README v2

README.md

@@ -1,102 +1,139 @@
----
-license: cc0-1.0
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/*/*.arrow
----
-
-# KB-Books
-
-
-## Dataset Description  
-
-
-### Dataset Summary
-
-Documents from the [Royal Danish Library](https://www.kb.dk/en) published between 1750 and 1930. 
-
-The dataset has each page of each document in image and text format. The text was extracted with [OCR](https://en.wikipedia.org/wiki/Optical_character_recognition) at the time of digitization.
-
-The documents (books of various genres) were obtained from the library in .pdf format with additional metadata as .json files. The dataset was assembled to make these public domain Danish texts more accessible.
-
-### Languages
-
-All texts are in Danish.
-
-## Dataset Structure
-
-### Data Instances
-
-```
-{
-  "doc_id": "unique document identifier",
-  "page_id": "unique page identifier",
-  "page_image":"image of the page, extracted from a pdf",
-  "page_text": "OCRed text of the page, extracted from a pdf",
-  "author" : "name of the author. If more than one, separated by ';' ",
-  "title" : "document title",
-  "published" : "year of publishing",
-  "digitalized" : "year of processing the physical document by the library",
-  "file_name" : "file_name of the original PDF"
-}
-```
-
-The "page_text" was obtained through OCR, and is therefore likely to contain noisy data, especially in older documents, where the original text is either handwritten or printed in exaggerated fonts.
-
-"author" and "title" may be missing, especially in documents published before 1833.
-
-"digitalized" may be missing.
-
-
-### Data Splits
-
-All data is in the "train" split.  
-Data in [./data](./data/) is organized by year of publication, and is segmented into ~5GB chunks.
-
-
-## Dataset Creation
-
-### Curation Rationale
-The dataset makes public domain text data more accessible to whomever may wish to view it or use it.
-
-The dataset was created to be used mainly for research purposes and Natural Language Processing tasks. 
-
-The documents were filtered to make sure no non-public domain data is added. See [pd_check.md](./pd_check/pd_check.md) for the confirming of public domain status and [scraping.md](./scrape/scraping.md) for collecting possible Danish authors.
-
-**IMPORTANT: In case non-public domain data is found in the dataset, please let us know**
-
-### Source Data
-
-Data consists of OCRed documents from the [Royal Danish Library](https://www.kb.dk/en) published between 1750 and 1930.
-These documents are mostly books of various genres. No distinction was made among the documents based on genre. Additional to the text, the original PDF pages are also added as images for potentially improving the quality of text.
-
-The source data was made by humans, chiefly danish speaking authors, poets and playwrights.
-
-### Data Extraction
-
-#### Logic
-
-The flowchart is for a broad understanding and is not a fully accurate representation.
-
-![Logic flowchart](./imgs/extract_flowchart.jpg)
-
-The whole python script is provided for reference as [extract_data.py](./extract_data.py)  
-
-Made with:  
-
-- python 3.12.10  
-
-Required libraries for running:  
-
-- [PyMuPDF](https://pypi.org/project/PyMuPDF/) 1.26.0
-- [datasets](https://pypi.org/project/datasets/)  3.5.0
-
-
-## Additional Information
-
-### Dataset Curators
-***write something here***
-### License
+---
+license: cc0-1.0
+---
+
+# open-rdl-books
+
+
+## Dataset Description  
+
+|  |  |
+| ----------- | ----------- |
+| **Language** | dan, dansk, Danish |
+| **License** | Public Domain, cc0-1.0 |
+
+
+
+### Dataset Summary
+
+Documents from the [Royal Danish Library](https://www.kb.dk/en) published between 1750 and 1930. 
+
+The dataset has each page of each document in image and text format. The text was extracted with [OCR](https://en.wikipedia.org/wiki/Optical_character_recognition).
+
+The documents (books of various genres) were obtained from the library. The dataset was assembled to make these public domain Danish texts more accessible.
+
+## Dataset Structure
+
+### Data Instances
+
+```
+{
+  "doc_id": "unique document identifier",
+  "page_id": "unique page identifier",
+  "page_image":"image of the page, extracted from a pdf",
+  "page_text": "OCRed text of the page, extracted from a pdf",
+  "author" : "name of the author. If more than one, separated by ';' ",
+  "title" : "document title",
+  "published" : "year of publishing",
+  "digitalized" : "year of processing the physical document by the library",
+  "file_name" : "file_name of the original PDF"
+}
+```
+
+The "page_text" was obtained through OCR, and is therefore likely to contain noisy data, especially in older documents, where the original text is either handwritten or printed in exaggerated fonts.
