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denizspynk
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req_mod_ner_modelv2

Token Classification
Transformers
PyTorch
Dutch
roberta
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update model card README.md

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@@ -10,12 +10,6 @@ metrics:
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  model-index:
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  - name: req_mod_ner_modelv2
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  results: []
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- widget:
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- - text: "De Oplossing ondersteunt het zoeken op de metadata van zaken, documenten en objecten en op gegevens uit de basisregistraties die gekoppeld zijn aan een zaak."
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- - text: "De Oplossing ondersteunt parafering en het plaatsen van een gecertificeerde elektronische handtekening."
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- - text: "De Aangeboden oplossing stelt de medewerker in staat een zaak te registreren."
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- - text: "Het Financieel systeem heeft functionaliteit om een debiteurenadministratie te voeren."
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- - text: "Als gebruiker wil ik dat de oplossing mij naar zaken laat zoeken op basis van zaaknummer, zaaktitel, omschrijving en datum."
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -25,11 +19,12 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [pdelobelle/robbert-v2-dutch-ner](https://huggingface.co/pdelobelle/robbert-v2-dutch-ner) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.6678
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- - Precision: 0.7090
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- - Recall: 0.7701
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- - F1: 0.7383
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- - Accuracy: 0.9261
 
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  ## Model description
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@@ -56,33 +51,27 @@ The following hyperparameters were used during training:
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  - lr_scheduler_type: linear
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  - num_epochs: 16
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- ### Evaluation results
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-
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- | Validation Loss | Precision | Recall | F1 | Accuracy |
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- |:---------------:|:---------:|:------:|:------:|:--------:|
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- | 0.6678 | 0.7090 | 0.7701 | 0.7383 | 0.9261 |
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-
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-
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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- |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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- | No log | 1.0 | 240 | 0.4780 | 0.3456 | 0.4052 | 0.3730 | 0.8789 |
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- | No log | 2.0 | 480 | 0.3903 | 0.5934 | 0.4655 | 0.5217 | 0.9080 |
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- | 0.4168 | 3.0 | 720 | 0.5082 | 0.6782 | 0.5086 | 0.5813 | 0.9169 |
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- | 0.4168 | 4.0 | 960 | 0.4307 | 0.5846 | 0.6552 | 0.6179 | 0.9201 |
