ncc separated, create.py made more efficient, conf limit raised to 0.75

CHANGELOG.md

@@ -6,13 +6,14 @@ All notable changes to this project will be documented in this file.
 The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 
-## [v1.0.12] - 2025-05-05
+## [v1.0.12] - 2025-05-08
 
 ### Added
 
 - Added new datasets
-  - Norwegian Colossal Corpus (newspaper) (~1M tokens)
-  
+  - Norwegian Colossal Corpus (newspapers) (~191.08K tokens)
+  - Norwegian Colossal Corpus (books) (~531.97M tokens)
+  - Norwegian Colossal Corpus (maalfrid) (~29.26M tokens)
 
 ## [v1.0.11] - 2025-03-29
 

README.md

@@ -125,10 +125,18 @@ configs:
   data_files:
   - split: train
     path: data/nota/*.parquet
-- config_name: ncc_newspaper
+- config_name: ncc_newspapers
   data_files:
   - split: train
-    path: data/ncc_newspaper/*.parquet
+    path: data/ncc_newspapers/*.parquet
+- config_name: ncc_books
+  data_files:
+  - split: train
+    path: data/ncc_books/*.parquet
+- config_name: ncc_maalfrid
+  data_files:
+  - split: train
+    path: data/ncc_maalfrid/*.parquet
 annotations_creators:
 - no-annotation
 language_creators:
@@ -334,7 +342,6 @@ Below follows a brief overview of the sources in the corpus along with their ind
 | [hest]              | Samples from the Danish debate forum www.heste-nettet.dk                                                                                      | 389.33M     | [CC-0]                 |
 | [retsinformationdk] | [retsinformation.dk](https://www.retsinformation.dk) (legal-information.dk) the official legal information system of Denmark                  | 516.54M     | [Danish Copyright Law] |
 | [cellar]            | The official digital repository for European Union legal documents and open data                                                              | 1.28B       | [CC-BY-SA 4.0]         |
-| [ncc_newspaper]             | Danish subset of [NCC](https://huggingface.co/datasets/NbAiLab/NCC), The Norwegian Colossal Corpus (newspaper)                       | ?      | [CC-0]                |
 | **Total**           |                                                                                                                                               | 3.49B       |                        |
 
 [ai-aktindsigt]: data/ai-aktindsigt/ai-aktindsigt.md

data/ncc_books/create.py

@@ -0,0 +1,319 @@
+# /// script
+# requires-python = ">=3.12"
+# dependencies = [
+#     "datasets>=3.2.0"
+# ]
+# ///
+
+import logging
+import re
+import inspect
+
+from pathlib import Path
+from datetime import datetime
+from collections import defaultdict
+from collections.abc import Callable
+
+import pandas as pd
+from datasets import Dataset, load_dataset
+
+logger = logging.getLogger(__name__)
+########## edit manually for each source
+hf_path = "NbAiLab/NCC"
+source = "ncc_books"
+doc_type_searchword = "book"
+license = "cc0-1.0"
+domain = "Wiki & Books"
+num_proc = 10
+##########
+today = datetime.now().strftime("%Y-%m-%d")
+
+#stop words taken from spaCy
+#https://github.com/explosion/spaCy/blob/master/spacy/lang/da/stop_words.py
+# Source: Handpicked by Jens Dahl Møllerhøj.
+spacy_sw = set(
+    """
+af aldrig alene alle allerede alligevel alt altid anden andet andre at
+
+bag begge blandt blev blive bliver burde bør
+
+da de dem den denne dens der derefter deres derfor derfra deri dermed derpå derved det dette dig din dine disse dog du
+
+efter egen eller ellers en end endnu ene eneste enhver ens enten er et
+
+flere flest fleste for foran fordi forrige fra få før først
+
+gennem gjorde gjort god gør gøre gørende
+
+ham han hans har havde have hel heller hen hende hendes henover her herefter heri hermed herpå hun hvad hvem hver hvilke hvilken hvilkes hvis hvor hvordan hvorefter hvorfor hvorfra hvorhen hvori hvorimod hvornår hvorved
+
+i igen igennem ikke imellem imens imod ind indtil ingen intet
+
+jeg jer jeres jo
+
+kan kom kommer kun kunne
+
+lad langs lav lave lavet lidt lige ligesom lille længere
+
+man mange med meget mellem men mens mere mest mig min mindre mindst mine mit må måske
+
+ned nemlig nogen nogensinde noget nogle nok nu ny nyt nær næste næsten
+
+og også om omkring op os over overalt
+
+på
+
+samme sammen selv selvom senere ses siden sig sige skal skulle som stadig synes syntes så sådan således
+
+temmelig tidligere til tilbage tit
+
+ud uden udover under undtagen
+
+var ved vi via vil ville vore vores vær være været
+
+øvrigt
+""".split()
+)
+
+def word_tokenize(text: str) -> list[str]:
+    """
+    Tokenizes a string into words, splitting on whitespace and punctuation.
+
+    Example:
+        >>> word_tokenize("Hello, world!")
+        ['Hello', ',', 'world', '!']
+        >>> word_tokenize("This is a test.")
+        ['This', 'is', 'a', 'test', '.']
+        >>> word_tokenize("Many spaces    between words.")
+        ['Many', 'spaces', 'between', 'words', '.']
+    """
+
+    punkt = [",", ".", "!", "?", ":", ";", "(", ")", "[", "]", "{", "}", '"', "'"]
+    for p in punkt:
+        text = text.replace(p, f" {p} ")
+    return text.split()
+
+def alpha_ratio(text: str | list[str]) -> float:
+    """
+    If not split already to words, splits text with word_tokenize()
+    Calculates ratio of words with only alphabetical characters
+
+    """
+    if type(text) is str:
+        text = word_tokenize(text)
+    else:
+        pass
+
+    alpha_ratio = 1 - sum(not word.isalpha() for word in text) / len(text)
+
+    return alpha_ratio
+
+def count_min_target(given_list: list, target_list: list, min: int) -> bool:
+    """
+    Iterates through given list, until at least min items match any items from target list
+
+    """
+    c_item = 0
+    given_list_iter = iter(given_list)
+    while c_item < min:
+        try:
+            current_item = next(given_list_iter)
+            if current_item in target_list:
+                c_item += 1
+        except StopIteration:
+            break
+
+    return c_item == min
+
+def dynaword_format(
+    meta_document: dict[str, str | int]
+    ) -> dict[str, str | dict[str, str]]:
+    """Reformats data to fit dynaword standards"""
+
+    text = meta_document.get("text")
+    id = meta_document.get("id")
+    date = meta_document.get("publish_year")
+    doc_type = meta_document.get("doc_type")
+
+    newdata = {
+         "text": text,
+         "source": source,
+         "id": id,
+         "added": today,
+         "created": f"{date}-01-01, {date}-12-31",
+         "license": license,
+         "domain": domain,
+         "metadata": {
+                "source-pretty": f"Norwegian Colossal Corpus ({re.sub("ncc_","",source)})",
+                "source-type": doc_type,
+            },
+        }
+
+    return newdata
+
+def log_pre_filter_lang_data(lang_metadata : dict[str,dict[str,int]],
+                              filtered_ds: Dataset):
+    """
+    Function for logging changes in a large dataset,
+    based on the metadata pre filering and the filtered dataset,
+    used for language filtering
+    """
+    all_docs = sum(lang_metadata[source].values())
+    no_docs = lang_metadata[source].get("no")
+    da_docs = lang_metadata[source].get("da")
+    no_perc = round(no_docs/all_docs*100,4)
+    da_perc = round(da_docs/all_docs*100,4)
+
+    f_length = len(filtered_ds)
+    f_perc = round(f_length/da_docs*100,4)
+    f_total_perc = round(f_length/all_docs*100,4)
+
+    logger.info(f"Documents of {source}:")
+    logger.info(f"NO: {no_docs}, {no_perc}% ; DA: {da_docs}, {da_perc}%")
+    logger.info(f"After language confidence filtering:")
+    logger.info(f"DA: {f_length}, lost: {100-f_perc}%")
+    logger.info(f"Total document change:")
+    logger.info(f"{all_docs} -> {f_length}, loss: {100-f_total_perc}%")
+
+def get_var_name(var):
+    """ outputs the variable name """
+    callers_local_vars = inspect.currentframe().f_back.f_back.f_back.f_locals.items()
+    return [var_name for var_name, var_val in callers_local_vars if var_val is var]
+
+def filter_with_changelog(filter_func:Callable[[Dataset],Dataset],
+                          dataset:Dataset) -> Dataset:
+    """
+    Function, which takes a filter and a dataset.
+    Counts text docs and tokens before and after filtering,
+    Saves filtering changes to log.
+    """
+
+    filter_name = get_var_name(filter_func)
+    pre_filter_docs = len(dataset)
+    pre_filter_tokens= sum(len(word_tokenize(i["text"])) for i in dataset)
+
+    dataset = dataset.filter(filter_func,num_proc=num_proc)
+
+    post_filter_docs = len(dataset)
+    post_filter_tokens= sum(len(word_tokenize(i["text"])) for i in dataset)
+    tokens_removed = round((1-(post_filter_tokens/pre_filter_tokens))*100,2)
+    docs_removed = round((1-(post_filter_docs/pre_filter_docs))*100,2)
+    
+    logger.info(f"FILTER: {filter_name}")
+    logger.info(f"TOKENS: pre: {pre_filter_tokens}, post: {post_filter_tokens}, loss: {tokens_removed}%")
+    logger.info(f"DOCUMENTS: pre: {pre_filter_docs}, post: {post_filter_docs}, loss: {docs_removed}%")
+    
+    return dataset
+
+
+source_filter = lambda ds : doc_type_searchword in ds["doc_type"]
+length_filter = lambda ds: len(word_tokenize(ds["text"])) >= 10
+too_long_filter = lambda ds: len(word_tokenize(ds["text"])) > 1e5
+alpha_filter = lambda ds: alpha_ratio(ds["text"]) >= 0.7
+stop_word_filter = lambda ds: count_min_target(word_tokenize(ds["text"]),spacy_sw,2)
+
+samples_pr_source: dict = defaultdict(lambda: defaultdict(int))
+def language_filter_with_desc_stats(ds:Dataset) -> bool:
+  """ 
+  Language filtering in a streamed dataset while logging all languages 
+  """
+  s = source
+  language = ds["lang_fasttext"]
+  samples_pr_source[s][language] += 1
+
+  language_filter = ds["lang_fasttext"] == "da" and float(ds["lang_fasttext_conf"]) >= 0.75
+
+  return language_filter
+
+def quality_checks(ds:Dataset) -> Dataset:
+    """ 
+    Quality checks for:
+    - no duplicate ids
+    - no duplicate texts
+    - logs texts > 1e5 tokens
+    """
+    #convert to pandas for the drop_duplicates() 
+    df = pd.DataFrame(ds)
+    # remove duplicate ids
+    len_df = len(df)
+    df = df.drop_duplicates(subset=["id"])
+    logger.info(f"Removed {len_df - len(df)} duplicate ids")
+    # remove rows with duplicate text
+    len_df = len(df)
+    df = df.drop_duplicates(subset=["text"])
+    logger.info(f"Removed {len_df - len(df)} rows with duplicate text")
+    #reconvert and remove index
+    ds_f = Dataset.from_pandas(df,preserve_index=False)
+    try:
+        ds_f["__index_level_0__"]
+        ds_f = ds_f.remove_columns("__index_level_0__")
+    except KeyError:
+        pass
+
+    assert len(set(ds_f["id"])) == len(ds_f), "IDs are not unique"
+    assert len(set(ds_f["text"])) == len(ds_f), "Texts are not unique"
+
+    long_texts = ds_f.filter(too_long_filter,num_proc=None)
+    if len(long_texts["id"]) > 0:
+        logger.info(f"{len(long_texts["id"])} Long texts (>~1e5 tokens) found")
+        for id in long_texts["id"]:
+            logger.info(f"id: {id}")
+    else:
+        logger.info("No long texts (>~1e5 tokens) found")
+
+    return ds_f
+
+def main():
+    #load all splits
+    logger.info(f"Loading data from: {hf_path}")
+    danish_data = load_dataset(hf_path, streaming=False, split="train+validation", num_proc=num_proc)
+    danish_data.cleanup_cache_files()
+
+
+    #filter by metadata
+    logger.info(f"Processing source: {source}")
+    danish_data=danish_data.filter(source_filter,num_proc=num_proc)
+
+
+    logger.info(f"Processing language")
+    danish_data=danish_data.filter(language_filter_with_desc_stats, num_proc=None)
+
+    #log language changes
+    log_pre_filter_lang_data(samples_pr_source,danish_data)
+    
+    #convert to dynaword format
+    danish_data = danish_data.map(dynaword_format)
+    danish_data = danish_data.select_columns(["text",
+                                            "source",
+                                            "id",
+                                            "added",
+                                            "created",
+                                            "license",
+                                            "domain",
+                                            "metadata"])
+
+    #filter and log changes
+    danish_data = filter_with_changelog(length_filter,danish_data)
+    danish_data = filter_with_changelog(alpha_filter,danish_data)
+    danish_data = filter_with_changelog(stop_word_filter,danish_data)
+
+    #Quality checks
+    danish_data = quality_checks(danish_data)
+    
+    ### saving
+    save_path = Path(__file__).parent / f"{source}.parquet"
+    danish_data.to_parquet(save_path)
+
+
+
+if __name__ == "__main__":
+    log_path = Path(__file__).parent / f"{source}.log"
+    logging.basicConfig(
+        level=logging.INFO,
+        format="%(asctime)s - %(levelname)s - %(message)s",
+        handlers=[
+            logging.StreamHandler(),
+            logging.FileHandler(log_path),
+        ],
+    )
+    main()

