Datasets:

Modalities:
Image
Text
Formats:
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Languages:
Danish
ArXiv:
DOI:
Libraries:
Datasets
Dask
License:
Kenneth Enevoldsen commited on
Commit
9e230b3
·
unverified ·
1 Parent(s): e688db6

fix: reformat license for Historical Danish Handwriting for consistency

Browse files
README.md CHANGED
@@ -385,7 +385,7 @@ These license is applied to the constituent data, i.e., the text. The collection
385
  | CC-0 | [grundtvig], [danske-taler], [ncc_books], [ncc_newspaper], [miljoeportalen], [opensubtitles], [ep], [ft], [wikisource], [spont], [adl], [hest], [skat], [retspraksis], [wikibooks], [botxt], [naat], [synne], [wikipedia], [wiki-comments], [nordjyllandnews], [relig], [nota], [health_hovedstaden] | 2.06B |
386
  | Other (No attribution required) | [retsinformationdk], [domsdatabasen] | 904.61M |
387
  | Other (Attribution required) | [ai-aktindsigt], [ncc_maalfrid], [ncc_parliament], [dannet], [gutenberg] | 515.61M |
388
- | CC by 4.0 | [historical-danish-handwriting] | 5.20M |
389
  | **Total** | | 5.89B |
390
 
391
  [ai-aktindsigt]: data/ai-aktindsigt/ai-aktindsigt.md
@@ -443,12 +443,12 @@ Each entry in the dataset consists of a single text with associated metadata
443
  <!-- START-SAMPLE -->
444
  ```py
445
  {
446
- "id": "enevaeldens_nyheder_aalborg1767_1767-01-02_1000001",
447
- "text": "Et Menneske er skabt ey for sig selv allene: Hvert Lem paa Legemet det heele tiene maae, En Stolpes [...]",
448
- "source": "enevaeldens_nyheder",
449
- "added": "2025-08-05",
450
- "created": "1767-01-02, 1767-01-02",
451
- "token_count": 2377
452
  }
453
  ```
454
 
@@ -523,7 +523,7 @@ You can learn more about each dataset by pressing the link in the first column.
523
  | [gutenberg] | The Danish subsection from Project [Gutenberg](https://www.gutenberg.org) | Books | 6.76M | [Gutenberg] |
524
  | [wikisource] | The Danish subsection of [Wikisource](https://en.wikisource.org/wiki/Main_Page) | Encyclopedic | 6.28M | [CC-0] |
525
  | [wiki-comments] | Text from the comments sections of the Danish Wikipedia | Encyclopedic | 6.14M | [CC-0] |
526
- | [historical-danish-handwriting] | Minutes from City and Parish Council meetings between 1841 and 1939 from [The Historical Danish handwriting dataset](https://huggingface.co/datasets/aarhus-city-archives/historical-danish-handwriting) | Other | 5.20M | [CC by 4.0] |
527
  | [jvj] | The works of the Danish author and poet, [Johannes V. Jensen](https://da.wikipedia.org/wiki/Johannes_V._Jensen) | Books | 3.55M | [CC-BY-SA 4.0] |
528
  | [spont] | Conversational samples collected as a part of research projects at Aarhus University | Conversation | 1.56M | [CC-0] |
529
  | [dannet] | [DanNet](https://cst.ku.dk/projekter/dannet) is a Danish WordNet | Other | 1.48M | [DanNet 1.0] |
@@ -579,6 +579,7 @@ You can learn more about each dataset by pressing the link in the first column.
579
 
580
  [CC-0]: https://creativecommons.org/publicdomain/zero/1.0/legalcode.en
581
  [CC-BY-SA 4.0]: https://creativecommons.org/licenses/by-sa/4.0/deed.en
 
582
  [Apache 2.0]: https://www.apache.org/licenses/LICENSE-2.0
583
  [NLOD 2.0]: ./data/ncc_maalfrid/ncc_maalfrid.md#license-information
584
  [NLOD 2.0]: ./data/ncc_parliament/ncc_parliament.md#license-information
@@ -586,7 +587,6 @@ You can learn more about each dataset by pressing the link in the first column.
586
  [DanNet 1.0]: ./data/dannet/dannet.md#license-information
587
  [Gutenberg]: ./data/gutenberg/gutenberg.md#license-information
588
  [Danish Copyright Law]: ./data/domsdatabasen/domsdatabasen.md#license-information
589
- [CC by 4.0]: ./data/historical-danish-handwriting/historical-danish-handwriting.md#license-information
590
  <!-- END-MAIN TABLE -->
591
 