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- | 0.1633 | 5.0 | 1200 | 0.5179 | 0.6 | 0.5948 | 0.5974 | 0.9233 |
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- | 0.1633 | 6.0 | 1440 | 0.6073 | 0.5752 | 0.5603 | 0.5677 | 0.9185 |
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- | 0.0676 | 7.0 | 1680 | 0.6198 | 0.6638 | 0.6638 | 0.6638 | 0.9233 |
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- | 0.0676 | 8.0 | 1920 | 0.6876 | 0.6311 | 0.6638 | 0.6471 | 0.9185 |
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- | 0.0445 | 9.0 | 2160 | 0.7112 | 0.6522 | 0.6466 | 0.6494 | 0.9201 |
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- | 0.0445 | 10.0 | 2400 | 0.7232 | 0.6522 | 0.6466 | 0.6494 | 0.9193 |
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- | 0.0259 | 11.0 | 2640 | 0.6511 | 0.6371 | 0.6810 | 0.6583 | 0.9233 |
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- | 0.0259 | 12.0 | 2880 | 0.6733 | 0.6783 | 0.6724 | 0.6753 | 0.9257 |
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- | 0.0146 | 13.0 | 3120 | 0.6636 | 0.6695 | 0.6810 | 0.6752 | 0.9282 |
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- | 0.0146 | 14.0 | 3360 | 0.6943 | 0.6496 | 0.6552 | 0.6524 | 0.9257 |
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- | 0.0134 | 15.0 | 3600 | 0.7055 | 0.6552 | 0.6552 | 0.6552 | 0.9257 |
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- | 0.0134 | 16.0 | 3840 | 0.7115 | 0.6522 | 0.6466 | 0.6494 | 0.9249 |
 
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  ### Framework versions
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  model-index:
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  - name: req_mod_ner_modelv2
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  results: []
 
 
 
 
 
 
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
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  This model is a fine-tuned version of [pdelobelle/robbert-v2-dutch-ner](https://huggingface.co/pdelobelle/robbert-v2-dutch-ner) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.6908
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+ - Precision: 0.7412
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+ - Recall: 0.7241
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+ - F1: 0.7326
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+ - Accuracy: 0.9238
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+ - Alles: {'Actor': {'precision': 0.8571428571428571, 'recall': 1.0, 'f1': 0.923076923076923, 'number': 12}, 'Attribute': {'precision': 0.8333333333333334, 'recall': 0.6451612903225806, 'f1': 0.7272727272727272, 'number': 31}, 'COTS': {'precision': 0.7777777777777778, 'recall': 0.875, 'f1': 0.823529411764706, 'number': 24}, 'Entity': {'precision': 0.7567567567567568, 'recall': 0.8, 'f1': 0.7777777777777778, 'number': 35}, 'Function': {'precision': 0.7454545454545455, 'recall': 0.6612903225806451, 'f1': 0.7008547008547009, 'number': 62}, 'Result': {'precision': 0.3076923076923077, 'recall': 0.4, 'f1': 0.34782608695652173, 'number': 10}, 'overall_precision': 0.7411764705882353, 'overall_recall': 0.7241379310344828, 'overall_f1': 0.7325581395348838, 'overall_accuracy': 0.9237947122861586}
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  ## Model description
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  - lr_scheduler_type: linear
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  - num_epochs: 16
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | Alles |
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+ |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
