Fixing minor things after reviews

README.md

@@ -336,7 +336,7 @@ Below follows a brief overview of the sources in the corpus along with their ind
 | [nordjyllandnews]    | Articles from the Danish Newspaper [TV2 Nord](https://www.tv2nord.dk)                                                                                                                  | News         | 37.90M      | [CC-0]                 |
 | [eur-lex-sum-da]     | The Danish subsection of EUR-lex SUM consisting of EU legislation paired with professionally written summaries                                                                         | Legal        | 31.37M      | [CC-BY-SA 4.0]         |
 | [ncc_maalfrid]       | Danish content from Norwegian institutions websites                                                                                                                                    | Web          | 29.26M      | [NLOD 2.0]             |
-| [scrape_hovedstaden] | A document collection containing guidelines and informational documents for healthcare professionals                                                                                   | Encyclopedic | 27.07M      | [CC-0]                 |
+| [scrape_hovedstaden] | Guidelines and informational documents for healthcare professionals from the Capital Region                                                                                            | Medical      | 27.07M      | [CC-0]                 |
 | [tv2r]               | Contemporary Danish newswire articles published between 2010 and 2019                                                                                                                  | News         | 21.67M      | [CC-BY-SA 4.0]         |
 | [danske-taler]       | Danish Speeches from [dansketaler.dk](https://www.dansketaler.dk)                                                                                                                      | Conversation | 8.23M       | [CC-0]                 |
 | [nota]               | The text only part of the [Nota lyd- og tekstdata](https://sprogteknologi.dk/dataset/nota-lyd-og-tekstdata) dataset                                                                    | Readaloud    | 7.30M       | [CC-0]                 |

data/scrape_hovedstaden/descriptive_stats.json

@@ -2,5 +2,5 @@
   "number_of_samples": 23996,
   "average_document_length": 3329.0515919319887,
   "number_of_tokens": 27066716,
-  "revision": "16931a4cca4569bf00335846bc85c549ea59a82a"
+  "revision": "78cc135f92c8c12ee8ba131d1a03befc5c78477d"
 }

data/scrape_hovedstaden/scrape_hovedstaden.md

@@ -1,5 +1,5 @@
 ---
-pretty_name: "Scrape fra dokumentsamling p\xE5 Vip Region Hovedstaden"
+pretty_name: "Health Hovedstaden"
 language:
 - da
 license: cc0-1.0
@@ -12,25 +12,22 @@ task_ids:
 source_datasets:
 - Den-Intelligente-Patientjournal/region_hovedstaden_text
 domains:
+- Medical
 - Encyclopedic
 ---
 
-# Dataset Card for Scrape fra dokumentsamling på Vip Region Hovedstaden
+# Dataset Card for Health Hovedstaden
 
 <!-- START-SHORT DESCRIPTION -->
-A document collection containing guidelines and informational documents for healthcare professionals.
+Guidelines and informational documents for healthcare professionals from the Capital Region
 <!-- END-SHORT DESCRIPTION -->
 
 The document collection consists of guidelines and informational documents for healthcare professionals in the Capital Region of Denmark. The documents therefore contain a number of specialized terms and concepts that are frequently used within the healthcare sector.
 
-The corpus contains 9,941,236 tokens (word separation by spaces) extracted from 15,829 documents and 8,923 tables.
-
 The corpus was created based on the texts in the document collection and has been post-processed so that the texts can be used for the development of language technology.
 
 Martin Sundahl Laursen and Thiusius R. Savarimuthu from the University of Southern Denmark have assisted the Danish Agency for Digital Government with the post-processing of the data. Read their joint paper on "Automatic Annotation of Training Data for Deep Learning Based De-identification of Narrative Clinical Text."
 
-Please note that the corpus has been developed for the purpose of language technology development and should not be used as a source of healthcare information. The documents were scraped at a specific time and will therefore not be updated with changes. In this regard, please refer to the Capital Region of Denmark's document collection.
-
 
 
 
@@ -38,7 +35,7 @@ Please note that the corpus has been developed for the purpose of language techn
 
 <!-- START-DESC-STATS -->
 - **Language**: dan, dansk, Danish
-- **Domains**: Encyclopedic
+- **Domains**: Medical, Encyclopedic
 - **Number of samples**: 24.00K
 - **Number of tokens (Llama 3)**: 27.07M
 - **Average document length (characters)**: 3329.05
@@ -76,6 +73,12 @@ An entry in the dataset consists of the following fields:
 ### Additional Processing
 
