  
# Dataset description
  
This dataset was created for fine-tuning the model [robbert-base-v2-NER-NL-legislation-refs](https://huggingface.co/romjansen/robbert-base-v2-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 [pdelobelle/robbert-v2-dutch-base](https://huggingface.co/pdelobelle/robbert-v2-dutch-base)'s tokenizer.
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"}]}]}
TAGS
#task_categories-token-classification #task_ids-named-entity-recognition #multilinguality-monolingual #region-us 

  
# Dataset description
  
This dataset was created for fine-tuning the model robbert-base-v2-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, pre-tokenized and labelled (biluo_tags_from_offsets) through spaCy and further tokenized through applying Hugging Face's AutoTokenizer.from_pretrained() for pdelobelle/robbert-v2-dutch-base's tokenizer.
# Dataset description
  
This dataset was created for fine-tuning the model robbert-base-v2-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, pre-tokenized and labelled (biluo_tags_from_offsets) through spaCy and further tokenized through applying Hugging Face's AutoTokenizer.from_pretrained() for pdelobelle/robbert-v2-dutch-base's tokenizer.
TAGS
#task_categories-token-classification #task_ids-named-entity-recognition #multilinguality-monolingual #region-us 
 # Dataset description
  
This dataset was created for fine-tuning the model robbert-base-v2-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, pre-tokenized and labelled (biluo_tags_from_offsets) through spaCy and further tokenized through applying Hugging Face's AutoTokenizer.from_pretrained() for pdelobelle/robbert-v2-dutch-base's tokenizer.
passage: TAGS
#task_categories-token-classification #task_ids-named-entity-recognition #multilinguality-monolingual #region-us 
# Dataset description
  
This dataset was created for fine-tuning the model robbert-base-v2-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, pre-tokenized and labelled (biluo_tags_from_offsets) through spaCy and further tokenized through applying Hugging Face's AutoTokenizer.from_pretrained() for pdelobelle/robbert-v2-dutch-base's tokenizer.