data/ncc_books/descriptive_stats.json

@@ -0,0 +1,7 @@
+{
+  "number_of_samples": 4902,
+  "average_document_length": 314064.2513259894,
+  "number_of_tokens": 531969285,
+  "language": "dan, dansk, Danish",
+  "revision": "da633eaca923cc25686bda59a41369694597baac"
+}

data/ncc_books/ncc_books.log

@@ -0,0 +1,1081 @@
+2025-05-08 08:54:49,165 - INFO - Loading data from: NbAiLab/NCC
+2025-05-08 08:54:58,162 - INFO - Processing source: ncc_books
+2025-05-08 08:56:46,434 - INFO - Processing language
+2025-05-08 08:57:22,861 - INFO - Documents of ncc_books:
+2025-05-08 08:57:22,867 - INFO - NO: 12496, 51.5235% ; DA: 8443, 34.8122%
+2025-05-08 08:57:22,867 - INFO - After language confidence filtering:
+2025-05-08 08:57:22,867 - INFO - DA: 5125, lost: 39.2988%
+2025-05-08 08:57:22,867 - INFO - Total document change:
+2025-05-08 08:57:22,867 - INFO - 24253 -> 5125, loss: 78.8686%
+2025-05-08 09:01:43,677 - INFO - FILTER: ['length_filter']
+2025-05-08 09:01:43,688 - INFO - TOKENS: pre: 322682794, post: 322682794, loss: 0.0%
+2025-05-08 09:01:43,688 - INFO - DOCUMENTS: pre: 5125, post: 5125, loss: 0.0%
+2025-05-08 09:05:38,723 - INFO - FILTER: ['alpha_filter']
+2025-05-08 09:05:38,724 - INFO - TOKENS: pre: 322682794, post: 307341040, loss: 4.75%
+2025-05-08 09:05:38,726 - INFO - DOCUMENTS: pre: 5125, post: 4902, loss: 4.35%
+2025-05-08 09:09:44,812 - INFO - FILTER: ['stop_word_filter']
+2025-05-08 09:09:44,812 - INFO - TOKENS: pre: 307341040, post: 307341040, loss: 0.0%
+2025-05-08 09:09:44,812 - INFO - DOCUMENTS: pre: 4902, post: 4902, loss: 0.0%
+2025-05-08 09:09:50,536 - INFO - Removed 0 duplicate ids
+2025-05-08 09:09:52,320 - INFO - Removed 0 rows with duplicate text
+2025-05-08 09:11:29,706 - INFO - 1060 Long texts (>~1e5 tokens) found
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data/ncc_books/ncc_books.md

