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Update README.md
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README.md
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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.
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- Precision: 0.
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- Recall: 0.
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- F1: 0.
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- Accuracy: 0.9261
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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 |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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| No log | 1.0 |
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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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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
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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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- lr_scheduler_type: linear
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- num_epochs: 16
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### Evaluation results
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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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### 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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