592
  </details>
 
385
  | CC-0 | [grundtvig], [danske-taler], [ncc_books], [ncc_newspaper], [miljoeportalen], [opensubtitles], [ep], [ft], [wikisource], [spont], [adl], [hest], [skat], [retspraksis], [wikibooks], [botxt], [naat], [synne], [wikipedia], [wiki-comments], [nordjyllandnews], [relig], [nota], [health_hovedstaden] | 2.06B |
386
  | Other (No attribution required) | [retsinformationdk], [domsdatabasen] | 904.61M |
387
  | Other (Attribution required) | [ai-aktindsigt], [ncc_maalfrid], [ncc_parliament], [dannet], [gutenberg] | 515.61M |
388
+ | CC-BY 4.0 | [historical-danish-handwriting] | 5.20M |
389
  | **Total** | | 5.89B |
390
 
391
  [ai-aktindsigt]: data/ai-aktindsigt/ai-aktindsigt.md
 
443
  <!-- START-SAMPLE -->
444
  ```py
445
  {
446
+ "id": "digibok_2009033103031",
447
+ "text": "P. FR. RIST. OLAF RYES SAGA. OPTEGNELSER, DAGBØGER OG BREVE. DET NORDISKE FORLAG. Denne Bog søger at[...]",
448
+ "source": "ncc_books",
449
+ "added": "2025-05-08",
450
+ "created": "1899-01-01, 1899-12-31",
451
+ "token_count": 192301
452
  }
453
  ```
454
 
 
523
  | [gutenberg] | The Danish subsection from Project [Gutenberg](https://www.gutenberg.org) | Books | 6.76M | [Gutenberg] |
524
  | [wikisource] | The Danish subsection of [Wikisource](https://en.wikisource.org/wiki/Main_Page) | Encyclopedic | 6.28M | [CC-0] |
525
  | [wiki-comments] | Text from the comments sections of the Danish Wikipedia | Encyclopedic | 6.14M | [CC-0] |
526
+ | [historical-danish-handwriting] | Minutes from City and Parish Council meetings between 1841 and 1939 from [The Historical Danish handwriting dataset](https://huggingface.co/datasets/aarhus-city-archives/historical-danish-handwriting) | Other | 5.20M | [CC-BY 4.0] |
527
  | [jvj] | The works of the Danish author and poet, [Johannes V. Jensen](https://da.wikipedia.org/wiki/Johannes_V._Jensen) | Books | 3.55M | [CC-BY-SA 4.0] |
528
  | [spont] | Conversational samples collected as a part of research projects at Aarhus University | Conversation | 1.56M | [CC-0] |
529
  | [dannet] | [DanNet](https://cst.ku.dk/projekter/dannet) is a Danish WordNet | Other | 1.48M | [DanNet 1.0] |
 
579
 
580
  [CC-0]: https://creativecommons.org/publicdomain/zero/1.0/legalcode.en
581
  [CC-BY-SA 4.0]: https://creativecommons.org/licenses/by-sa/4.0/deed.en
582
+ [CC-BY 4.0]: https://creativecommons.org/licenses/by/4.0/deed.en
583
  [Apache 2.0]: https://www.apache.org/licenses/LICENSE-2.0
584
  [NLOD 2.0]: ./data/ncc_maalfrid/ncc_maalfrid.md#license-information
585
  [NLOD 2.0]: ./data/ncc_parliament/ncc_parliament.md#license-information
 
587
  [DanNet 1.0]: ./data/dannet/dannet.md#license-information
588
  [Gutenberg]: ./data/gutenberg/gutenberg.md#license-information
589
  [Danish Copyright Law]: ./data/domsdatabasen/domsdatabasen.md#license-information
 
590
  <!-- END-MAIN TABLE -->
591
 
592
  </details>
data/historical-danish-handwriting/create.py CHANGED
@@ -9,9 +9,9 @@
9
  # ]
10
  # ///
11
 
12
- """
13
- This script loads the historical danish handwriting data from Aarhus city archives (https://huggingface.co/datasets/aarhus-city-archives/historical-danish-handwriting).
14
- It loads the columns doc_id, sequence and alto (and not the image files).
15
  """
16
 