 
+
+### Unintended Uses
+
+Please note that the corpus has been developed for the purpose of language technology development and should not be used as a source of healthcare information. The documents were scraped at a specific time and will therefore not be updated with changes. In this regard, please refer to the Capital Region of Denmark's document collection.
+
+
 ### Dataset Statistics
 
 <!-- START-DATASET PLOTS -->
@@ -88,19 +91,8 @@ An entry in the dataset consists of the following fields:
 # Additional Information
 
 ## License Information
-Udgivet under en CC-0 licens.
+The dataset have been released under a CC-0 license. 
 
 ### Citation Information
 
-If you are using the data please reference the following paper:
-
-```
-@inproceedings{laursen2023automatic,
-  title={Automatic Annotation of Training Data for Deep Learning Based De-identification of Narrative Clinical Text},
-  author={Laursen, Martin Sundahl and Pedersen, Jannik Skyttegaard and Vinholt, Pernille and Savarimuthu, Thiusius R},
-  booktitle={The First Workshop on Context-aware NLP in eHealth:(WNLPe-Health 2022)},
-  pages={30--44},
-  year={2023},
-  organization={CEUR Workshop Proceedings}
-}
-```
+If you are using the data please reference the following paper [Automatic Annotation of Training Data for Deep Learning Based De-identification of Narrative Clinical Text](https://ceur-ws.org/Vol-3416/paper_5.pdf)

descriptive_stats.json

@@ -2,5 +2,5 @@
   "number_of_samples": 915090,
   "average_document_length": 14778.0072255188,
   "number_of_tokens": 4396075044,
-  "revision": "16931a4cca4569bf00335846bc85c549ea59a82a"
+  "revision": "78cc135f92c8c12ee8ba131d1a03befc5c78477d"
 }

src/dynaword/typings.py

@@ -6,6 +6,7 @@ DOMAIN = Literal[
     "Dialect",
     "Encyclopedic",
     "Legal",
+    "Medical",
     "News",
     "Other",
     "Readaloud",

test_results.log

@@ -1,25 +1,24 @@
 ============================= test session starts ==============================
-platform darwin -- Python 3.12.0, pytest-8.3.4, pluggy-1.5.0
-rootdir: /Users/au561649/Github/danish-dynaword
+platform darwin -- Python 3.12.9, pytest-8.3.4, pluggy-1.5.0
+rootdir: /Users/kristianjensen/Documents/danish-dynaword
 configfile: pyproject.toml
-plugins: anyio-4.9.0
-collected 310 items
+collected 319 items
 
 src/tests/test_dataset_schema.py ....................................... [ 12%]
-.............................                                            [ 21%]
+...............................                                          [ 21%]
 src/tests/test_datasheets.py ........................................... [ 35%]
-........................................................................ [ 59%]
-.......................................................                  [ 76%]
+........................................................................ [ 57%]
+............................................................             [ 76%]
 src/tests/test_load.py ..                                                [ 77%]
-src/tests/test_quality/test_duplicates.py .............................. [ 87%]
-....s                                                                    [ 88%]
-src/tests/test_quality/test_short_texts.py ............................. [ 98%]
-.....                                                                    [ 99%]
+src/tests/test_quality/test_duplicates.py .............................. [ 86%]
+.....s                                                                   [ 88%]
+src/tests/test_quality/test_short_texts.py ............................. [ 97%]
+......                                                                   [ 99%]
 src/tests/test_unique_ids.py .                                           [100%]
 
 =============================== warnings summary ===============================
-src/tests/test_quality/test_short_texts.py: 34 warnings
-  /Users/au561649/Github/danish-dynaword/.venv/lib/python3.12/site-packages/datasets/utils/_dill.py:385: DeprecationWarning: co_lnotab is deprecated, use co_lines instead.
+src/tests/test_quality/test_short_texts.py: 35 warnings
+  /Users/kristianjensen/Documents/danish-dynaword/.venv/lib/python3.12/site-packages/datasets/utils/_dill.py:385: DeprecationWarning: co_lnotab is deprecated, use co_lines instead.
 
 -- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
-============ 309 passed, 1 skipped, 34 warnings in 77.84s (0:01:17) ============
+================= 318 passed, 1 skipped, 35 warnings in 28.20s =================