@@ -0,0 +1,174 @@
+---
+pretty_name: Norwegian Colossal Corpus (books)
+language:
+  - da
+license: cc0-1.0
+license_name: CC0 1.0
+task_categories:
+  - text-generation
+  - fill-mask
+task_ids:
+  - language-modeling
+---
+
+# Dataset Card for Norwegian Colossal Corpus (books)
+
+<!-- START-SHORT DESCRIPTION -->
+Danish books extracted from the [Norwegian Colossal Corpus](https://huggingface.co/datasets/NbAiLab/NCC) derived from OCR. 
+<!-- END-SHORT DESCRIPTION -->
+
+The Norwegian Colossal Corpus is a collection of multiple smaller Norwegian corpuses suitable for training large language models.  
+(desc. taken from [NCC](https://huggingface.co/datasets/NbAiLab/NCC))
+
+This subset is the result of the following filtering from all available data splits:
+- Document comes from books
+- Document is classified as Danish with a threshold of 0.75
+- Document has at least 10 words (whitespace separated strings + punctuation)
+- The ratio of all words / words with only alphabetical characters is at least 0.7
+- The document contains at least 2 Danish stop words
+
+
+
+## Dataset Description
+
+<!-- START-DESC-STATS -->
+- **Language**: dan, dansk, Danish
+- **Number of samples**: 4.90K
+- **Number of tokens (Llama 3)**: 531.97M
+- **Average document length (characters)**: 314064.25
+<!-- END-DESC-STATS -->
+
+
+## Dataset Structure
+An example from the dataset looks as follows.
+<!-- START-SAMPLE -->
+```py
+{
+  "text": "P. FR. RIST. OLAF RYES SAGA. OPTEGNELSER, DAGB�GER OG BREVE. DET NORDISKE FORLAG. Denne Bog s�ger at[...]",
+  "source": "ncc_books",
+  "id": "digibok_2009033103031",
+  "added": "2025-05-08",
+  "created": "1899-01-01, 1899-12-31",
+  "license": "cc0-1.0",
+  "domain": "Wiki & Books",
+  "metadata": {
+    "source-pretty": "Norwegian Colossal Corpus (books)",
+    "source-type": "books"
+  }
+}
+```
+
+### Data Fields
+
+An entry in the dataset consists of the following fields:
+
+- `text`(`str`): The content of the document.
+- `source` (`str`): The source of the document (see [Source Data](#source-data)).
+- `id` (`str`): An unique identifier for each document.
+- `added` (`str`): An date for when the document was added to this collection.
+- `created` (`str`): An date range for when the document was originally created.
+- `license` (`str`): The license of the document. The licenses vary according to the source. 
+- `domain` (`str`): The domain of the source
+- `metadata/source-pretty` (`str`): The long form version of the short-form source name
+- `metadata/*`: Potentially additional metadata
+<!-- END-SAMPLE -->
+
+
+
+### Dataset Statistics
+
+<!-- START-DATASET PLOTS -->
+<img src="./images/dist_document_length.png" width="600" style="margin-right: 10px;" />
+<img>
+<!-- END-DATASET PLOTS -->
+
+
+
+## Additional Information
+
+## License Information
+[CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/)
+
+## Filtering
+### Pre filtering 
+Documents, which are tagged as "book*" in the "doc_type" column of the NCC  
+
+Norwegian documents: 12496  
+~ 51.5% of data    
+Danish documents: 8443  
+~ 34.8% of data  
+
+### Classification confidence
+Removing documents, where the "lang_fasttext_conf" column of the NCC is less than 0.75
+
+Documents: 5125  
+Loss: ~ 39.29% 
+
+### Length
+Removing documents based on the "text" column of the NCC. Text is tokenized by splitting on whitespace. Common punctuation marks are counted as separate tokens.
+
+Texts with less than 10 tokens are removed.
+
+No changes are made by this filtering step.
+
+### Alpha Ratio
+Texts, in which the ratio of words and words with only alphabetic characters is less than 0.7, are removed.
+
+TOKENS: pre: 322682794, post: 307341040, loss: 4.75%  
+DOCUMENTS: pre: 5125, post: 4902, loss: 4.35%
+
+### Stop Words
+Texts which contain less then 2 stop words are removed.  
+
+No changes are made by this filtering step. 
+
+### Quality
+1060 long texts (>~1e5 tokens) were found.  
+
+It is important to note, that recurring ocr mistakes and archaic expressions in older texts hinder the legibility of some of the documents and make differentiating between Norwegian and Danish difficult.
+<details>
+<summary>Examples</summary>
+<br>
+
+- De nartotifte Stoffer, vi benytte. ae Tobat...  
+
+- Sqmfruer hav. baaret lige faadan mpffe og Prydelfe...
+
+</details>
+
+### Citation Information
+```
+@inproceedings{kummervold-etal-2022-norwegian-colossal,
+  title     = {The {N}orwegian colossal corpus: A text corpus for training large {N}orwegian language models},
+  author    = {Kummervold, Per E  and
+               Wetjen, Freddy    and
+               De la Rosa, Javier},
+  booktitle = {Proceedings of the Thirteenth Language Resources and Evaluation Conference (LREC)},
+  year      = {2022},
+  address   = {Marseille, France},
+  publisher = {European Language Resources Association},
+  url       = {https://aclanthology.org/2022.lrec-1.410},
+  pages     = {3852--3860},
+  abstract  = {Norwegian has been one of many languages lacking sufficient available text to train quality language models. In an attempt to bridge this gap, we introduce the Norwegian Colossal Corpus (NCC), which comprises 49GB of clean Norwegian textual data containing over 7B words. The NCC is composed of different and varied sources, ranging from books and newspapers to government documents and public reports, showcasing the various uses of the Norwegian language in society. The corpus contains mainly Norwegian Bokmål and Norwegian Nynorsk. Each document in the corpus is tagged with metadata that enables the creation of sub-corpora for specific needs. Its structure makes it easy to combine with large web archives that for licensing reasons could not be distributed together with the NCC. By releasing this corpus openly to the public, we hope to foster the creation of both better Norwegian language models and multilingual language models with support for Norwegian.},
+}
+
+@inproceedings{kummervold-etal-2021-operationalizing,
+  title     = {Operationalizing a National Digital Library: The Case for a {N}orwegian Transformer Model},
+  author    = {Kummervold, Per E  and
+               De la Rosa, Javier  and
+               Wetjen, Freddy  and
+               Brygfjeld, Svein Arne},
+  booktitle = {Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa)},
+  year      = {2021},
+  address   = {Reykjavik, Iceland (Online)},
+  publisher = {Linköping University Electronic Press, Sweden},
+  url       = {https://aclanthology.org/2021.nodalida-main.3},
+  pages     = {20--29},
+  abstract  = {In this work, we show the process of building a large-scale training set from digital and digitized collections at a national library.
+  The resulting Bidirectional Encoder Representations from Transformers (BERT)-based language model for Norwegian outperforms multilingual BERT (mBERT) models
+  in several token and sequence classification tasks for both Norwegian Bokmål and Norwegian Nynorsk. Our model also improves the mBERT performance for other
+  languages present in the corpus such as English, Swedish, and Danish. For languages not included in the corpus, the weights degrade moderately while keeping strong multilingual properties. Therefore,
+  we show that building high-quality models within a memory institution using somewhat noisy optical character recognition (OCR) content is feasible, and we hope to pave the way for other memory institutions to follow.},
+}
+
+```

data/ncc_books/ncc_books.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:573d5383e77c46433c8d311dc82decad28171ca5a93fbc794c746d5c19622ee1
+size 978600075