17
  import os
@@ -27,58 +27,84 @@ from dynaword.process_dataset import add_token_count, ensure_column_order
27
 
28
 
29
  def load_historical_danish_handwriting(path):
30
- """ Load historical-danish-handwriting data from aarhus-city-archives huggingface repo (path), return as pd df """
31
- raw_df = pd.DataFrame(columns=['doc_id', 'sequence', 'alto'])
32
- for file in sorted(glob.glob(os.path.join(path, 'data', "*.parquet"))):
33
- raw_df = pd.concat([raw_df, pd.read_parquet(file, columns=['doc_id', 'sequence', 'alto'])], axis=0, ignore_index=True)
34
- raw_df.sort_values(by=['doc_id', 'sequence'], inplace=True)
 
 
 
 
35
  return raw_df
36
 
37
 
38
  def parse_xml(bs_obj):
39
- """ Extract text from xml strings (alto format) """
40
  text = ""
41
- layout_str = bs_obj.Layout.find_all('String')
42
  if len(layout_str) == 0:
43
  return ""
44
- else:
45
  for j in layout_str:
46
- if len(text) ==0:
47
- text = text + j.attrs['CONTENT']
48
- else:
49
- text = text + " " + j.attrs['CONTENT']
50
- return "\n" + text
51
 
52
 
53
  def extract_text(raw_df):
54
  """
55
  Extract text from historical-danish-handwriting data and return pd df
56
  """
57
- text_df = pd.DataFrame(columns=['id', 'text', 'source', 'added','created'])
58
  i = 0
59
- for doc in pd.unique(raw_df['doc_id']):
60
- for seq in raw_df[raw_df['doc_id'] == doc]['sequence']:
61
- bs = BeautifulSoup(raw_df.loc[(raw_df['doc_id']==doc) & (raw_df['sequence'] == seq)]['alto'].item(), 'xml')
62
- if (seq == 1):
 
 
 
 
 
63
  filename = re.split("_", bs.Description.fileName.string)[1]
64
- text_df.loc[i, 'text'] = filename + parse_xml(bs)
65
- else:
66
- assert filename in bs.Description.fileName.string, f"id in current seq {seq} does not match first in doc {doc}"
67
- text_df.at[i, 'text'] = text_df.loc[i, 'text'] + parse_xml(bs)
68
- text_df['source'] = 'historical-danish-handwriting'
69
- text_df.loc[i, 'text'] = text_df.loc[i, 'text'].strip("\n")
70
- created = re.search(r'[0-9]{4}-[0-9]{4}', filename).group().split("-")
71
- text_df.loc[i, 'created'] = f"{created[0]}-01-01, {created[1]}-12-31"
72
- text_df.loc[i, 'id'] = f"{text_df.loc[i, 'source']}_{str(doc)}_{re.sub(" ", "_", filename).strip().lower()}"
73
- i+=1
 
 
 
 
74
  return text_df
75
 
 
76
  def convert_dynaword_format(text_df, outpath):
77
- """ Converts pandas df to dynaword format (dataset), adds source, added and token count columns. Saves as .parquet to defined path """
78
- text_df['added'] = datetime.now().date().isoformat()
79
- ds = Dataset.from_pandas(text_df, preserve_index=False, split='train',
80
- features = Features({'id':Value(dtype='string'), 'text':Value(dtype='string'), 'source':Value(dtype='string'),
81
- 'added': Value(dtype='string'), 'created':Value(dtype='string'), 'token_count':Value(dtype="int64")}))
 
 
 
 
 
 
 
 
 
 
 
 
82
  ds = add_token_count(ds)
83
  ds = ensure_column_order(ds)
84
  ds.to_parquet(outpath)
@@ -86,8 +112,12 @@ def convert_dynaword_format(text_df, outpath):
86
 
87
 
88
  def main():
89
- outpath = Path(__file__).parent/'historical-danish-handwriting.parquet'
90
- path = snapshot_download(repo_id="aarhus-city-archives/historical-danish-handwriting", repo_type='dataset', allow_patterns="data/")
 
 
 
 
91
  raw_df = load_historical_danish_handwriting(path)
92
  text_df = extract_text(raw_df)
93
  convert_dynaword_format(text_df, outpath)
 