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+ | No log | 1.0 | 270 | 0.5418 | 0.6065 | 0.5402 | 0.5714 | 0.8802 | {'Actor': {'precision': 0.75, 'recall': 0.75, 'f1': 0.75, 'number': 12}, 'Attribute': {'precision': 0.4642857142857143, 'recall': 0.41935483870967744, 'f1': 0.44067796610169496, 'number': 31}, 'COTS': {'precision': 0.7241379310344828, 'recall': 0.875, 'f1': 0.7924528301886793, 'number': 24}, 'Entity': {'precision': 0.49019607843137253, 'recall': 0.7142857142857143, 'f1': 0.5813953488372093, 'number': 35}, 'Function': {'precision': 0.7428571428571429, 'recall': 0.41935483870967744, 'f1': 0.5360824742268042, 'number': 62}, 'Result': {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10}, 'overall_precision': 0.6064516129032258, 'overall_recall': 0.5402298850574713, 'overall_f1': 0.5714285714285714, 'overall_accuracy': 0.880248833592535} |
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+ | 0.5551 | 2.0 | 540 | 0.4299 | 0.5481 | 0.6552 | 0.5969 | 0.8896 | {'Actor': {'precision': 0.8, 'recall': 0.6666666666666666, 'f1': 0.7272727272727272, 'number': 12}, 'Attribute': {'precision': 0.6666666666666666, 'recall': 0.45161290322580644, 'f1': 0.5384615384615384, 'number': 31}, 'COTS': {'precision': 0.7241379310344828, 'recall': 0.875, 'f1': 0.7924528301886793, 'number': 24}, 'Entity': {'precision': 0.5294117647058824, 'recall': 0.7714285714285715, 'f1': 0.627906976744186, 'number': 35}, 'Function': {'precision': 0.4777777777777778, 'recall': 0.6935483870967742, 'f1': 0.5657894736842105, 'number': 62}, 'Result': {'precision': 0.14285714285714285, 'recall': 0.1, 'f1': 0.11764705882352941, 'number': 10}, 'overall_precision': 0.5480769230769231, 'overall_recall': 0.6551724137931034, 'overall_f1': 0.5968586387434556, 'overall_accuracy': 0.8895800933125972} |
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+ | 0.5551 | 3.0 | 810 | 0.4987 | 0.6358 | 0.5517 | 0.5908 | 0.9020 | {'Actor': {'precision': 0.5294117647058824, 'recall': 0.75, 'f1': 0.6206896551724139, 'number': 12}, 'Attribute': {'precision': 0.65625, 'recall': 0.6774193548387096, 'f1': 0.6666666666666667, 'number': 31}, 'COTS': {'precision': 0.7142857142857143, 'recall': 0.8333333333333334, 'f1': 0.7692307692307692, 'number': 24}, 'Entity': {'precision': 0.9333333333333333, 'recall': 0.4, 'f1': 0.5599999999999999, 'number': 35}, 'Function': {'precision': 0.5384615384615384, 'recall': 0.45161290322580644, 'f1': 0.4912280701754386, 'number': 62}, 'Result': {'precision': 0.5714285714285714, 'recall': 0.4, 'f1': 0.47058823529411764, 'number': 10}, 'overall_precision': 0.6357615894039735, 'overall_recall': 0.5517241379310345, 'overall_f1': 0.5907692307692308, 'overall_accuracy': 0.9020217729393468} |
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+ | 0.1935 | 4.0 | 1080 | 0.5620 | 0.6159 | 0.4885 | 0.5449 | 0.8935 | {'Actor': {'precision': 0.8181818181818182, 'recall': 0.75, 'f1': 0.7826086956521738, 'number': 12}, 'Attribute': {'precision': 0.7222222222222222, 'recall': 0.41935483870967744, 'f1': 0.5306122448979592, 'number': 31}, 'COTS': {'precision': 0.7692307692307693, 'recall': 0.8333333333333334, 'f1': 0.8, 'number': 24}, 'Entity': {'precision': 0.6428571428571429, 'recall': 0.5142857142857142, 'f1': 0.5714285714285714, 'number': 35}, 'Function': {'precision': 0.5121951219512195, 'recall': 0.3387096774193548, 'f1': 0.4077669902912621, 'number': 62}, 'Result': {'precision': 0.2857142857142857, 'recall': 0.4, 'f1': 0.3333333333333333, 'number': 10}, 'overall_precision': 0.6159420289855072, 'overall_recall': 0.4885057471264368, 'overall_f1': 0.5448717948717948, 'overall_accuracy': 0.8934681181959565} |
