---
tags:
- spacy
- token-classification
language:
- nl
model-index:
- name: nl_pipeline
  results:
  - task:
      name: NER
      type: token-classification
    metrics:
    - name: NER Precision
      type: precision
      value: 0.6350364964
    - name: NER Recall
      type: recall
      value: 0.5878378378
    - name: NER F Score
      type: f_score
      value: 0.6105263158
---
| Feature | Description |
| --- | --- |
| **Name** | `nl_pipeline` |
| **Version** | `0.0.0` |
| **spaCy** | `>=3.6.1,<3.7.0` |
| **Default Pipeline** | `tok2vec`, `ner` |
| **Components** | `tok2vec`, `ner` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | n/a |
| **License** | n/a |
| **Author** | [n/a]() |

### Label Scheme

<details>

<summary>View label scheme (7 labels for 1 components)</summary>

| Component | Labels |
| --- | --- |
| **`ner`** | `BEDRAG`, `LOC`, `ORG`, `PERSOON`, `PROJECT`, `SUB`, `TIJD` |

</details>

### Accuracy

| Type | Score |
| --- | --- |
| `ENTS_F` | 61.05 |
| `ENTS_P` | 63.50 |
| `ENTS_R` | 58.78 |
| `TOK2VEC_LOSS` | 41795.01 |
| `NER_LOSS` | 26551.93 |

### Description

Voor meer info:  https://github.com/RaThorat/my-chatbot-project

Prodigy (ner.manual) is gebruikt om te annoteren van entiteiten zoals: PERSOON, ORGANISATIE, PROJECT, BEDRAG, LOCATIE, TIJDSPERIODE, SUBSIDIE, PRODUCT.

<Annotatie> prodigy ner.manual ner_dataset nl_core_news_lg ./Data/combined_documents.txt --label PERSOON,ORG,PROJECT,BEDRAG,LOC,TIJD,SUB 

<Training> prodigy train ./models --ner ner_dataset --lang nl --label-stats --verbose --eval-split 0.2

<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