+
+"author" and "title" may be missing, especially in documents published before 1833.
+
+"digitalized" may be missing.
+
+
+### Data Splits
+
+All data is in the "train" split.  
+[Data](https://huggingface.co/datasets/chcaa/kb-books/tree/main/data) is organized by year of publication, and is segmented into ~5GB chunks.
+
+
+
+## Dataset Creation
+
+### Curation Rationale
+The dataset makes public domain text data more accessible to whomever may wish to view it or use it.
+
+The dataset was created for the projects:  
+- [Danish Foundation Models](https://www.foundationmodels.dk)  
+- [Golden Matrix](https://chc.au.dk/research/golden-matrix)
+
+The documents were filtered to make sure no non-public domain data is added. See [pd_check.md](./pd_check/pd_check.md) for the confirming of public domain status and [scraping.md](./scrape/scraping.md) for collecting possible Danish authors.
+
+
+### Source Data
+
+Data consists of OCRed documents from the [Royal Danish Library](https://www.kb.dk/en) published between 1750 and 1930.
+These documents are mostly books of various genres. No distinction was made among the documents based on genre. Additional to the text, the original PDF pages are also added as images for potentially improving the quality of text.
+
+The source data was made by humans, chiefly danish speaking authors, poets and playwrights.
+
+### Data Extraction
+
+#### Logic
+The whole python script is provided for reference as [extract_data.py](./extract_data.py)  
+For more detailed information the other scripts see
+[pd_check](./pd_check/pd_check.md) and [scraping](./scrape/scraping.md)
+
+<details>
+<summary>General Data Extraction</summary>
+<br>
+ flowchart is for a broad understanding and is not a fully accurate representation.
+
+![Logic flowchart](./imgs/extract_flowchart.jpg)
+
+</details>
+
+<details>
+<summary>Confirm Public Domain</summary>
+<br>
+ flowchart is for a broad understanding and is not a fully accurate representation.
+
+![Logic flowchart]()
+
+</details>
+
+
+## Additional Information
+
+### Citation
+
+If you use this work please cite: 
+ ```
+@report{open-rdl-books,
+  title={Public domain books from the Danish Royal Library},
+  author={Szabo, Balazs and Vahlstrup, Peter and Møldrup-Dalum, Per and Nielbo, Kristoffer L. and Enevoldsen, Kenneth},
+  year={2025}
+}
+```
+### Personal and Sensitive Information
+
+This dataset is under the public domain and, to our knowledge, does not contain personally sensitive information.
+
+### Bias, Risks, and Limitations
+
+The works in this collection are historical, and thus they reflect the linguistic, cultural, and ideological norms of their respective times. As such, it includes perspectives, assumptions, and biases characteristic of the period, likely including stances that we would now deem inappropriate.
+
+### Notice and takedown policy  
+We redistribute files under the public domain or with a license permitting redistribution.  
+If you have concerns about the licensing of these files, please contact us. If you consider that the data contains material that infringes your copyright, please:
+
+Clearly identify yourself with detailed contact information, such as an address, a telephone number, or an email address at which you can be contacted.  
+Clearly reference the original work claimed to be infringed  
+Clearly identify the material claimed to be infringing and information reasonably sufficient to allow us to locate the material.  
+We will comply with legitimate requests by removing the affected sources from the next release of the dataset.
+
+### Dataset Curators
+
+The dataset was created for use in these projects by the [Center for Humanities Computing](https://chc.au.dk/) at Aarhus University:  
+- [Danish Foundation Models](https://www.foundationmodels.dk)  
+- [Golden Matrix](https://chc.au.dk/research/golden-matrix)
+
+
+### License
 The documents in the dataset are part of the [public domain](https://creativecommons.org/public-domain/)

extract_data.py

@@ -1,3 +1,10 @@
+# /// script
+# requires-python = "==3.12"
+# dependencies = [
+#     "PyMuPDF>=1.26.0",
+#     "datasets>=3.5.0",
+# ]
+# ///
 import os
 import re
 import string
@@ -17,15 +24,15 @@ source = "kb_books"
 #how many years should go in one parquet file (do not change!)