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+ | 0.1935 | 5.0 | 1350 | 0.4922 | 0.6786 | 0.6552 | 0.6667 | 0.9121 | {'Actor': {'precision': 0.8571428571428571, 'recall': 1.0, 'f1': 0.923076923076923, 'number': 12}, 'Attribute': {'precision': 0.7916666666666666, 'recall': 0.6129032258064516, 'f1': 0.6909090909090909, 'number': 31}, 'COTS': {'precision': 0.7407407407407407, 'recall': 0.8333333333333334, 'f1': 0.7843137254901961, 'number': 24}, 'Entity': {'precision': 0.7714285714285715, 'recall': 0.7714285714285715, 'f1': 0.7714285714285715, 'number': 35}, 'Function': {'precision': 0.5517241379310345, 'recall': 0.5161290322580645, 'f1': 0.5333333333333333, 'number': 62}, 'Result': {'precision': 0.4, 'recall': 0.4, 'f1': 0.4000000000000001, 'number': 10}, 'overall_precision': 0.6785714285714286, 'overall_recall': 0.6551724137931034, 'overall_f1': 0.6666666666666666, 'overall_accuracy': 0.9121306376360808} |
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+ | 0.0913 | 6.0 | 1620 | 0.5406 | 0.6087 | 0.5632 | 0.5851 | 0.8950 | {'Actor': {'precision': 0.8461538461538461, 'recall': 0.9166666666666666, 'f1': 0.8799999999999999, 'number': 12}, 'Attribute': {'precision': 0.8636363636363636, 'recall': 0.6129032258064516, 'f1': 0.7169811320754716, 'number': 31}, 'COTS': {'precision': 0.7692307692307693, 'recall': 0.8333333333333334, 'f1': 0.8, 'number': 24}, 'Entity': {'precision': 0.7619047619047619, 'recall': 0.45714285714285713, 'f1': 0.5714285714285714, 'number': 35}, 'Function': {'precision': 0.42857142857142855, 'recall': 0.43548387096774194, 'f1': 0.432, 'number': 62}, 'Result': {'precision': 0.3125, 'recall': 0.5, 'f1': 0.38461538461538464, 'number': 10}, 'overall_precision': 0.6086956521739131, 'overall_recall': 0.5632183908045977, 'overall_f1': 0.5850746268656717, 'overall_accuracy': 0.8950233281493002} |
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+ | 0.0913 | 7.0 | 1890 | 0.6307 | 0.7425 | 0.7126 | 0.7273 | 0.9222 | {'Actor': {'precision': 0.8571428571428571, 'recall': 1.0, 'f1': 0.923076923076923, 'number': 12}, 'Attribute': {'precision': 0.65625, 'recall': 0.6774193548387096, 'f1': 0.6666666666666667, 'number': 31}, 'COTS': {'precision': 0.7692307692307693, 'recall': 0.8333333333333334, 'f1': 0.8, 'number': 24}, 'Entity': {'precision': 0.7567567567567568, 'recall': 0.8, 'f1': 0.7777777777777778, 'number': 35}, 'Function': {'precision': 0.7647058823529411, 'recall': 0.6290322580645161, 'f1': 0.6902654867256637, 'number': 62}, 'Result': {'precision': 0.5714285714285714, 'recall': 0.4, 'f1': 0.47058823529411764, 'number': 10}, 'overall_precision': 0.7425149700598802, 'overall_recall': 0.7126436781609196, 'overall_f1': 0.7272727272727273, 'overall_accuracy': 0.9222395023328149} |
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+ | 0.0702 | 8.0 | 2160 | 0.4425 | 0.6684 | 0.7414 | 0.7030 | 0.9277 | {'Actor': {'precision': 0.75, 'recall': 1.0, 'f1': 0.8571428571428571, 'number': 12}, 'Attribute': {'precision': 0.8076923076923077, 'recall': 0.6774193548387096, 'f1': 0.7368421052631579, 'number': 31}, 'COTS': {'precision': 0.8636363636363636, 'recall': 0.7916666666666666, 'f1': 0.8260869565217391, 'number': 24}, 'Entity': {'precision': 0.6590909090909091, 'recall': 0.8285714285714286, 'f1': 0.7341772151898734, 'number': 35}, 'Function': {'precision': 0.56, 'recall': 0.6774193548387096, 'f1': 0.613138686131387, 'number': 62}, 'Result': {'precision': 0.6, 'recall': 0.6, 'f1': 0.6, 'number': 10}, 'overall_precision': 0.6683937823834197, 'overall_recall': 0.7413793103448276, 'overall_f1': 0.7029972752043598, 'overall_accuracy': 0.9276827371695179} |
