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--- |
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tags: |
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- spacy |
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- token-classification |
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language: |
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- nl |
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model-index: |
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- name: nl_pipeline |
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results: |
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- task: |
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name: NER |
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type: token-classification |
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metrics: |
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- name: NER Precision |
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type: precision |
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value: 0.6350364964 |
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- name: NER Recall |
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type: recall |
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value: 0.5878378378 |
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- name: NER F Score |
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type: f_score |
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value: 0.6105263158 |
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--- |
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| Feature | Description | |
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| --- | --- | |
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| **Name** | `nl_pipeline` | |
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| **Version** | `0.0.0` | |
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| **spaCy** | `>=3.6.1,<3.7.0` | |
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| **Default Pipeline** | `tok2vec`, `ner` | |
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| **Components** | `tok2vec`, `ner` | |
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| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) | |
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| **Sources** | n/a | |
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| **License** | n/a | |
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| **Author** | [n/a]() | |
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### Label Scheme |
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<details> |
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<summary>View label scheme (7 labels for 1 components)</summary> |
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| Component | Labels | |
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| --- | --- | |
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| **`ner`** | `BEDRAG`, `LOC`, `ORG`, `PERSOON`, `PROJECT`, `SUB`, `TIJD` | |
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</details> |
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### Accuracy |
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| Type | Score | |
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| --- | --- | |
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| `ENTS_F` | 61.05 | |
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| `ENTS_P` | 63.50 | |
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| `ENTS_R` | 58.78 | |
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| `TOK2VEC_LOSS` | 41795.01 | |
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| `NER_LOSS` | 26551.93 | |
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### Description |
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Voor meer info: https://github.com/RaThorat/my-chatbot-project |
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Prodigy (ner.manual) is gebruikt om te annoteren van entiteiten zoals: PERSOON, ORGANISATIE, PROJECT, BEDRAG, LOCATIE, TIJDSPERIODE, SUBSIDIE, PRODUCT. |
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<Annotatie> prodigy ner.manual ner_dataset nl_core_news_lg ./Data/combined_documents.txt --label PERSOON,ORG,PROJECT,BEDRAG,LOC,TIJD,SUB |
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<Training> prodigy train ./models --ner ner_dataset --lang nl --label-stats --verbose --eval-split 0.2 |
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<data>30 documenten (https://github.com/RaThorat/my-chatbot-project/tree/main/Data/txt) uit de DUS-i website gedownload, schoongemaakt, samengesteld in combined_documents.txt |