9
  # ]
10
  # ///
11
 
12
+ """
13
+ This script loads the historical danish handwriting data from Aarhus city archives (https://huggingface.co/datasets/aarhus-city-archives/historical-danish-handwriting).
14
+ It loads the columns doc_id, sequence and alto (and not the image files).
15
  """
16
 
17
  import os
 
27
 
28
 
29
  def load_historical_danish_handwriting(path):
30
+ """Load historical-danish-handwriting data from aarhus-city-archives huggingface repo (path), return as pd df"""
31
+ raw_df = pd.DataFrame(columns=["doc_id", "sequence", "alto"])
32
+ for file in sorted(glob.glob(os.path.join(path, "data", "*.parquet"))):
33
+ raw_df = pd.concat(
34
+ [raw_df, pd.read_parquet(file, columns=["doc_id", "sequence", "alto"])],
35
+ axis=0,
36
+ ignore_index=True,
37
+ )
38
+ raw_df.sort_values(by=["doc_id", "sequence"], inplace=True)
39
  return raw_df
40
 
41
 
42
  def parse_xml(bs_obj):
43
+ """Extract text from xml strings (alto format)"""
44
  text = ""
45
+ layout_str = bs_obj.Layout.find_all("String")
46
  if len(layout_str) == 0:
47
  return ""
48
+ else:
49
  for j in layout_str:
50
+ if len(text) == 0:
51
+ text = text + j.attrs["CONTENT"]
52
+ else:
53
+ text = text + " " + j.attrs["CONTENT"]
54
+ return "\n" + text
55
 
56
 
57
  def extract_text(raw_df):
58
  """
59
  Extract text from historical-danish-handwriting data and return pd df
60
  """
61
+ text_df = pd.DataFrame(columns=["id", "text", "source", "added", "created"])
62
  i = 0
63
+ for doc in pd.unique(raw_df["doc_id"]):
64
+ for seq in raw_df[raw_df["doc_id"] == doc]["sequence"]:
65
+ bs = BeautifulSoup(
66
+ raw_df.loc[(raw_df["doc_id"] == doc) & (raw_df["sequence"] == seq)][
67
+ "alto"
68
+ ].item(),
69
+ "xml",
70
+ )
71
+ if seq == 1:
72
  filename = re.split("_", bs.Description.fileName.string)[1]
73
+ text_df.loc[i, "text"] = filename + parse_xml(bs)
74
+ else:
75
+ assert (
76
+ filename in bs.Description.fileName.string
77
+ ), f"id in current seq {seq} does not match first in doc {doc}"
78
+ text_df.at[i, "text"] = text_df.loc[i, "text"] + parse_xml(bs)
79
+ text_df["source"] = "historical-danish-handwriting"
80
+ text_df.loc[i, "text"] = text_df.loc[i, "text"].strip("\n")
81
+ created = re.search(r"[0-9]{4}-[0-9]{4}", filename).group().split("-")
82
+ text_df.loc[i, "created"] = f"{created[0]}-01-01, {created[1]}-12-31"
83
+ text_df.loc[i, "id"] = (
84
+ f"{text_df.loc[i, 'source']}_{str(doc)}_{re.sub(" ", "_", filename).strip().lower()}"
85
+ )
86
+ i += 1
87
  return text_df
88
 
89
+
90
  def convert_dynaword_format(text_df, outpath):
91
+ """Converts pandas df to dynaword format (dataset), adds source, added and token count columns. Saves as .parquet to defined path"""
92
+ text_df["added"] = datetime.now().date().isoformat()
93
+ ds = Dataset.from_pandas(
94
+ text_df,
95
+ preserve_index=False,
96
+ split="train",
97
+ features=Features(
98
+ {
99
+ "id": Value(dtype="string"),
100
+ "text": Value(dtype="string"),
101
+ "source": Value(dtype="string"),
102
+ "added": Value(dtype="string"),
103
+ "created": Value(dtype="string"),
104
+ "token_count": Value(dtype="int64"),
105
+ }
106
+ ),
107
+ )
108
  ds = add_token_count(ds)
109
  ds = ensure_column_order(ds)
110
  ds.to_parquet(outpath)
 