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+ | 0.0702 | 9.0 | 2430 | 0.6028 | 0.7158 | 0.7529 | 0.7339 | 0.9285 | {'Actor': {'precision': 0.9230769230769231, 'recall': 1.0, 'f1': 0.9600000000000001, 'number': 12}, 'Attribute': {'precision': 0.7586206896551724, 'recall': 0.7096774193548387, 'f1': 0.7333333333333333, 'number': 31}, 'COTS': {'precision': 0.7777777777777778, 'recall': 0.875, 'f1': 0.823529411764706, 'number': 24}, 'Entity': {'precision': 0.7878787878787878, 'recall': 0.7428571428571429, 'f1': 0.7647058823529412, 'number': 35}, 'Function': {'precision': 0.6285714285714286, 'recall': 0.7096774193548387, 'f1': 0.6666666666666666, 'number': 62}, 'Result': {'precision': 0.5454545454545454, 'recall': 0.6, 'f1': 0.5714285714285713, 'number': 10}, 'overall_precision': 0.7158469945355191, 'overall_recall': 0.7528735632183908, 'overall_f1': 0.7338935574229692, 'overall_accuracy': 0.9284603421461898} |
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+ | 0.0472 | 10.0 | 2700 | 0.6491 | 0.7303 | 0.7471 | 0.7386 | 0.9246 | {'Actor': {'precision': 0.8571428571428571, 'recall': 1.0, 'f1': 0.923076923076923, 'number': 12}, 'Attribute': {'precision': 0.696969696969697, 'recall': 0.7419354838709677, 'f1': 0.71875, 'number': 31}, 'COTS': {'precision': 0.76, 'recall': 0.7916666666666666, 'f1': 0.7755102040816326, 'number': 24}, 'Entity': {'precision': 0.8, 'recall': 0.8, 'f1': 0.8000000000000002, 'number': 35}, 'Function': {'precision': 0.7636363636363637, 'recall': 0.6774193548387096, 'f1': 0.7179487179487181, 'number': 62}, 'Result': {'precision': 0.375, 'recall': 0.6, 'f1': 0.4615384615384615, 'number': 10}, 'overall_precision': 0.7303370786516854, 'overall_recall': 0.7471264367816092, 'overall_f1': 0.7386363636363638, 'overall_accuracy': 0.9245723172628305} |
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+ | 0.0472 | 11.0 | 2970 | 0.6442 | 0.7198 | 0.7529 | 0.7360 | 0.9292 | {'Actor': {'precision': 0.9230769230769231, 'recall': 1.0, 'f1': 0.9600000000000001, 'number': 12}, 'Attribute': {'precision': 0.8, 'recall': 0.6451612903225806, 'f1': 0.7142857142857142, 'number': 31}, 'COTS': {'precision': 0.7586206896551724, 'recall': 0.9166666666666666, 'f1': 0.830188679245283, 'number': 24}, 'Entity': {'precision': 0.8235294117647058, 'recall': 0.8, 'f1': 0.8115942028985507, 'number': 35}, 'Function': {'precision': 0.6176470588235294, 'recall': 0.6774193548387096, 'f1': 0.6461538461538462, 'number': 62}, 'Result': {'precision': 0.5384615384615384, 'recall': 0.7, 'f1': 0.608695652173913, 'number': 10}, 'overall_precision': 0.7197802197802198, 'overall_recall': 0.7528735632183908, 'overall_f1': 0.7359550561797753, 'overall_accuracy': 0.9292379471228616} |
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+ | 0.0305 | 12.0 | 3240 | 0.5980 | 0.7412 | 0.7241 | 0.7326 | 0.9230 | {'Actor': {'precision': 0.8571428571428571, 'recall': 1.0, 'f1': 0.923076923076923, 'number': 12}, 'Attribute': {'precision': 0.8260869565217391, 'recall': 0.6129032258064516, 'f1': 0.7037037037037037, 'number': 31}, 'COTS': {'precision': 0.7692307692307693, 'recall': 0.8333333333333334, 'f1': 0.8, 'number': 24}, 'Entity': {'precision': 0.7631578947368421, 'recall': 0.8285714285714286, 'f1': 0.7945205479452055, 'number': 35}, 'Function': {'precision': 0.7241379310344828, 'recall': 0.6774193548387096, 'f1': 0.7, 'number': 62}, 'Result': {'precision': 0.36363636363636365, 'recall': 0.4, 'f1': 0.380952380952381, 'number': 10}, 'overall_precision': 0.7411764705882353, 'overall_recall': 0.7241379310344828, 'overall_f1': 0.7325581395348838, 'overall_accuracy': 0.9230171073094868} |
