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layoutlm-custom_no_text

Token Classification
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TensorBoard
Safetensors
layoutlm
Generated from Trainer
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End of training

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  1. README.md +23 -23
  2. logs/events.out.tfevents.1704864698.6898f8981200.808.0 +2 -2
  3. model.safetensors +1 -1
README.md CHANGED
@@ -15,13 +15,13 @@ should probably proofread and complete it, then remove this comment. -->
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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 None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.1523
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- - Noise: {'precision': 0.8811544991511036, 'recall': 0.8994800693240901, 'f1': 0.8902229845626072, 'number': 577}
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- - Signal: {'precision': 0.8675721561969439, 'recall': 0.8856152512998267, 'f1': 0.8765008576329331, 'number': 577}
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- - Overall Precision: 0.8744
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- - Overall Recall: 0.8925
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- - Overall F1: 0.8834
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- - Overall Accuracy: 0.9664
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  ## Model description
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@@ -51,23 +51,23 @@ The following hyperparameters were used during training:
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Noise | Signal | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
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- | 0.3886 | 1.0 | 18 | 0.2452 | {'precision': 0.6213235294117647, 'recall': 0.58578856152513, 'f1': 0.6030330062444246, 'number': 577} | {'precision': 0.6323529411764706, 'recall': 0.5961871750433275, 'f1': 0.6137377341659233, 'number': 577} | 0.6268 | 0.5910 | 0.6084 | 0.8992 |
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- | 0.1673 | 2.0 | 36 | 0.1441 | {'precision': 0.7667269439421338, 'recall': 0.7348353552859619, 'f1': 0.7504424778761062, 'number': 577} | {'precision': 0.7450271247739603, 'recall': 0.7140381282495667, 'f1': 0.7292035398230089, 'number': 577} | 0.7559 | 0.7244 | 0.7398 | 0.9356 |
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- | 0.0959 | 3.0 | 54 | 0.1168 | {'precision': 0.8131487889273357, 'recall': 0.8145580589254766, 'f1': 0.8138528138528138, 'number': 577} | {'precision': 0.7941176470588235, 'recall': 0.7954939341421143, 'f1': 0.7948051948051947, 'number': 577} | 0.8036 | 0.8050 | 0.8043 | 0.9510 |
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- | 0.0622 | 4.0 | 72 | 0.1166 | {'precision': 0.8402061855670103, 'recall': 0.8474870017331022, 'f1': 0.8438308886971526, 'number': 577} | {'precision': 0.8333333333333334, 'recall': 0.8405545927209706, 'f1': 0.8369283865401207, 'number': 577} | 0.8368 | 0.8440 | 0.8404 | 0.9591 |
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- | 0.0424 | 5.0 | 90 | 0.1325 | {'precision': 0.8476027397260274, 'recall': 0.8578856152512998, 'f1': 0.8527131782945737, 'number': 577} | {'precision': 0.839041095890411, 'recall': 0.8492201039861352, 'f1': 0.8440999138673558, 'number': 577} | 0.8433 | 0.8536 | 0.8484 | 0.9586 |
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- | 0.031 | 6.0 | 108 | 0.1167 | {'precision': 0.8720136518771331, 'recall': 0.8856152512998267, 'f1': 0.878761822871883, 'number': 577} | {'precision': 0.8583617747440273, 'recall': 0.8717504332755632, 'f1': 0.8650042992261393, 'number': 577} | 0.8652 | 0.8787 | 0.8719 | 0.9628 |
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- | 0.0213 | 7.0 | 126 | 0.1339 | {'precision': 0.8610634648370498, 'recall': 0.8700173310225303, 'f1': 0.8655172413793105, 'number': 577} | {'precision': 0.855917667238422, 'recall': 0.8648180242634316, 'f1': 0.860344827586207, 'number': 577} | 0.8585 | 0.8674 | 0.8629 | 0.9608 |
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- | 0.0159 | 8.0 | 144 | 0.1335 | {'precision': 0.8692699490662139, 'recall': 0.8873483535528596, 'f1': 0.8782161234991425, 'number': 577} | {'precision': 0.8590831918505942, 'recall': 0.8769497400346621, 'f1': 0.8679245283018868, 'number': 577} | 0.8642 | 0.8821 | 0.8731 | 0.9630 |