112
 
113
 
114
  def main():
115
+ outpath = Path(__file__).parent / "historical-danish-handwriting.parquet"
116
+ path = snapshot_download(
117
+ repo_id="aarhus-city-archives/historical-danish-handwriting",
118
+ repo_type="dataset",
119
+ allow_patterns="data/",
120
+ )
121
  raw_df = load_historical_danish_handwriting(path)
122
  text_df = extract_text(raw_df)
123
  convert_dynaword_format(text_df, outpath)
data/historical-danish-handwriting/historical-danish-handwriting.md CHANGED
@@ -2,8 +2,8 @@
2
  pretty_name: Historical Danish handwriting 1841-1939
3
  language:
4
  - da
5
- license: other
6
- license_name: CC by 4.0
7
  size_categories:
8
  - 10K<n<100K
9
  task_categories:
 
2
  pretty_name: Historical Danish handwriting 1841-1939
3
  language:
4
  - da
5
+ license: cc-by-4.0
6
+ license_name: CC-BY 4.0
7
  size_categories:
8
  - 10K<n<100K
9
  task_categories:
data/wiki-comments/create.py CHANGED
@@ -10,17 +10,16 @@
10
  # dynaword = { git = "https://huggingface.co/datasets/danish-foundation-models/danish-dynaword", rev = "6b3822fd6965dda59ae361da99c19b5c56c1263f" }
11
  # ///
12
  """
13
- This script downloads and cleans Wikipedia, it only keeps the user comments.
14
  """
 
15
  import html
16
  import json
17
  import os
18
- import requests
19
- import sys
20
 
21
- from datasets import IterableDataset
22
- from datasets import Dataset
23
  import fasttext
 
 
24
  from huggingface_hub import hf_hub_download
25
 
26
  from dynaword.process_dataset import (
@@ -35,10 +34,11 @@ def run_cmd(cmd):
35
  print(cmd)
36
  os.system(cmd)
37
 
 
38
  def download_data(lang, date):
39
- filename = lang + 'wiki-' + date + '-pages-articles.xml.bz2'
40
  if not os.path.isfile(filename):
41
- url = 'https://dumps.wikimedia.org/' + lang + 'wiki/' + date + '/' + filename
42
  response = requests.get(url)
43
 
44
  with open(filename, "wb") as file:
@@ -46,67 +46,84 @@ def download_data(lang, date):
46
 
47
  return filename
48
 
 
49
  def install_wikiextractor():
50
- if not os.path.isdir('wikiextractor'):
51
- cmd = 'git clone https://github.com/robvanderg/wikiextractor.git'
52
  run_cmd(cmd)
53
 
 
54
  def run_wikiextractor(in_path, out_path):
55
  # clean the data
56
- if not os.path.isdir('wikiextractor/' + out_path):
57
- cmd = 'cd wikiextractor && python3 -m wikiextractor.WikiExtractor ../' + in_path + ' -o ' + out_path + ' --get_misc --json && cd ../ '
 
 
 
 
 
 
58
  run_cmd(cmd)
59
 
 
60
  def read_and_clean(path):
61
  # Load fasttext model
62
- model_path = hf_hub_download(repo_id="facebook/fasttext-language-identification", filename="model.bin")
 
 
63
  fasttext_model = fasttext.load_model(model_path)
64
 
65
  comment_id = 0
66
  all_rows = []
67
- for (root,dirs,files) in os.walk(path, topdown=True):
68
  for file in files:
69
  path = os.path.join(root, file)
70
  for line in open(path):
71
  linedata = json.loads(line)
72
- title = linedata['title']
73
- category = title.split(':')[0]
74
- if category == 'Wikipedia':
75
- if title.startswith('Wikipedia:Dagens '):
76
  continue
77
- id = 'wikicomment_' + str(comment_id)
78
  comment_id += 1
79
- else: # There is more data, but we just want to comments for now
80
  continue
81
- source = 'wiki_misc'
82
  # TODO add linedata['url'] somewhere?
83
- text = html.unescape(linedata['text'])
84
- lines = line.split('\n')
85
- filtered_text = ''
86
- for line in text.split('\n'):
87
- if '{{}}' in text: # unresolved templates
88
  continue
89
 
90
  lang_pred = fasttext_model.predict(line)
91
- if lang_pred[0][0] == '__label__dan_Latn' and lang_pred[1][0] > .5:
92
- filtered_text += line.replace('[', '').replace(']', '') + '\n'
93
- added = '2025-07-21'
94
- created = '2002-02-01, 2025-07-20'
95
- row = {"id": id, "text": filtered_text, "source": source, "added": added, "created": created}
 