data/ncc_maalfrid/create.py

@@ -0,0 +1,318 @@
+# /// script
+# requires-python = ">=3.12"
+# dependencies = [
+#     "datasets>=3.2.0"
+# ]
+# ///
+
+import logging
+import re
+import inspect
+
+from pathlib import Path
+from datetime import datetime
+from collections import defaultdict
+from collections.abc import Callable
+
+import pandas as pd
+from datasets import Dataset, load_dataset
+
+logger = logging.getLogger(__name__)
+########## edit manually for each source
+hf_path = "NbAiLab/NCC"
+source = "ncc_maalfrid"
+doc_type_searchword = "maalfrid"
+license = "other"
+domain = "Legal"
+num_proc = 10
+##########
+today = datetime.now().strftime("%Y-%m-%d")
+
+#stop words taken from spaCy
+#https://github.com/explosion/spaCy/blob/master/spacy/lang/da/stop_words.py
+# Source: Handpicked by Jens Dahl Møllerhøj.
+spacy_sw = set(
+    """
+af aldrig alene alle allerede alligevel alt altid anden andet andre at
+
+bag begge blandt blev blive bliver burde bør
+
+da de dem den denne dens der derefter deres derfor derfra deri dermed derpå derved det dette dig din dine disse dog du
+
+efter egen eller ellers en end endnu ene eneste enhver ens enten er et
+
+flere flest fleste for foran fordi forrige fra få før først
+
+gennem gjorde gjort god gør gøre gørende
+
+ham han hans har havde have hel heller hen hende hendes henover her herefter heri hermed herpå hun hvad hvem hver hvilke hvilken hvilkes hvis hvor hvordan hvorefter hvorfor hvorfra hvorhen hvori hvorimod hvornår hvorved
+
+i igen igennem ikke imellem imens imod ind indtil ingen intet
+
+jeg jer jeres jo
+
+kan kom kommer kun kunne
+
+lad langs lav lave lavet lidt lige ligesom lille længere
+
+man mange med meget mellem men mens mere mest mig min mindre mindst mine mit må måske
+
+ned nemlig nogen nogensinde noget nogle nok nu ny nyt nær næste næsten
+
+og også om omkring op os over overalt
+
+på
+
+samme sammen selv selvom senere ses siden sig sige skal skulle som stadig synes syntes så sådan således
+
+temmelig tidligere til tilbage tit
+
+ud uden udover under undtagen
+
+var ved vi via vil ville vore vores vær være været
+
+øvrigt
+""".split()
+)
+
+def word_tokenize(text: str) -> list[str]:
+    """
+    Tokenizes a string into words, splitting on whitespace and punctuation.
+
+    Example:
+        >>> word_tokenize("Hello, world!")
+        ['Hello', ',', 'world', '!']
+        >>> word_tokenize("This is a test.")
+        ['This', 'is', 'a', 'test', '.']
+        >>> word_tokenize("Many spaces    between words.")
+        ['Many', 'spaces', 'between', 'words', '.']
+    """
+
+    punkt = [",", ".", "!", "?", ":", ";", "(", ")", "[", "]", "{", "}", '"', "'"]
+    for p in punkt:
+        text = text.replace(p, f" {p} ")
+    return text.split()
+
+def alpha_ratio(text: str | list[str]) -> float:
+    """
+    If not split already to words, splits text with word_tokenize()
+    Calculates ratio of words with only alphabetical characters
+
+    """
+    if type(text) is str:
+        text = word_tokenize(text)
+    else:
+        pass
+
+    alpha_ratio = 1 - sum(not word.isalpha() for word in text) / len(text)
+
+    return alpha_ratio
+
+def count_min_target(given_list: list, target_list: list, min: int) -> bool:
+    """
+    Iterates through given list, until at least min items match any items from target list
+
+    """
+    c_item = 0
+    given_list_iter = iter(given_list)
+    while c_item < min:
+        try:
+            current_item = next(given_list_iter)
+            if current_item in target_list:
+                c_item += 1
+        except StopIteration:
+            break
+
+    return c_item == min
+
+def dynaword_format(
+    meta_document: dict[str, str | int]
+    ) -> dict[str, str | dict[str, str]]:
+    """Reformats data to fit dynaword standards"""
+
+    text = meta_document.get("text")
+    id = meta_document.get("id")
+    date = meta_document.get("publish_year")
+    doc_type = meta_document.get("doc_type")
+
+    newdata = {
+         "text": text,
+         "source": source,
+         "id": id,
+         "added": today,
+         "created": f"{date}-01-01, {date}-12-31",
+         "license": license,
+         "domain": domain,
+         "metadata": {
+                "source-pretty": f"Norwegian Colossal Corpus ({re.sub("ncc_","",source)})",
+                "source-type": doc_type,
+            },
+        }
+
+    return newdata
+
+def log_pre_filter_lang_data(lang_metadata : dict[str,dict[str,int]],
+                              filtered_ds: Dataset):
+    """
+    Function for logging changes in a large dataset,
+    based on the metadata pre filering and the filtered dataset,
+    used for language filtering
+    """
+    all_docs = sum(lang_metadata[source].values())
+    no_docs = lang_metadata[source].get("no")
+    da_docs = lang_metadata[source].get("da")
+    no_perc = round(no_docs/all_docs*100,4)
+    da_perc = round(da_docs/all_docs*100,4)
+
+    f_length = len(filtered_ds)
+    f_perc = round(f_length/da_docs*100,4)
+    f_total_perc = round(f_length/all_docs*100,4)
+
+    logger.info(f"Documents of {source}:")
+    logger.info(f"NO: {no_docs}, {no_perc}% ; DA: {da_docs}, {da_perc}%")
+    logger.info(f"After language confidence filtering:")
+    logger.info(f"DA: {f_length}, lost: {100-f_perc}%")
+    logger.info(f"Total document change:")
+    logger.info(f"{all_docs} -> {f_length}, loss: {100-f_total_perc}%")
+
+def get_var_name(var):
+    """ outputs the variable name """
+    callers_local_vars = inspect.currentframe().f_back.f_back.f_back.f_locals.items()
+    return [var_name for var_name, var_val in callers_local_vars if var_val is var]
+
+def filter_with_changelog(filter_func:Callable[[Dataset],Dataset],
+                          dataset:Dataset) -> Dataset:
+    """
+    Function, which takes a filter and a dataset.
+    Counts text docs and tokens before and after filtering,
+    Saves filtering changes to log.
+    """
+
+    filter_name = get_var_name(filter_func)
+    pre_filter_docs = len(dataset)
+    pre_filter_tokens= sum(len(word_tokenize(i["text"])) for i in dataset)
+
+    dataset = dataset.filter(filter_func,num_proc=num_proc)
+
+    post_filter_docs = len(dataset)
+    post_filter_tokens= sum(len(word_tokenize(i["text"])) for i in dataset)
+    tokens_removed = round((1-(post_filter_tokens/pre_filter_tokens))*100,2)
+    docs_removed = round((1-(post_filter_docs/pre_filter_docs))*100,2)
+    
+    logger.info(f"FILTER: {filter_name}")
+    logger.info(f"TOKENS: pre: {pre_filter_tokens}, post: {post_filter_tokens}, loss: {tokens_removed}%")
+    logger.info(f"DOCUMENTS: pre: {pre_filter_docs}, post: {post_filter_docs}, loss: {docs_removed}%")
+    
+    return dataset
+
+
+source_filter = lambda ds : doc_type_searchword in ds["doc_type"]
+length_filter = lambda ds: len(word_tokenize(ds["text"])) >= 10
+too_long_filter = lambda ds: len(word_tokenize(ds["text"])) > 1e5
+alpha_filter = lambda ds: alpha_ratio(ds["text"]) >= 0.7
+stop_word_filter = lambda ds: count_min_target(word_tokenize(ds["text"]),spacy_sw,2)
+
+samples_pr_source: dict = defaultdict(lambda: defaultdict(int))
+def language_filter_with_desc_stats(ds:Dataset) -> bool:
+  """ 
+  Language filtering in a streamed dataset while logging all languages 
+  """
+  s = source
+  language = ds["lang_fasttext"]
+  samples_pr_source[s][language] += 1
+
+  language_filter = ds["lang_fasttext"] == "da" and float(ds["lang_fasttext_conf"]) >= 0.75
+
+  return language_filter
+
+def quality_checks(ds:Dataset) -> Dataset:
+    """ 
+    Quality checks for:
+    - no duplicate ids
+    - no duplicate texts
+    - logs texts > 1e5 tokens
+    """
+    #convert to pandas for the drop_duplicates() 
+    df = pd.DataFrame(ds)
+    # remove duplicate ids
+    len_df = len(df)
+    df = df.drop_duplicates(subset=["id"])
+    logger.info(f"Removed {len_df - len(df)} duplicate ids")
+    # remove rows with duplicate text
+    len_df = len(df)
+    df = df.drop_duplicates(subset=["text"])
+    logger.info(f"Removed {len_df - len(df)} rows with duplicate text")
+    #reconvert and remove index
+    ds_f = Dataset.from_pandas(df,preserve_index=False)
+    try:
+        ds_f["__index_level_0__"]
+        ds_f = ds_f.remove_columns("__index_level_0__")
+    except KeyError:
+        pass
+
+    assert len(set(ds_f["id"])) == len(ds_f), "IDs are not unique"
+    assert len(set(ds_f["text"])) == len(ds_f), "Texts are not unique"
+
+    long_texts = ds_f.filter(too_long_filter,num_proc=num_proc)
+    if len(long_texts["id"]) > 0:
+        logger.info(f"{len(long_texts["id"])} Long texts (>~1e5 tokens) found")
+        for id in long_texts["id"]:
+            logger.info(f"id: {id}")
+    else:
+        logger.info("No long texts (>~1e5 tokens) found")
+
+    return ds_f
+
+def main():
+    #load all splits
+    logger.info(f"Loading data from: {hf_path}")
+    danish_data = load_dataset(hf_path, streaming=False, split="train+validation", num_proc=num_proc)
+
+
+    #filter by metadata
+    logger.info(f"Processing source: {source}")
+    danish_data=danish_data.filter(source_filter,num_proc=num_proc)
+
+
+    logger.info(f"Processing language")
+    danish_data=danish_data.filter(language_filter_with_desc_stats, num_proc=None)
+
+    #log language changes
+    log_pre_filter_lang_data(samples_pr_source,danish_data)
+    
+    #convert to dynaword format
+    danish_data = danish_data.map(dynaword_format)
+    danish_data = danish_data.select_columns(["text",
+                                            "source",
+                                            "id",
+                                            "added",
+                                            "created",
+                                            "license",
+                                            "domain",
+                                            "metadata"])
+
+    #filter and log changes
+    danish_data = filter_with_changelog(length_filter,danish_data)
+    danish_data = filter_with_changelog(alpha_filter,danish_data)
+    danish_data = filter_with_changelog(stop_word_filter,danish_data)
+
+    #Quality checks
+    danish_data = quality_checks(danish_data)
+    
+    ### saving
+    save_path = Path(__file__).parent / f"{source}.parquet"
+    danish_data.to_parquet(save_path)
+
+
+
+if __name__ == "__main__":
+    log_path = Path(__file__).parent / f"{source}.log"
+    logging.basicConfig(
+        level=logging.INFO,
+        format="%(asctime)s - %(levelname)s - %(message)s",
+        handlers=[
+            logging.StreamHandler(),
+            logging.FileHandler(log_path),
+        ],
+    )
+    main()