 n_chunks = 1
 #how many docs in 1 parquet
-n_batch = 5
+n_batch = 1
 #paths
 input_path = os.path.join("..","..","kb-books","raw")
 output_path = os.path.join(".","data")
 logs = os.path.join(".","log")
 #first year to process
-start_year = 1751
+start_year = 1876
 #last year to process
-stop_year = 1752
+stop_year = 1880
 #misc folders in data
 unwanted_folders = ("README.txt","logs")
 #demo run for testing, if true, only first page is read
@@ -461,7 +468,7 @@ def main():
     temporary_ds = None
     del ds
     del temporary_ds
-    reorganize_data(output_path)
+    #reorganize_data(output_path)
     
 
 if __name__ == "__main__":

pd_check/select_pd.py

@@ -1,3 +1,10 @@
+# /// script
+# requires-python = "==3.12"
+# dependencies = [
+#     "PyMuPDF>=1.26.0",
+#     "datasets>=3.5.0",
+# ]
+# ///
 import os
 import re
 import string

reorganize_data.py

@@ -0,0 +1,82 @@
+# /// script
+# requires-python = "==3.12"
+# dependencies = [
+#     "PyMuPDF>=1.26.0",
+#     "datasets>=3.5.0",
+# ]
+# ///
+import os
+import gc
+import re
+import string
+import json
+import logging
+import shutil
+from datetime import datetime
+from tqdm import tqdm
+
+import fitz
+from datasets import Dataset, load_dataset
+
+
+output_path = os.path.join(".","data")
+
+def remove(path):
+    """ param <path> could either be relative or absolute. """
+    if os.path.isfile(path) or os.path.islink(path):
+        os.remove(path)  # remove the file
+    elif os.path.isdir(path):
+        shutil.rmtree(path)  # remove dir and all contains
+    else:
+        raise ValueError("file {} is not a file or dir.".format(path))
+
+def reorganize_data(output_path: str, shard_size: str = "5"):
+    """ Loads the temporary data folders in the data path and creates 5GB shards for each year, deletes temporary files
+    """
+    folders = os.listdir(output_path)
+    temp_folders = [i for i in folders if "_t" in i]
+    if len(temp_folders) == 0:
+        print("DATA ORGANIZED")
+        return
+    print("REORGANIZING DATA...")
+    for t_fold in tqdm(temp_folders):
+        #load all separate parquets into 1 ds
+        data_path = os.path.join(output_path,t_fold)
+        data_set = load_dataset(data_path, split = "train")
+        #save it to appropriately size chunks
+        year_str = t_fold[:-2]
+        new_data_path = os.path.join(output_path,year_str)
+        try:
+            data_set.save_to_disk(new_data_path, max_shard_size="5GB")
+        except BaseException as e:
+            print(f"temporarty_folder: {t_fold} could not be processed")
+            print("Lowering max shard size to 3GB")
+            #remove folder and try again
+            remove(new_data_path)
+            try:
+                data_set.save_to_disk(new_data_path, max_shard_size="3GB")
+            except BaseException as e:
+                print(f"temporarty_folder: {t_fold} could not be processed")
+                print("Lowering max shard size to 1GB")
+                #remove folder and try again
+                remove(new_data_path)
+                try:
+                    data_set.save_to_disk(new_data_path, max_shard_size="1GB")
+                except BaseException as e:
+                    print(f"temporarty_folder: {t_fold} could not be processed")
+                    #print("Lowering max shard size to 1GB")
+                    #remove folder and try again
+                    remove(new_data_path)
+                    continue
+        #delete temp_folder
+        try :
+            remove(data_path)
+        except PermissionError as e:
+            print(f"{e}")
+        data_set.cleanup_cache_files()
+
+def main():
+    reorganize_data(output_path)
+
+if __name__ == "__main__":
+    main()

scrape/scrape_large.py

@@ -1,3 +1,10 @@
+# /// script
+# requires-python = "==3.12"
+# dependencies = [
+#     "beautifulsoup4==4.13.4",
+#     "datasets>=3.5.0",
+# ]
+# ///
 import os
 import re
 import requests