 
 
 
 
 
96
  all_rows.append(row)
97
  return all_rows
98
-
99
 
100
- if __name__ == '__main__':
101
- date = '20250720' # obtained from https://dumps.wikimedia.org/dawiki/
102
- lang = 'da'
 
103
  bz2_path = download_data(lang, date)
104
 
105
  install_wikiextractor()
106
- data_folder = lang + 'wiki-misc-' + date
107
  run_wikiextractor(bz2_path, data_folder)
108
 
109
- full_data = read_and_clean('wikiextractor/' + data_folder)
110
  print(len(full_data))
111
  exit(1)
112
 
@@ -117,5 +134,4 @@ if __name__ == '__main__':
117
  ds = add_token_count(ds)
118
  ds = ensure_column_order(ds)
119
 
120
- ds.to_parquet('data/wiki-comments/wiki-comments.parquet')
121
-
 
10
  # dynaword = { git = "https://huggingface.co/datasets/danish-foundation-models/danish-dynaword", rev = "6b3822fd6965dda59ae361da99c19b5c56c1263f" }
11
  # ///
12
  """
13
+ This script downloads and cleans Wikipedia, it only keeps the user comments.
14
  """
15
+
16
  import html
17
  import json
18
  import os
 
 
19
 
 
 
20
  import fasttext
21
+ import requests
22
+ from datasets import Dataset
23
  from huggingface_hub import hf_hub_download
24
 
25
  from dynaword.process_dataset import (
 
34
  print(cmd)
35
  os.system(cmd)
36
 
37
+
38
  def download_data(lang, date):
39
+ filename = lang + "wiki-" + date + "-pages-articles.xml.bz2"
40
  if not os.path.isfile(filename):
41
+ url = "https://dumps.wikimedia.org/" + lang + "wiki/" + date + "/" + filename
42
  response = requests.get(url)
43
 
44
  with open(filename, "wb") as file:
 
46
 
47
  return filename
48
 
49
+
50
  def install_wikiextractor():
51
+ if not os.path.isdir("wikiextractor"):
52
+ cmd = "git clone https://github.com/robvanderg/wikiextractor.git"
53
  run_cmd(cmd)
54
 
55
+
56
  def run_wikiextractor(in_path, out_path):
57
  # clean the data
58
+ if not os.path.isdir("wikiextractor/" + out_path):
59
+ cmd = (
60
+ "cd wikiextractor && python3 -m wikiextractor.WikiExtractor ../"
61
+ + in_path
62
+ + " -o "
63
+ + out_path
64
+ + " --get_misc --json && cd ../ "
65
+ )
66
  run_cmd(cmd)
67
 
68
+
69
  def read_and_clean(path):
70
  # Load fasttext model
71
+ model_path = hf_hub_download(
72
+ repo_id="facebook/fasttext-language-identification", filename="model.bin"
73
+ )
74
  fasttext_model = fasttext.load_model(model_path)
75
 
76
  comment_id = 0
77
  all_rows = []
78
+ for root, dirs, files in os.walk(path, topdown=True):
79
  for file in files:
80
  path = os.path.join(root, file)
81
  for line in open(path):
82
  linedata = json.loads(line)
83
+ title = linedata["title"]
84
+ category = title.split(":")[0]
85
+ if category == "Wikipedia":
86
+ if title.startswith("Wikipedia:Dagens "):
87
  continue
88
+ id = "wikicomment_" + str(comment_id)
89
  comment_id += 1
90
+ else: # There is more data, but we just want to comments for now
91
  continue
92
+ source = "wiki_misc"
93
  # TODO add linedata['url'] somewhere?
94
+ text = html.unescape(linedata["text"])
95
+ lines = line.split("\n") # noqa
96
+ filtered_text = ""
97
+ for line in text.split("\n"):
98
+ if "{{}}" in text: # unresolved templates
99
  continue
100
 