data/ncc_maalfrid/descriptive_stats.json

@@ -0,0 +1,7 @@
+{
+  "number_of_samples": 33336,
+  "average_document_length": 2538.4334953203743,
+  "number_of_tokens": 29260357,
+  "language": "dan, dansk, Danish",
+  "revision": "da633eaca923cc25686bda59a41369694597baac"
+}

data/ncc_maalfrid/ncc_maalfrid.log

@@ -0,0 +1,21 @@
+2025-05-08 09:18:42,463 - INFO - Loading data from: NbAiLab/NCC
+2025-05-08 09:18:54,476 - INFO - Processing source: ncc_maalfrid
+2025-05-08 09:20:23,697 - INFO - Processing language
+2025-05-08 09:36:04,572 - INFO - Documents of ncc_maalfrid:
+2025-05-08 09:36:04,572 - INFO - NO: 4462910, 66.2608% ; DA: 256659, 3.8106%
+2025-05-08 09:36:04,572 - INFO - After language confidence filtering:
+2025-05-08 09:36:04,572 - INFO - DA: 51523, lost: 79.9255%
+2025-05-08 09:36:04,572 - INFO - Total document change:
+2025-05-08 09:36:04,572 - INFO - 6735368 -> 51523, loss: 99.235%
+2025-05-08 09:36:53,427 - INFO - FILTER: ['length_filter']
+2025-05-08 09:36:53,427 - INFO - TOKENS: pre: 21315653, post: 21305735, loss: 0.05%
+2025-05-08 09:36:53,427 - INFO - DOCUMENTS: pre: 51523, post: 49948, loss: 3.06%
+2025-05-08 09:37:37,970 - INFO - FILTER: ['alpha_filter']
+2025-05-08 09:37:37,970 - INFO - TOKENS: pre: 21305735, post: 15216676, loss: 28.58%
+2025-05-08 09:37:37,970 - INFO - DOCUMENTS: pre: 49948, post: 33390, loss: 33.15%
+2025-05-08 09:38:10,796 - INFO - FILTER: ['stop_word_filter']
+2025-05-08 09:38:10,797 - INFO - TOKENS: pre: 15216676, post: 15215917, loss: 0.0%
+2025-05-08 09:38:10,797 - INFO - DOCUMENTS: pre: 33390, post: 33340, loss: 0.15%
+2025-05-08 09:38:20,194 - INFO - Removed 0 duplicate ids
+2025-05-08 09:38:20,297 - INFO - Removed 4 rows with duplicate text
+2025-05-08 09:38:47,894 - INFO - No long texts (>~1e5 tokens) found

data/ncc_maalfrid/ncc_maalfrid.md

@@ -0,0 +1,161 @@
+---
+pretty_name: Norwegian Colossal Corpus (maalfrid)
+language:
+  - da
+license: other
+license_name: NLOD 2.0
+task_categories:
+  - text-generation
+  - fill-mask
+task_ids:
+  - language-modeling
+---
+
+# Dataset Card for Norwegian Colossal Corpus (maalfrid)
+
+<!-- START-SHORT DESCRIPTION -->
+Danish documents from the [Målfrid collection](https://www.nb.no/sprakbanken/en/resource-catalogue/oai-nb-no-sbr-69/) (scraped from websites run by Norwegian state institutions). Extracted from the [Norwegian Colossal Corpus](https://huggingface.co/datasets/NbAiLab/NCC). 
+<!-- END-SHORT DESCRIPTION -->
+
+The Norwegian Colossal Corpus is a collection of multiple smaller Norwegian corpuses suitable for training large language models.  
+(desc. taken from [NCC](https://huggingface.co/datasets/NbAiLab/NCC))
+
+This subset is the result of the following filtering from all available data splits:
+- Document comes from the Målfrid collection
+- Document is classified as Danish with a threshold of 0.75
+- Document has at least 10 words (whitespace separated strings + punctuation)
+- The ratio of all words / words with only alphabetical characters is at least 0.7
+- The document contains at least 2 Danish stop words
+
+
+
+## Dataset Description
+
+<!-- START-DESC-STATS -->
+- **Language**: dan, dansk, Danish
+- **Number of samples**: 33.34K
+- **Number of tokens (Llama 3)**: 29.26M
+- **Average document length (characters)**: 2538.43
+<!-- END-DESC-STATS -->
+
+
+## Dataset Structure
+An example from the dataset looks as follows.
+<!-- START-SAMPLE -->
+```py
+{
+  "text": "Anno 1815, Torsdagen den 5te Octbr. blev i Sagen Fuldm�gtig Engebrethsen contra Snedkermester Hansen[...]",
+  "source": "ncc_maalfrid",
+  "id": "maalfrid_56267641f4d6de44ab69875a31634e31e68db1a8_166",
+  "added": "2025-05-08",
+  "created": "2021-01-01, 2021-12-31",
+  "license": "other",
+  "domain": "Legal",
+  "metadata": {
+    "source-pretty": "Norwegian Colossal Corpus (maalfrid)",
+    "source-type": "maalfrid_arkivverket"
+  }
+}
+```
+
+### Data Fields
+
+An entry in the dataset consists of the following fields:
+
+- `text`(`str`): The content of the document.
+- `source` (`str`): The source of the document (see [Source Data](#source-data)).
+- `id` (`str`): An unique identifier for each document.
+- `added` (`str`): An date for when the document was added to this collection.
+- `created` (`str`): An date range for when the document was originally created.
+- `license` (`str`): The license of the document. The licenses vary according to the source. 
+- `domain` (`str`): The domain of the source
+- `metadata/source-pretty` (`str`): The long form version of the short-form source name
+- `metadata/*`: Potentially additional metadata
+<!-- END-SAMPLE -->
+
+
+
+### Dataset Statistics
+
+<!-- START-DATASET PLOTS -->
+<img src="./images/dist_document_length.png" width="600" style="margin-right: 10px;" />
+<img>
+<!-- END-DATASET PLOTS -->
+
+## Additional Information
+
+## License Information
+[NLOD 2.0](https://data.norge.no/nlod/en/2.0)
+
+## Filtering
+### Pre filtering 
+Documents, which are tagged as "maalfrid*" in the "doc_type" column of the NCC  
+
+Norwegian documents: 4462910  
+~ 66.2% of data    
+Danish documents: 256659    
+~ 3.8% of data  
+
+### Classification confidence
+Removing documents, where the "lang_fasttext_conf" column of the NCC is less than 0.75
+
+Documents:  51523     
+Loss: ~ 79.9% 
+
+### Length
+Removing documents based on the "text" column of the NCC. Text is tokenized by splitting on whitespace. Common punctuation marks are counted as separate tokens.
+
+Texts with less than 10 tokens are removed.  
+TOKENS: pre: 21315653, post: 21305735, loss: 0.05%  
+DOCUMENTS: pre: 51523, post: 49948, loss: 3.06%
+
+### Alpha Ratio
+Texts, in which the ratio of words and words with only alphabetic characters is less than 0.7, are removed.
+
+TOKENS: pre: 21305735, post: 15216676, loss: 28.58%  
+DOCUMENTS: pre: 49948, post: 33390, loss: 33.15% 
+
+### Stop Words
+Texts which contain less then 2 stop words are removed.  
+TOKENS: pre: 15216676, post: 15215917, loss: 0.0%  
+DOCUMENTS: pre: 33390, post: 33340, loss: 0.15%
+### Quality
+4 duplicate texts were removed  
+
+
+### Citation Information
+```
+@inproceedings{kummervold-etal-2022-norwegian-colossal,
+  title     = {The {N}orwegian colossal corpus: A text corpus for training large {N}orwegian language models},
+  author    = {Kummervold, Per E  and
+               Wetjen, Freddy    and
+               De la Rosa, Javier},
+  booktitle = {Proceedings of the Thirteenth Language Resources and Evaluation Conference (LREC)},
+  year      = {2022},
+  address   = {Marseille, France},
+  publisher = {European Language Resources Association},
+  url       = {https://aclanthology.org/2022.lrec-1.410},
+  pages     = {3852--3860},
+  abstract  = {Norwegian has been one of many languages lacking sufficient available text to train quality language models. In an attempt to bridge this gap, we introduce the Norwegian Colossal Corpus (NCC), which comprises 49GB of clean Norwegian textual data containing over 7B words. The NCC is composed of different and varied sources, ranging from books and newspapers to government documents and public reports, showcasing the various uses of the Norwegian language in society. The corpus contains mainly Norwegian Bokmål and Norwegian Nynorsk. Each document in the corpus is tagged with metadata that enables the creation of sub-corpora for specific needs. Its structure makes it easy to combine with large web archives that for licensing reasons could not be distributed together with the NCC. By releasing this corpus openly to the public, we hope to foster the creation of both better Norwegian language models and multilingual language models with support for Norwegian.},
+}
+
+@inproceedings{kummervold-etal-2021-operationalizing,
+  title     = {Operationalizing a National Digital Library: The Case for a {N}orwegian Transformer Model},
+  author    = {Kummervold, Per E  and
+               De la Rosa, Javier  and
+               Wetjen, Freddy  and
+               Brygfjeld, Svein Arne},
+  booktitle = {Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa)},
+  year      = {2021},
+  address   = {Reykjavik, Iceland (Online)},
+  publisher = {Linköping University Electronic Press, Sweden},
+  url       = {https://aclanthology.org/2021.nodalida-main.3},
+  pages     = {20--29},
+  abstract  = {In this work, we show the process of building a large-scale training set from digital and digitized collections at a national library.
+  The resulting Bidirectional Encoder Representations from Transformers (BERT)-based language model for Norwegian outperforms multilingual BERT (mBERT) models
+  in several token and sequence classification tasks for both Norwegian Bokmål and Norwegian Nynorsk. Our model also improves the mBERT performance for other
+  languages present in the corpus such as English, Swedish, and Danish. For languages not included in the corpus, the weights degrade moderately while keeping strong multilingual properties. Therefore,
+  we show that building high-quality models within a memory institution using somewhat noisy optical character recognition (OCR) content is feasible, and we hope to pave the way for other memory institutions to follow.},
+}
+
+```