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- | 0.0117 | 9.0 | 162 | 0.1489 | {'precision': 0.8686006825938567, 'recall': 0.8821490467937608, 'f1': 0.8753224419604471, 'number': 577} | {'precision': 0.8600682593856656, 'recall': 0.8734835355285961, 'f1': 0.8667239896818572, 'number': 577} | 0.8643 | 0.8778 | 0.8710 | 0.9622 |
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- | 0.011 | 10.0 | 180 | 0.1593 | {'precision': 0.8623063683304647, 'recall': 0.8682842287694974, 'f1': 0.8652849740932642, 'number': 577} | {'precision': 0.8519793459552496, 'recall': 0.8578856152512998, 'f1': 0.854922279792746, 'number': 577} | 0.8571 | 0.8631 | 0.8601 | 0.9600 |
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- | 0.0094 | 11.0 | 198 | 0.1336 | {'precision': 0.8896434634974533, 'recall': 0.9081455805892548, 'f1': 0.8987993138936535, 'number': 577} | {'precision': 0.8760611205432938, 'recall': 0.8942807625649913, 'f1': 0.8850771869639794, 'number': 577} | 0.8829 | 0.9012 | 0.8919 | 0.9686 |
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- | 0.0066 | 12.0 | 216 | 0.1357 | {'precision': 0.8928571428571429, 'recall': 0.9098786828422877, 'f1': 0.9012875536480687, 'number': 577} | {'precision': 0.8792517006802721, 'recall': 0.8960138648180243, 'f1': 0.8875536480686695, 'number': 577} | 0.8861 | 0.9029 | 0.8944 | 0.9692 |
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- | 0.0072 | 13.0 | 234 | 0.1528 | {'precision': 0.8830508474576271, 'recall': 0.902946273830156, 'f1': 0.8928877463581834, 'number': 577} | {'precision': 0.8711864406779661, 'recall': 0.8908145580589255, 'f1': 0.8808911739502999, 'number': 577} | 0.8771 | 0.8969 | 0.8869 | 0.9670 |
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- | 0.0061 | 14.0 | 252 | 0.1552 | {'precision': 0.8779661016949153, 'recall': 0.8977469670710572, 'f1': 0.8877463581833762, 'number': 577} | {'precision': 0.8661016949152542, 'recall': 0.8856152512998267, 'f1': 0.8757497857754927, 'number': 577} | 0.8720 | 0.8917 | 0.8817 | 0.9664 |
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- | 0.0054 | 15.0 | 270 | 0.1523 | {'precision': 0.8811544991511036, 'recall': 0.8994800693240901, 'f1': 0.8902229845626072, 'number': 577} | {'precision': 0.8675721561969439, 'recall': 0.8856152512998267, 'f1': 0.8765008576329331, 'number': 577} | 0.8744 | 0.8925 | 0.8834 | 0.9664 |
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  ### Framework versions
 
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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 None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.1118
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+ - Noise: {'precision': 0.8832116788321168, 'recall': 0.8832116788321168, 'f1': 0.8832116788321168, 'number': 548}
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+ - Signal: {'precision': 0.8594890510948905, 'recall': 0.8594890510948905, 'f1': 0.8594890510948904, 'number': 548}
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+ - Overall Precision: 0.8714
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+ - Overall Recall: 0.8714
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+ - Overall F1: 0.8714
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+ - Overall Accuracy: 0.9773
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  ## Model description
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Noise | Signal | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
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+ | 0.4739 | 1.0 | 18 | 0.1915 | {'precision': 0.6647398843930635, 'recall': 0.6295620437956204, 'f1': 0.6466729147141518, 'number': 548} | {'precision': 0.6782273603082851, 'recall': 0.6423357664233577, 'f1': 0.6597938144329897, 'number': 548} | 0.6715 | 0.6359 | 0.6532 | 0.9293 |
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+ | 0.188 | 2.0 | 36 | 0.1127 | {'precision': 0.8265107212475633, 'recall': 0.7737226277372263, 'f1': 0.7992459943449576, 'number': 548} | {'precision': 0.7953216374269005, 'recall': 0.7445255474452555, 'f1': 0.769085768143261, 'number': 548} | 0.8109 | 0.7591 | 0.7842 | 0.9579 |