101
  lang_pred = fasttext_model.predict(line)
102
+ if lang_pred[0][0] == "__label__dan_Latn" and lang_pred[1][0] > 0.5:
103
+ filtered_text += line.replace("[", "").replace("]", "") + "\n"
104
+ added = "2025-07-21"
105
+ created = "2002-02-01, 2025-07-20"
106
+ row = {
107
+ "id": id,
108
+ "text": filtered_text,
109
+ "source": source,
110
+ "added": added,
111
+ "created": created,
112
+ }
113
  all_rows.append(row)
114
  return all_rows
 
115
 
116
+
117
+ if __name__ == "__main__":
118
+ date = "20250720" # obtained from https://dumps.wikimedia.org/dawiki/
119
+ lang = "da"
120
  bz2_path = download_data(lang, date)
121
 
122
  install_wikiextractor()
123
+ data_folder = lang + "wiki-misc-" + date
124
  run_wikiextractor(bz2_path, data_folder)
125
 
126
+ full_data = read_and_clean("wikiextractor/" + data_folder)
127
  print(len(full_data))
128
  exit(1)
129
 
 
134
  ds = add_token_count(ds)
135
  ds = ensure_column_order(ds)
136
 
137
+ ds.to_parquet("data/wiki-comments/wiki-comments.parquet")
 
data/wikibooks/create.py CHANGED
@@ -33,10 +33,7 @@ from tqdm import tqdm
33
  import pandas as pd
34
 
35
  from datasets import Dataset
36
- from transformers import AutoTokenizer
37
- from concurrent.futures import ProcessPoolExecutor
38
- from functools import partial
39
- from typing import Any, Tuple
40
 
41
  from dynaword.process_dataset import (
42
  add_token_count,
 
33
  import pandas as pd
34
 
35
  from datasets import Dataset
36
+ from typing import Tuple
 
 
 
37
 
38
  from dynaword.process_dataset import (
39
  add_token_count,
data/wikipedia/create.py CHANGED
@@ -34,10 +34,7 @@ from tqdm import tqdm
34
  import pandas as pd
35
 
36
  from datasets import Dataset
37
- from transformers import AutoTokenizer
38
- from concurrent.futures import ProcessPoolExecutor
39
- from functools import partial
40
- from typing import Any, Tuple
41
 
42
  from dynaword.process_dataset import (
43
  add_token_count,
@@ -226,7 +223,7 @@ def main():
226
 
227
  # Local file paths
228
  DOWNLOADED_BZ2_FILE = f"tmp/da{source}-latest-pages-articles.xml.bz2"
229
- OUTPUT_PARQUET_FILE = f"wikipedia.parquet"
230
 
231
  # --- Execution ---
232
  # 1. Download the dump file
 
34
  import pandas as pd
35
 
36
  from datasets import Dataset
37
+ from typing import Tuple
 
 
 
38
 
39
  from dynaword.process_dataset import (
40
  add_token_count,
 
223
 
224
  # Local file paths
225
  DOWNLOADED_BZ2_FILE = f"tmp/da{source}-latest-pages-articles.xml.bz2"
226
+ OUTPUT_PARQUET_FILE = "wikipedia.parquet"
227
 
228
  # --- Execution ---
229
  # 1. Download the dump file
data/wikisource/create.py CHANGED
@@ -33,10 +33,7 @@ from tqdm import tqdm
33
  import pandas as pd
34
 
35
  from datasets import Dataset
36
- from transformers import AutoTokenizer
37
- from concurrent.futures import ProcessPoolExecutor
38
- from functools import partial
39
- from typing import Any, Tuple
40
 
41
  from dynaword.process_dataset import (
42
  add_token_count,
 
33
  import pandas as pd
34
 
35
  from datasets import Dataset
36
+ from typing import Tuple
 
 
 
37
 
38
  from dynaword.process_dataset import (
39
  add_token_count,
images/dataset_size_plot.html CHANGED
@@ -3880,6 +3880,6 @@ maplibre-gl/dist/maplibre-gl.js:
3880
 
3881
  window.Plotly = Plotly;
3882
  return Plotly;
3883
- }));</script> <div id="5bf602bc-fdf6-4ae2-a886-0db489c81546" class="plotly-graph-div" style="height:800px; width:100%;"></div> <script type="text/javascript"> window.PLOTLYENV=window.PLOTLYENV || {}; if (document.getElementById("5bf602bc-fdf6-4ae2-a886-0db489c81546")) { Plotly.newPlot( "5bf602bc-fdf6-4ae2-a886-0db489c81546", 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