data/ncc_maalfrid/ncc_maalfrid.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cbd78231ab0d8bf217d43e74fff25f6746f1667219002242ab9fe93e8f660ae2
+size 54719348

data/ncc_newspapers/create.py

@@ -0,0 +1,318 @@
+# /// script
+# requires-python = ">=3.12"
+# dependencies = [
+#     "datasets>=3.2.0"
+# ]
+# ///
+
+import logging
+import re
+import inspect
+
+from pathlib import Path
+from datetime import datetime
+from collections import defaultdict
+from collections.abc import Callable
+
+import pandas as pd
+from datasets import Dataset, load_dataset
+
+logger = logging.getLogger(__name__)
+########## edit manually for each source
+hf_path = "NbAiLab/NCC"
+source = "ncc_newspapers"
+doc_type_searchword = "newspaper"
+license = "cc0-1.0"
+domain = "News"
+num_proc = 10
+##########
+today = datetime.now().strftime("%Y-%m-%d")
+
+#stop words taken from spaCy
+#https://github.com/explosion/spaCy/blob/master/spacy/lang/da/stop_words.py
+# Source: Handpicked by Jens Dahl Møllerhøj.
+spacy_sw = set(
+    """
+af aldrig alene alle allerede alligevel alt altid anden andet andre at
+
+bag begge blandt blev blive bliver burde bør
+
+da de dem den denne dens der derefter deres derfor derfra deri dermed derpå derved det dette dig din dine disse dog du
+
+efter egen eller ellers en end endnu ene eneste enhver ens enten er et
+
+flere flest fleste for foran fordi forrige fra få før først
+
+gennem gjorde gjort god gør gøre gørende
+
+ham han hans har havde have hel heller hen hende hendes henover her herefter heri hermed herpå hun hvad hvem hver hvilke hvilken hvilkes hvis hvor hvordan hvorefter hvorfor hvorfra hvorhen hvori hvorimod hvornår hvorved
+
+i igen igennem ikke imellem imens imod ind indtil ingen intet
+
+jeg jer jeres jo
+
+kan kom kommer kun kunne
+
+lad langs lav lave lavet lidt lige ligesom lille længere
+
+man mange med meget mellem men mens mere mest mig min mindre mindst mine mit må måske
+
+ned nemlig nogen nogensinde noget nogle nok nu ny nyt nær næste næsten
+
+og også om omkring op os over overalt
+
+på
+
+samme sammen selv selvom senere ses siden sig sige skal skulle som stadig synes syntes så sådan således
+
+temmelig tidligere til tilbage tit
+
+ud uden udover under undtagen
+
+var ved vi via vil ville vore vores vær være været
+
+øvrigt
+""".split()
+)
+
+def word_tokenize(text: str) -> list[str]:
+    """
+    Tokenizes a string into words, splitting on whitespace and punctuation.
+
+    Example:
+        >>> word_tokenize("Hello, world!")
+        ['Hello', ',', 'world', '!']
+        >>> word_tokenize("This is a test.")
+        ['This', 'is', 'a', 'test', '.']
+        >>> word_tokenize("Many spaces    between words.")
+        ['Many', 'spaces', 'between', 'words', '.']
+    """
+
+    punkt = [",", ".", "!", "?", ":", ";", "(", ")", "[", "]", "{", "}", '"', "'"]
+    for p in punkt:
+        text = text.replace(p, f" {p} ")
+    return text.split()
+
+def alpha_ratio(text: str | list[str]) -> float:
+    """
+    If not split already to words, splits text with word_tokenize()
+    Calculates ratio of words with only alphabetical characters
+
+    """
+    if type(text) is str:
+        text = word_tokenize(text)
+    else:
+        pass
+
+    alpha_ratio = 1 - sum(not word.isalpha() for word in text) / len(text)
+
+    return alpha_ratio
+
+def count_min_target(given_list: list, target_list: list, min: int) -> bool:
+    """
+    Iterates through given list, until at least min items match any items from target list
+
+    """
+    c_item = 0
+    given_list_iter = iter(given_list)
+    while c_item < min:
+        try:
+            current_item = next(given_list_iter)
+            if current_item in target_list:
+                c_item += 1
+        except StopIteration:
+            break
+
+    return c_item == min
+
+def dynaword_format(
+    meta_document: dict[str, str | int]
+    ) -> dict[str, str | dict[str, str]]:
+    """Reformats data to fit dynaword standards"""
+
+    text = meta_document.get("text")
+    id = meta_document.get("id")
+    date = meta_document.get("publish_year")
+    doc_type = meta_document.get("doc_type")
+
+    newdata = {
+         "text": text,
+         "source": source,
+         "id": id,
+         "added": today,
+         "created": f"{date}-01-01, {date}-12-31",
+         "license": license,
+         "domain": domain,
+         "metadata": {
+                "source-pretty": f"Norwegian Colossal Corpus ({re.sub("ncc_","",source)})",
+                "source-type": doc_type,
+            },
+        }
+
+    return newdata
+
+def log_pre_filter_lang_data(lang_metadata : dict[str,dict[str,int]],
+                              filtered_ds: Dataset):
+    """
+    Function for logging changes in a large dataset,
+    based on the metadata pre filering and the filtered dataset,
+    used for language filtering
+    """
+    all_docs = sum(lang_metadata[source].values())
+    no_docs = lang_metadata[source].get("no")
+    da_docs = lang_metadata[source].get("da")
+    no_perc = round(no_docs/all_docs*100,4)
+    da_perc = round(da_docs/all_docs*100,4)
+
+    f_length = len(filtered_ds)
+    f_perc = round(f_length/da_docs*100,4)
+    f_total_perc = round(f_length/all_docs*100,4)
+
+    logger.info(f"Documents of {source}:")
+    logger.info(f"NO: {no_docs}, {no_perc}% ; DA: {da_docs}, {da_perc}%")
+    logger.info(f"After language confidence filtering:")
+    logger.info(f"DA: {f_length}, lost: {100-f_perc}%")
+    logger.info(f"Total document change:")
+    logger.info(f"{all_docs} -> {f_length}, loss: {100-f_total_perc}%")
+
+def get_var_name(var):
+    """ outputs the variable name """
+    callers_local_vars = inspect.currentframe().f_back.f_back.f_back.f_locals.items()
+    return [var_name for var_name, var_val in callers_local_vars if var_val is var]
+
+def filter_with_changelog(filter_func:Callable[[Dataset],Dataset],
+                          dataset:Dataset) -> Dataset:
+    """
+    Function, which takes a filter and a dataset.
+    Counts text docs and tokens before and after filtering,
+    Saves filtering changes to log.
+    """
+
+    filter_name = get_var_name(filter_func)
+    pre_filter_docs = len(dataset)
+    pre_filter_tokens= sum(len(word_tokenize(i["text"])) for i in dataset)
+
