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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: req_mod_ner_modelv2 |
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results: [] |
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language: |
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- nl |
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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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--- |
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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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should probably proofread and complete it, then remove this comment. --> |
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# req_mod_ner_modelv2 |
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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: 0.6964 |
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- Precision: 0.544 |
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- Recall: 0.5862 |
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- F1: 0.5643 |
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- Accuracy: 0.9153 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 2 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 32 |
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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 | 120 | 0.6075 | 0.8095 | 0.1466 | 0.2482 | 0.8822 | |
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| No log | 2.0 | 240 | 0.4917 | 0.6667 | 0.1897 | 0.2953 | 0.8878 | |
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| No log | 3.0 | 360 | 0.4429 | 0.5 | 0.3362 | 0.4021 | 0.8918 | |
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| No log | 4.0 | 480 | 0.4255 | 0.5 | 0.4914 | 0.4957 | 0.9007 | |
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| 0.507 | 5.0 | 600 | 0.4278 | 0.5085 | 0.5172 | 0.5128 | 0.9007 | |
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| 0.507 | 6.0 | 720 | 0.4321 | 0.5294 | 0.5431 | 0.5362 | 0.9064 | |
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| 0.507 | 7.0 | 840 | 0.4574 | 0.5410 | 0.5690 | 0.5546 | 0.9064 | |
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| 0.507 | 8.0 | 960 | 0.4720 | 0.5804 | 0.5603 | 0.5702 | 0.9096 | |
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| 0.1626 | 9.0 | 1080 | 0.4947 | 0.5197 | 0.5690 | 0.5432 | 0.9056 | |
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| 0.1626 | 10.0 | 1200 | 0.5013 | 0.5159 | 0.5603 | 0.5372 | 0.9096 | |
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| 0.1626 | 11.0 | 1320 | 0.5306 | 0.5271 | 0.5862 | 0.5551 | 0.9104 | |
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| 0.1626 | 12.0 | 1440 | 0.5450 | 0.5070 | 0.6207 | 0.5581 | 0.9112 | |
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| 0.0687 | 13.0 | 1560 | 0.5753 | 0.5152 | 0.5862 | 0.5484 | 0.9112 | |
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| 0.0687 | 14.0 | 1680 | 0.5746 | 0.5547 | 0.6121 | 0.5820 | 0.9169 | |
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| 0.0687 | 15.0 | 1800 | 0.5925 | 0.5328 | 0.6293 | 0.5771 | 0.9144 | |
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| 0.0687 | 16.0 | 1920 | 0.6200 | 0.5656 | 0.5948 | 0.5798 | 0.9144 | |
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| 0.0368 | 17.0 | 2040 | 0.6442 | 0.5583 | 0.5776 | 0.5678 | 0.9169 | |
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| 0.0368 | 18.0 | 2160 | 0.6468 | 0.5317 | 0.5776 | 0.5537 | 0.9136 | |
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| 0.0368 | 19.0 | 2280 | 0.6563 | 0.5403 | 0.5776 | 0.5583 | 0.9153 | |
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| 0.0368 | 20.0 | 2400 | 0.6683 | 0.5323 | 0.5690 | 0.5500 | 0.9104 | |
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| 0.0227 | 21.0 | 2520 | 0.6766 | 0.5074 | 0.5948 | 0.5476 | 0.9096 | |
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| 0.0227 | 22.0 | 2640 | 0.6784 | 0.4965 | 0.6121 | 0.5483 | 0.9072 | |
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| 0.0227 | 23.0 | 2760 | 0.6897 | 0.5583 | 0.5776 | 0.5678 | 0.9144 | |
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| 0.0227 | 24.0 | 2880 | 0.6858 | 0.5182 | 0.6121 | 0.5613 | 0.9112 | |
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| 0.0146 | 25.0 | 3000 | 0.6828 | 0.5224 | 0.6034 | 0.5600 | 0.9128 | |
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| 0.0146 | 26.0 | 3120 | 0.6937 | 0.5528 | 0.5862 | 0.5690 | 0.9169 | |
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| 0.0146 | 27.0 | 3240 | 0.6939 | 0.5397 | 0.5862 | 0.5620 | 0.9144 | |
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| 0.0146 | 28.0 | 3360 | 0.6934 | 0.5476 | 0.5948 | 0.5702 | 0.9169 | |
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| 0.0146 | 29.0 | 3480 | 0.6848 | 0.5147 | 0.6034 | 0.5556 | 0.9120 | |
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| 0.0132 | 30.0 | 3600 | 0.6864 | 0.5231 | 0.5862 | 0.5528 | 0.9112 | |
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| 0.0132 | 31.0 | 3720 | 0.6948 | 0.544 | 0.5862 | 0.5643 | 0.9161 | |
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| 0.0132 | 32.0 | 3840 | 0.6964 | 0.544 | 0.5862 | 0.5643 | 0.9153 | |
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### Framework versions |
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- Transformers 4.24.0 |
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- Pytorch 2.0.0 |
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- Datasets 2.9.0 |
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- Tokenizers 0.11.0 |
