README.md

---
annotations_creators:
- expert-generated
- machine-generated
language_creators:
- found
language:
- da
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- dane
- extended|other-Danish-Universal-Dependencies-treebank
- DANSK
task_categories:
- token-classification
task_ids:
- named-entity-recognition
- part-of-speech
paperswithcode_id: dane
pretty_name: DaNE+

dataset_info:
  features:
  - name: text
    dtype: string
  - name: ents
    list:
    - name: end
      dtype: int64
    - name: label
      dtype: string
    - name: start
      dtype: int64
  - name: sents
    list:
    - name: end
      dtype: int64
    - name: start
      dtype: int64
  - name: tokens
    list:
    - name: dep
      dtype: string
    - name: end
      dtype: int64
    - name: head
      dtype: int64
    - name: id
      dtype: int64
    - name: lemma
      dtype: string
    - name: morph
      dtype: string
    - name: pos
      dtype: string
    - name: start
      dtype: int64
    - name: tag
      dtype: string
  splits:
  - name: train
    num_bytes: 7886693
    num_examples: 4383
  - name: dev
    num_bytes: 1016350
    num_examples: 564
  - name: test
    num_bytes: 991137
    num_examples: 565
  download_size: 1627548
  dataset_size: 9894180
---

# DaNE+

This is a version of [DaNE](https://huggingface.co/datasets/dane), where the original NER labels have been updated to follow the ontonotes annotation scheme. The annotation process used the model trained on the Danish dataset [DANSK](https://huggingface.co/datasets/chcaa/DANSK) for the first round of annotation and then all the discrepancies were manually reviewed and corrected by Kenneth C. Enevoldsen. A discrepancy include notably also includes newly added entities such as `PRODUCT` and `WORK_OF_ART`. Thus in practice a great deal of entities were manually reviews. If there was an uncertainty the annotation was left as it was.

The additional annotations (e.g. part-of-speech tags) stems from the Danish Dependency Treebank, however, if you wish to use these I would recommend using the latest version as this version here will likely become outdated over time.


## Process of annotation

1) Install the requirements:
```
--extra-index-url pip install prodigy -f https://{DOWNLOAD KEY}@download.prodi.gy
prodigy>=1.11.0,<2.0.0
```

2) Create outline dataset
```bash
python annotate.py
```

3) Review and correction annotation using prodigy:
Add datasets to prodigy
```bash
prodigy db-in dane reference.jsonl
prodigy db-in dane_plus_mdl_pred predictions.jsonl
```

Run review using prodigy:
```bash
prodigy review daneplus dane_plus_mdl_pred,dane  --view-id ner_manual --l NORP,CARDINAL,PRODUCT,ORGANIZATION,PERSON,WORK_OF_ART,EVENT,LAW,QUANTITY,DATE,TIME,ORDINAL,LOCATION,GPE,MONEY,PERCENT,FACILITY
```

Export the dataset:
```bash
prodigy data-to-spacy daneplus --ner daneplus --lang da -es 0
```

4) Redo the original split:

```bash
python split.py
```