+    dataset = dataset.filter(filter_func,num_proc=num_proc)
+
+    post_filter_docs = len(dataset)
+    post_filter_tokens= sum(len(word_tokenize(i["text"])) for i in dataset)
+    tokens_removed = round((1-(post_filter_tokens/pre_filter_tokens))*100,2)
+    docs_removed = round((1-(post_filter_docs/pre_filter_docs))*100,2)
+    
+    logger.info(f"FILTER: {filter_name}")
+    logger.info(f"TOKENS: pre: {pre_filter_tokens}, post: {post_filter_tokens}, loss: {tokens_removed}%")
+    logger.info(f"DOCUMENTS: pre: {pre_filter_docs}, post: {post_filter_docs}, loss: {docs_removed}%")
+    
+    return dataset
+
+
+source_filter = lambda ds : doc_type_searchword in ds["doc_type"]
+length_filter = lambda ds: len(word_tokenize(ds["text"])) >= 10
+too_long_filter = lambda ds: len(word_tokenize(ds["text"])) > 1e5
+alpha_filter = lambda ds: alpha_ratio(ds["text"]) >= 0.7
+stop_word_filter = lambda ds: count_min_target(word_tokenize(ds["text"]),spacy_sw,2)
+
+samples_pr_source: dict = defaultdict(lambda: defaultdict(int))
+def language_filter_with_desc_stats(ds:Dataset) -> bool:
+  """ 
+  Language filtering in a streamed dataset while logging all languages 
+  """
+  s = source
+  language = ds["lang_fasttext"]
+  samples_pr_source[s][language] += 1
+
+  language_filter = ds["lang_fasttext"] == "da" and float(ds["lang_fasttext_conf"]) >= 0.75
+
+  return language_filter
+
+def quality_checks(ds:Dataset) -> Dataset:
+    """ 
+    Quality checks for:
+    - no duplicate ids
+    - no duplicate texts
+    - logs texts > 1e5 tokens
+    """
+    #convert to pandas for the drop_duplicates() 
+    df = pd.DataFrame(ds)
+    # remove duplicate ids
+    len_df = len(df)
+    df = df.drop_duplicates(subset=["id"])
+    logger.info(f"Removed {len_df - len(df)} duplicate ids")
+    # remove rows with duplicate text
+    len_df = len(df)
+    df = df.drop_duplicates(subset=["text"])
+    logger.info(f"Removed {len_df - len(df)} rows with duplicate text")
+    #reconvert and remove index
+    ds_f = Dataset.from_pandas(df,preserve_index=False)
+    try:
+        ds_f["__index_level_0__"]
+        ds_f = ds_f.remove_columns("__index_level_0__")
+    except KeyError:
+        pass
+
+    assert len(set(ds_f["id"])) == len(ds_f), "IDs are not unique"
+    assert len(set(ds_f["text"])) == len(ds_f), "Texts are not unique"
+
+    long_texts = ds_f.filter(too_long_filter,num_proc=None)
+    if len(long_texts["id"]) > 0:
+        logger.info(f"{len(long_texts["id"])} Long texts (>~1e5 tokens) found")
+        for id in long_texts["id"]:
+            logger.info(f"id: {id}")
+    else:
+        logger.info("No long texts (>~1e5 tokens) found")
+
+    return ds_f
+
+def main():
+    #load all splits
+    logger.info(f"Loading data from: {hf_path}")
+    danish_data = load_dataset(hf_path, streaming=False, split="train+validation", num_proc=num_proc)
+
+
+    #filter by metadata
+    logger.info(f"Processing source: {source}")
+    danish_data=danish_data.filter(source_filter,num_proc=num_proc)
+
+
+    logger.info(f"Processing language")
+    danish_data=danish_data.filter(language_filter_with_desc_stats, num_proc=None)
+
+    #log language changes
+    log_pre_filter_lang_data(samples_pr_source,danish_data)
+    
+    #convert to dynaword format
+    danish_data = danish_data.map(dynaword_format)
+    danish_data = danish_data.select_columns(["text",
+                                            "source",
+                                            "id",
+                                            "added",
+                                            "created",
+                                            "license",
+                                            "domain",
+                                            "metadata"])
+
+    #filter and log changes
+    danish_data = filter_with_changelog(length_filter,danish_data)
+    danish_data = filter_with_changelog(alpha_filter,danish_data)
+    danish_data = filter_with_changelog(stop_word_filter,danish_data)
+
+    #Quality checks
+    danish_data = quality_checks(danish_data)
+    
+    ### saving
+    save_path = Path(__file__).parent / f"{source}.parquet"
+    danish_data.to_parquet(save_path)
+
+
+
+if __name__ == "__main__":
+    log_path = Path(__file__).parent / f"{source}.log"
+    logging.basicConfig(
+        level=logging.INFO,
+        format="%(asctime)s - %(levelname)s - %(message)s",
+        handlers=[
+            logging.StreamHandler(),
+            logging.FileHandler(log_path),
+        ],
+    )
+    main()

data/ncc_newspapers/descriptive_stats.json

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+{
+  "number_of_samples": 707,
+  "average_document_length": 793.6053748231966,
+  "number_of_tokens": 191082,
+  "language": "dan, dansk, Danish",
+  "revision": "da633eaca923cc25686bda59a41369694597baac"
+}

data/ncc_newspapers/ncc_newspapers.log

@@ -0,0 +1,21 @@
+2025-05-08 08:44:51,942 - INFO - Loading data from: NbAiLab/NCC
+2025-05-08 08:45:05,442 - INFO - Processing source: ncc_newspapers
+2025-05-08 08:46:51,180 - INFO - Processing language
+2025-05-08 08:48:03,192 - INFO - Documents of ncc_newspapers:
+2025-05-08 08:48:03,192 - INFO - NO: 487086, 73.2081% ; DA: 17516, 2.6326%
+2025-05-08 08:48:03,192 - INFO - After language confidence filtering:
+2025-05-08 08:48:03,192 - INFO - DA: 1162, lost: 93.3661%
+2025-05-08 08:48:03,192 - INFO - Total document change:
+2025-05-08 08:48:03,192 - INFO - 665344 -> 1162, loss: 99.8254%
+2025-05-08 08:48:17,499 - INFO - FILTER: ['length_filter']
+2025-05-08 08:48:17,499 - INFO - TOKENS: pre: 119536, post: 117538, loss: 1.67%
+2025-05-08 08:48:17,499 - INFO - DOCUMENTS: pre: 1162, post: 843, loss: 27.45%
+2025-05-08 08:48:28,912 - INFO - FILTER: ['alpha_filter']
+2025-05-08 08:48:28,912 - INFO - TOKENS: pre: 117538, post: 111512, loss: 5.13%
+2025-05-08 08:48:28,912 - INFO - DOCUMENTS: pre: 843, post: 722, loss: 14.35%
+2025-05-08 08:48:42,727 - INFO - FILTER: ['stop_word_filter']
+2025-05-08 08:48:42,727 - INFO - TOKENS: pre: 111512, post: 111341, loss: 0.15%
+2025-05-08 08:48:42,727 - INFO - DOCUMENTS: pre: 722, post: 707, loss: 2.08%
+2025-05-08 08:48:42,928 - INFO - Removed 0 duplicate ids
+2025-05-08 08:48:42,938 - INFO - Removed 0 rows with duplicate text
+2025-05-08 08:48:43,036 - INFO - No long texts (>~1e5 tokens) found

