task_categories:token-classification task_ids:named-entity-recognition multilinguality:monolingual region:us
---
multilinguality:
- monolingual
task_categories:
- token-classification
task_ids:
- named-entity-recognition
train-eval-index:
- task: token-classification
  task_id: entity_extraction
  splits:
    train_split: train    
    eval_split: test
    val_split: validation
  col_mapping:
    tokens: tokens
    ner_tags: tags
  metrics:
    - type: seqeval
      name: seqeval
---
  
# Dataset description
  
This dataset was created for fine-tuning the model [mbert-base-cased-NER-NL-legislation-refs](https://huggingface.co/romjansen/mbert-base-cased-NER-NL-legislation-refs) and consists of 512 token long examples which each contain one or more legislation references. These examples were created from a weakly labelled corpus of Dutch case law which was scraped from [Linked Data Overheid](https://linkeddata.overheid.nl/), pre-tokenized and labelled ([biluo_tags_from_offsets](https://spacy.io/api/top-level#biluo_tags_from_offsets)) through [spaCy](https://spacy.io/) and further tokenized through applying Hugging Face's [AutoTokenizer.from_pretrained()](https://huggingface.co/docs/transformers/model_doc/auto#transformers.AutoTokenizer.from_pretrained) for [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased)'s tokenizer.