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+ | 0.1052 | 3.0 | 54 | 0.0889 | {'precision': 0.8455743879472694, 'recall': 0.8193430656934306, 'f1': 0.8322520852641334, 'number': 548} | {'precision': 0.8248587570621468, 'recall': 0.7992700729927007, 'f1': 0.8118628359592215, 'number': 548} | 0.8352 | 0.8093 | 0.8221 | 0.9674 |
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+ | 0.0645 | 4.0 | 72 | 0.0766 | {'precision': 0.8775510204081632, 'recall': 0.8631386861313869, 'f1': 0.8702851885924563, 'number': 548} | {'precision': 0.8552875695732839, 'recall': 0.8412408759124088, 'f1': 0.8482060717571298, 'number': 548} | 0.8664 | 0.8522 | 0.8592 | 0.9750 |
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+ | 0.0427 | 5.0 | 90 | 0.0914 | {'precision': 0.8586956521739131, 'recall': 0.864963503649635, 'f1': 0.8618181818181818, 'number': 548} | {'precision': 0.8351449275362319, 'recall': 0.8412408759124088, 'f1': 0.8381818181818181, 'number': 548} | 0.8469 | 0.8531 | 0.8500 | 0.9730 |
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+ | 0.0283 | 6.0 | 108 | 0.0987 | {'precision': 0.8756855575868373, 'recall': 0.8740875912408759, 'f1': 0.8748858447488584, 'number': 548} | {'precision': 0.8555758683729433, 'recall': 0.8540145985401459, 'f1': 0.8547945205479452, 'number': 548} | 0.8656 | 0.8641 | 0.8648 | 0.9761 |
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+ | 0.0205 | 7.0 | 126 | 0.0988 | {'precision': 0.8646209386281588, 'recall': 0.8740875912408759, 'f1': 0.8693284936479129, 'number': 548} | {'precision': 0.8375451263537906, 'recall': 0.8467153284671532, 'f1': 0.8421052631578947, 'number': 548} | 0.8511 | 0.8604 | 0.8557 | 0.9742 |
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+ | 0.0141 | 8.0 | 144 | 0.1086 | {'precision': 0.8706739526411658, 'recall': 0.8722627737226277, 'f1': 0.8714676390154968, 'number': 548} | {'precision': 0.8542805100182149, 'recall': 0.8558394160583942, 'f1': 0.8550592525068369, 'number': 548} | 0.8625 | 0.8641 | 0.8633 | 0.9753 |
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+ | 0.012 | 9.0 | 162 | 0.1076 | {'precision': 0.8811700182815356, 'recall': 0.8795620437956204, 'f1': 0.8803652968036529, 'number': 548} | {'precision': 0.8592321755027422, 'recall': 0.8576642335766423, 'f1': 0.8584474885844748, 'number': 548} | 0.8702 | 0.8686 | 0.8694 | 0.9773 |
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+ | 0.0104 | 10.0 | 180 | 0.1089 | {'precision': 0.8788990825688073, 'recall': 0.8740875912408759, 'f1': 0.8764867337602928, 'number': 548} | {'precision': 0.8568807339449541, 'recall': 0.8521897810218978, 'f1': 0.8545288197621226, 'number': 548} | 0.8679 | 0.8631 | 0.8655 | 0.9764 |
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+ | 0.0101 | 11.0 | 198 | 0.1111 | {'precision': 0.8813868613138686, 'recall': 0.8813868613138686, 'f1': 0.8813868613138687, 'number': 548} | {'precision': 0.8594890510948905, 'recall': 0.8594890510948905, 'f1': 0.8594890510948904, 'number': 548} | 0.8704 | 0.8704 | 0.8704 | 0.9761 |
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+ | 0.008 | 12.0 | 216 | 0.1049 | {'precision': 0.886654478976234, 'recall': 0.885036496350365, 'f1': 0.8858447488584474, 'number': 548} | {'precision': 0.8665447897623401, 'recall': 0.864963503649635, 'f1': 0.8657534246575344, 'number': 548} | 0.8766 | 0.875 | 0.8758 | 0.9778 |
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+ | 0.0072 | 13.0 | 234 | 0.1094 | {'precision': 0.8775137111517367, 'recall': 0.8759124087591241, 'f1': 0.8767123287671232, 'number': 548} | {'precision': 0.8519195612431444, 'recall': 0.8503649635036497, 'f1': 0.8511415525114155, 'number': 548} | 0.8647 | 0.8631 | 0.8639 | 0.9759 |
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+ | 0.007 | 14.0 | 252 | 0.1117 | {'precision': 0.8777372262773723, 'recall': 0.8777372262773723, 'f1': 0.8777372262773723, 'number': 548} | {'precision': 0.8540145985401459, 'recall': 0.8540145985401459, 'f1': 0.8540145985401459, 'number': 548} | 0.8659 | 0.8659 | 0.8659 | 0.9764 |
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+ | 0.0084 | 15.0 | 270 | 0.1118 | {'precision': 0.8832116788321168, 'recall': 0.8832116788321168, 'f1': 0.8832116788321168, 'number': 548} | {'precision': 0.8594890510948905, 'recall': 0.8594890510948905, 'f1': 0.8594890510948904, 'number': 548} | 0.8714 | 0.8714 | 0.8714 | 0.9773 |
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  ### Framework versions
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