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+ | 0.0209 | 13.0 | 3510 | 0.6186 | 0.7232 | 0.7356 | 0.7293 | 0.9238 | {'Actor': {'precision': 0.8571428571428571, 'recall': 1.0, 'f1': 0.923076923076923, 'number': 12}, 'Attribute': {'precision': 0.8181818181818182, 'recall': 0.5806451612903226, 'f1': 0.679245283018868, 'number': 31}, 'COTS': {'precision': 0.8, 'recall': 0.8333333333333334, 'f1': 0.816326530612245, 'number': 24}, 'Entity': {'precision': 0.7631578947368421, 'recall': 0.8285714285714286, 'f1': 0.7945205479452055, 'number': 35}, 'Function': {'precision': 0.7213114754098361, 'recall': 0.7096774193548387, 'f1': 0.7154471544715446, 'number': 62}, 'Result': {'precision': 0.29411764705882354, 'recall': 0.5, 'f1': 0.37037037037037035, 'number': 10}, 'overall_precision': 0.7231638418079096, 'overall_recall': 0.735632183908046, 'overall_f1': 0.7293447293447294, 'overall_accuracy': 0.9237947122861586} |
71
+ | 0.0209 | 14.0 | 3780 | 0.6791 | 0.7515 | 0.7299 | 0.7405 | 0.9253 | {'Actor': {'precision': 0.8571428571428571, 'recall': 1.0, 'f1': 0.923076923076923, 'number': 12}, 'Attribute': {'precision': 0.9166666666666666, 'recall': 0.7096774193548387, 'f1': 0.7999999999999999, 'number': 31}, 'COTS': {'precision': 0.7916666666666666, 'recall': 0.7916666666666666, 'f1': 0.7916666666666666, 'number': 24}, 'Entity': {'precision': 0.7837837837837838, 'recall': 0.8285714285714286, 'f1': 0.8055555555555555, 'number': 35}, 'Function': {'precision': 0.7321428571428571, 'recall': 0.6612903225806451, 'f1': 0.6949152542372881, 'number': 62}, 'Result': {'precision': 0.2857142857142857, 'recall': 0.4, 'f1': 0.3333333333333333, 'number': 10}, 'overall_precision': 0.7514792899408284, 'overall_recall': 0.7298850574712644, 'overall_f1': 0.7405247813411079, 'overall_accuracy': 0.9253499222395023} |
72
+ | 0.0148 | 15.0 | 4050 | 0.6832 | 0.7283 | 0.7241 | 0.7262 | 0.9238 | {'Actor': {'precision': 0.8571428571428571, 'recall': 1.0, 'f1': 0.923076923076923, 'number': 12}, 'Attribute': {'precision': 0.8333333333333334, 'recall': 0.6451612903225806, 'f1': 0.7272727272727272, 'number': 31}, 'COTS': {'precision': 0.7777777777777778, 'recall': 0.875, 'f1': 0.823529411764706, 'number': 24}, 'Entity': {'precision': 0.7567567567567568, 'recall': 0.8, 'f1': 0.7777777777777778, 'number': 35}, 'Function': {'precision': 0.7192982456140351, 'recall': 0.6612903225806451, 'f1': 0.689075630252101, 'number': 62}, 'Result': {'precision': 0.2857142857142857, 'recall': 0.4, 'f1': 0.3333333333333333, 'number': 10}, 'overall_precision': 0.7283236994219653, 'overall_recall': 0.7241379310344828, 'overall_f1': 0.7262247838616714, 'overall_accuracy': 0.9237947122861586} |
73
+ | 0.0148 | 16.0 | 4320 | 0.6908 | 0.7412 | 0.7241 | 0.7326 | 0.9238 | {'Actor': {'precision': 0.8571428571428571, 'recall': 1.0, 'f1': 0.923076923076923, 'number': 12}, 'Attribute': {'precision': 0.8333333333333334, 'recall': 0.6451612903225806, 'f1': 0.7272727272727272, 'number': 31}, 'COTS': {'precision': 0.7777777777777778, 'recall': 0.875, 'f1': 0.823529411764706, 'number': 24}, 'Entity': {'precision': 0.7567567567567568, 'recall': 0.8, 'f1': 0.7777777777777778, 'number': 35}, 'Function': {'precision': 0.7454545454545455, 'recall': 0.6612903225806451, 'f1': 0.7008547008547009, 'number': 62}, 'Result': {'precision': 0.3076923076923077, 'recall': 0.4, 'f1': 0.34782608695652173, 'number': 10}, 'overall_precision': 0.7411764705882353, 'overall_recall': 0.7241379310344828, 'overall_f1': 0.7325581395348838, 'overall_accuracy': 0.9237947122861586} |
74
+
75
 
76
  ### Framework versions
77