data/ncc_newspapers/ncc_newspapers.md

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+---
+pretty_name: Norwegian Colossal Corpus (newspapers)
+language:
+  - da
+license: cc0-1.0
+license_name: CC0 1.0
+task_categories:
+  - text-generation
+  - fill-mask
+task_ids:
+  - language-modeling
+---
+
+# Dataset Card for Norwegian Colossal Corpus (newspapers)
+
+<!-- START-SHORT DESCRIPTION -->
+Danish Newspapers extracted from the [Norwegian Colossal Corpus](https://huggingface.co/datasets/NbAiLab/NCC) derived from OCR. 
+<!-- END-SHORT DESCRIPTION -->
+
+The Norwegian Colossal Corpus is a collection of multiple smaller Norwegian corpuses suitable for training large language models.  
+(desc. taken from [NCC](https://huggingface.co/datasets/NbAiLab/NCC))
+
+This subset is the result of the following filtering from all available data splits:
+- Document comes from newspaper articles
+- Document is classified as Danish with a threshold of 0.75
+- Document has at least 10 words (whitespace separated strings + punctuation)
+- The ratio of all words / words with only alphabetical characters is at least 0.7
+- The document contains at least 2 Danish stop words
+
+
+
+## Dataset Description
+
+<!-- START-DESC-STATS -->
+- **Language**: dan, dansk, Danish
+- **Number of samples**: 707
+- **Number of tokens (Llama 3)**: 191.08K
+- **Average document length (characters)**: 793.61
+<!-- END-DESC-STATS -->
+
+
+## Dataset Structure
+An example from the dataset looks as follows.
+<!-- START-SAMPLE -->
+```py
+{
+  "text": "Det blir ikke Kipparifestival i �r. Dette har arrang�rene meddelt. De er slitne.",
+  "source": "ncc_newspapers",
+  "id": "ruijankaiku_null_null_20120130_18_1_1_MODSMD_ARTICLE35",
+  "added": "2025-05-08",
+  "created": "2012-01-01, 2012-12-31",
+  "license": "cc0-1.0",
+  "domain": "News",
+  "metadata": {
+    "source-pretty": "Norwegian Colossal Corpus (newspapers)",
+    "source-type": "newspaper_ocr"
+  }
+}
+```
+
+### Data Fields
+
+An entry in the dataset consists of the following fields:
+
+- `text`(`str`): The content of the document.
+- `source` (`str`): The source of the document (see [Source Data](#source-data)).
+- `id` (`str`): An unique identifier for each document.
+- `added` (`str`): An date for when the document was added to this collection.
+- `created` (`str`): An date range for when the document was originally created.
+- `license` (`str`): The license of the document. The licenses vary according to the source. 
+- `domain` (`str`): The domain of the source
+- `metadata/source-pretty` (`str`): The long form version of the short-form source name
+- `metadata/*`: Potentially additional metadata
+<!-- END-SAMPLE -->
+
+
+
+### Dataset Statistics
+
+<!-- START-DATASET PLOTS -->
+<img src="./images/dist_document_length.png" width="600" style="margin-right: 10px;" />
+<img>
+<!-- END-DATASET PLOTS -->
+
+## Additional Information
+
+## License Information
+[CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/)
+
+## Filtering
+### Pre filtering 
+Documents, which are tagged as "newspaper*" in the "doc_type" column of the NCC  
+
+Norwegian documents: 487086  
+~73.2% of data    
+Danish documents: 17516  
+~2.6% of data  
+
+### Classification confidence
+Removing documents, where the "lang_fasttext_conf" column of the NCC is less than 0.75
+
+Documents: 1162   
+Loss: ~93.42% 
+
+### Length
+Removing documents based on the "text" column of the NCC. Text is tokenized by splitting on whitespace. Common punctuation marks are counted as separate tokens.
+
+Texts with less than 10 tokens are removed.  
+TOKENS: pre: 119536, post: 117538, loss: 1.67%  
+DOCUMENTS: pre: 1162, post: 843, loss: 27.45%
+
+### Alpha Ratio
+Texts, in which the ratio of words and words with only alphabetic characters is less than 0.7, are removed.
+
+TOKENS: pre: 117538, post: 111512, loss: 5.13%  
+DOCUMENTS: pre: 843, post: 722, loss: 14.35%
+
+### Stop Words
+Texts which contain less then 2 stop words are removed.  
+TOKENS: pre: 111512, post: 111341, loss: 0.15%  
+DOCUMENTS: pre: 722, post: 707, loss: 2.08%
+### Quality
+
+It is important to note, that recurring ocr mistakes and archaic expressions in older texts might hinder the legibility of some of the documents and make differentiating between Norwegian and Danish difficult.
+<details>
+<summary>Examples</summary>
+<br>
+
+- De nartotifte Stoffer, vi benytte. ae Tobat...  
+
+- Sqmfruer hav. baaret lige faadan mpffe og Prydelfe...
+
+</details>
+
+
+### Citation Information
+```
+@inproceedings{kummervold-etal-2022-norwegian-colossal,
+  title     = {The {N}orwegian colossal corpus: A text corpus for training large {N}orwegian language models},
+  author    = {Kummervold, Per E  and
+               Wetjen, Freddy    and
+               De la Rosa, Javier},
+  booktitle = {Proceedings of the Thirteenth Language Resources and Evaluation Conference (LREC)},
+  year      = {2022},
+  address   = {Marseille, France},
+  publisher = {European Language Resources Association},
+  url       = {https://aclanthology.org/2022.lrec-1.410},
+  pages     = {3852--3860},
+  abstract  = {Norwegian has been one of many languages lacking sufficient available text to train quality language models. In an attempt to bridge this gap, we introduce the Norwegian Colossal Corpus (NCC), which comprises 49GB of clean Norwegian textual data containing over 7B words. The NCC is composed of different and varied sources, ranging from books and newspapers to government documents and public reports, showcasing the various uses of the Norwegian language in society. The corpus contains mainly Norwegian Bokmål and Norwegian Nynorsk. Each document in the corpus is tagged with metadata that enables the creation of sub-corpora for specific needs. Its structure makes it easy to combine with large web archives that for licensing reasons could not be distributed together with the NCC. By releasing this corpus openly to the public, we hope to foster the creation of both better Norwegian language models and multilingual language models with support for Norwegian.},
+}
+
+@inproceedings{kummervold-etal-2021-operationalizing,
+  title     = {Operationalizing a National Digital Library: The Case for a {N}orwegian Transformer Model},
+  author    = {Kummervold, Per E  and
+               De la Rosa, Javier  and
+               Wetjen, Freddy  and
+               Brygfjeld, Svein Arne},
+  booktitle = {Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa)},
+  year      = {2021},
+  address   = {Reykjavik, Iceland (Online)},
+  publisher = {Linköping University Electronic Press, Sweden},
+  url       = {https://aclanthology.org/2021.nodalida-main.3},
+  pages     = {20--29},
+  abstract  = {In this work, we show the process of building a large-scale training set from digital and digitized collections at a national library.
+  The resulting Bidirectional Encoder Representations from Transformers (BERT)-based language model for Norwegian outperforms multilingual BERT (mBERT) models
+  in several token and sequence classification tasks for both Norwegian Bokmål and Norwegian Nynorsk. Our model also improves the mBERT performance for other
+  languages present in the corpus such as English, Swedish, and Danish. For languages not included in the corpus, the weights degrade moderately while keeping strong multilingual properties. Therefore,
+  we show that building high-quality models within a memory institution using somewhat noisy optical character recognition (OCR) content is feasible, and we hope to pave the way for other memory institutions to follow.},
+}
+
+```

data/ncc_newspapers/ncc_newspapers.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9b7c44c949f42d2fee6cab10b6b565511374592e57ce6f12aa1c1e3f3f57f485
+size 427218