ | 0.0014 | 73.6842 | 1400 | 1.8128 | {'precision': 0.8349514563106796, 'recall': 0.9473684210526315, 'f1': 0.8876146788990826, 'number': 817} | {'precision': 0.6078431372549019, 'recall': 0.5210084033613446, 'f1': 0.5610859728506787, 'number': 119} | {'precision': 0.9125475285171103, 'recall': 0.8913649025069638, 'f1': 0.9018318459370597, 'number': 1077} | 0.8630 | 0.8922 | 0.8774 | 0.7931 |
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+ | 0.001 | 84.2105 | 1600 | 1.7309 | {'precision': 0.8884758364312267, 'recall': 0.8776009791921665, 'f1': 0.8830049261083744, 'number': 817} | {'precision': 0.576271186440678, 'recall': 0.5714285714285714, 'f1': 0.5738396624472574, 'number': 119} | {'precision': 0.8825622775800712, 'recall': 0.9210770659238626, 'f1': 0.9014084507042255, 'number': 1077} | 0.8673 | 0.8828 | 0.8749 | 0.7998 |
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+ | 0.0006 | 94.7368 | 1800 | 1.7644 | {'precision': 0.8462414578587699, 'recall': 0.9094247246022031, 'f1': 0.8766961651917403, 'number': 817} | {'precision': 0.6363636363636364, 'recall': 0.5294117647058824, 'f1': 0.5779816513761468, 'number': 119} | {'precision': 0.9010175763182239, 'recall': 0.904363974001857, 'f1': 0.9026876737720111, 'number': 1077} | 0.8649 | 0.8843 | 0.8745 | 0.7967 |
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+ | 0.0006 | 105.2632 | 2000 | 1.6953 | {'precision': 0.8673835125448028, 'recall': 0.8886168910648715, 'f1': 0.8778718258766626, 'number': 817} | {'precision': 0.6666666666666666, 'recall': 0.5378151260504201, 'f1': 0.5953488372093023, 'number': 119} | {'precision': 0.8741319444444444, 'recall': 0.9350046425255338, 'f1': 0.9035441902198295, 'number': 1077} | 0.8619 | 0.8927 | 0.8770 | 0.8027 |
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+ | 0.0003 | 115.7895 | 2200 | 1.7110 | {'precision': 0.8460661345496009, 'recall': 0.9082007343941249, 'f1': 0.8760330578512396, 'number': 817} | {'precision': 0.6470588235294118, 'recall': 0.5546218487394958, 'f1': 0.5972850678733032, 'number': 119} | {'precision': 0.9019248395967002, 'recall': 0.9136490250696379, 'f1': 0.9077490774907748, 'number': 1077} | 0.8657 | 0.8902 | 0.8778 | 0.7988 |
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+ | 0.0002 | 126.3158 | 2400 | 1.7082 | {'precision': 0.8447488584474886, 'recall': 0.9057527539779682, 'f1': 0.874187832250443, 'number': 817} | {'precision': 0.6336633663366337, 'recall': 0.5378151260504201, 'f1': 0.5818181818181819, 'number': 119} | {'precision': 0.9002744739249772, 'recall': 0.9136490250696379, 'f1': 0.9069124423963134, 'number': 1077} | 0.8638 | 0.8882 | 0.8758 | 0.7978 |
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  ### Framework versions
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73
+ - Transformers 4.41.2
74
+ - Pytorch 2.3.0+cu121
75
+ - Datasets 2.19.2
76
  - Tokenizers 0.19.1
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