---
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 the annotated version of DaNE, where the annotations are added by the model predictions using model trained of Danish dataset DANSK and then all the discrepancies are manually reviewed and corrected by Kenneth C. Enevoldsen. If there was an uncertainty the annotation was left as it was.


## 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
```