## [v1.2.6] - 2025-07-21

### Added

- Added two table to get an overview of data by license and domain

### Changed

- Dataset overview table now appears in a drop down menu

README.md

@@ -182,7 +182,7 @@ https://github.com/huggingface/datasets/blob/main/templates/README_guide.md
 <!-- START README TABLE -->
 |              |                                                                                                                                                             |
 | ------------ | ----------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| **Version** | 1.2.5 ([Changelog](/CHANGELOG.md)) |
+| **Version** | 1.2.6 ([Changelog](/CHANGELOG.md)) |
 | **Language** | dan, dansk, Danish                                                                                                                                          |
 | **License**  | Openly Licensed, See the respective dataset                                                                                                                 |
 | **Models**   | For model trained used this data see [danish-foundation-models](https://huggingface.co/danish-foundation-models)                                            |
@@ -200,6 +200,7 @@ https://github.com/huggingface/datasets/blob/main/templates/README_guide.md
     - [Loading the dataset](#loading-the-dataset)
     - [Languages](#languages)
     - [Domains](#domains)
+    - [Licensing](#licensing)
   - [Dataset Structure](#dataset-structure)
     - [Data Instances](#data-instances)
     - [Data Fields](#data-fields)
@@ -274,17 +275,15 @@ Language is denoted using [BCP-47](https://en.wikipedia.org/wiki/IETF_language_t
 
 ### Domains
 
-To give a structured overview of the dataset composition, we include three summary tables:
+This dynaword consist of data from various domains (e.g., legal, books, social media). The following table and figure give an overview of the relative distributions of these domains. To see a full overview of the source check out the [source data section](#source-data)
 
-- The Domain Table groups the datasets by domain (e.g., legal, books, social media) and shows the total token count for each domain.
-- The License Table categorizes the data by license type, providing transparency into the usage rights associated with each source.
-- The Main Table offers a detailed breakdown of each dataset, including a short description, its assigned domain, token count, and license.
+<div style="display: flex; gap: 20px; align-items: flex-start;">
 
-Each source is linked to a metadata card with additional information about origin, preprocessing, and license verification.
+<div style="flex: 1;">
 
 
 <!-- START-DOMAIN TABLE -->
-| Domain       | Source with link                                                                                         | N. Tokens   |
+| Domain       | Sources                                                                                                  | N. Tokens   |
 |:-------------|:---------------------------------------------------------------------------------------------------------|:------------|
 | Legal        | [cellar], [eur-lex-sum-da], [fm-udgivelser], [retsinformationdk], [skat], [retspraksis], [domsdatabasen] | 2.32B       |
 | Books        | [ncc_books], [memo], [adl], [wikibooks], [jvj], [gutenberg], [relig]                                     | 722.00M     |
@@ -337,11 +336,73 @@ Each source is linked to a metadata card with additional information about origi
 [domsdatabasen]: data/domsdatabasen/domsdatabasen.md
 <!-- END-DOMAIN TABLE -->
 
+</div>
+
+<div style="flex: 1;">
 
 <p align="center">
 <img src="./images/domain_distribution.png" width="400" style="margin-right: 10px;" />
 </p>
 
+</div>
+
+</div>
+
+
+### Licensing
+
+The following gives an overview of the licensing in the Dynaword. To get the exact license of the individual datasets check out the [overview table](#source-data).
+These license is applied to the constituent data, i.e., the text. The collection of datasets (metadata, quality control, etc.) is licensed under [CC-0](https://creativecommons.org/publicdomain/zero/1.0/legalcode.en).
+
+<!-- START-LICENSE TABLE -->
+| License                         | Sources                                                                                                                                                                                                                                                           | N. Tokens   |
+|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------|
+| CC-0                            | [danske-taler], [ncc_books], [ncc_newspaper], [miljoeportalen], [opensubtitles], [ep], [ft], [wikisource], [spont], [adl], [hest], [skat], [retspraksis], [wikibooks], [botxt], [naat], [synne], [wiki], [nordjyllandnews], [relig], [nota], [health_hovedstaden] | 1.99B       |
+| CC-BY-SA 4.0                    | [cellar], [eur-lex-sum-da], [fm-udgivelser], [memo], [tv2r], [jvj], [depbank]                                                                                                                                                                                     | 1.37B       |
+| Other (No attribution required) | [retsinformationdk], [domsdatabasen]                                                                                                                                                                                                                              | 904.61M     |
+| Other (Attribution required)    | [ai-aktindsigt], [ncc_maalfrid], [ncc_parliament], [dannet], [gutenberg]                                                                                                                                                                                          | 515.61M     |
+| **Total**                       |                                                                                                                                                                                                                                                                   | 4.78B       |
+
+[ai-aktindsigt]: data/ai-aktindsigt/ai-aktindsigt.md
+[cellar]: data/cellar/cellar.md
+[danske-taler]: data/danske-taler/danske-taler.md
+[ncc_books]: data/ncc_books/ncc_books.md
+[ncc_newspaper]: data/ncc_newspaper/ncc_newspaper.md
+[ncc_maalfrid]: data/ncc_maalfrid/ncc_maalfrid.md
+[ncc_parliament]: data/ncc_parliament/ncc_parliament.md
+[eur-lex-sum-da]: data/eur-lex-sum-da/eur-lex-sum-da.md
+[miljoeportalen]: data/miljoeportalen/miljoeportalen.md
+[fm-udgivelser]: data/fm-udgivelser/fm-udgivelser.md
+[memo]: data/memo/memo.md
+[opensubtitles]: data/opensubtitles/opensubtitles.md
+[retsinformationdk]: data/retsinformationdk/retsinformationdk.md
+[ep]: data/ep/ep.md
+[ft]: data/ft/ft.md
+[wikisource]: data/wikisource/wikisource.md
+[spont]: data/spont/spont.md
+[tv2r]: data/tv2r/tv2r.md
+[adl]: data/adl/adl.md
+[hest]: data/hest/hest.md
+[skat]: data/skat/skat.md
+[dannet]: data/dannet/dannet.md
+[retspraksis]: data/retspraksis/retspraksis.md
+[wikibooks]: data/wikibooks/wikibooks.md
+[jvj]: data/jvj/jvj.md
+[gutenberg]: data/gutenberg/gutenberg.md
+[botxt]: data/botxt/botxt.md
+[depbank]: data/depbank/depbank.md
+[naat]: data/naat/naat.md
+[synne]: data/synne/synne.md
+[wiki]: data/wiki/wiki.md
+[nordjyllandnews]: data/nordjyllandnews/nordjyllandnews.md
+[relig]: data/relig/relig.md
+[nota]: data/nota/nota.md
+[health_hovedstaden]: data/health_hovedstaden/health_hovedstaden.md
+[domsdatabasen]: data/domsdatabasen/domsdatabasen.md
+<!-- END-LICENSE TABLE -->
+
+
+
 ## Dataset Structure
 
 The dataset contains text from different sources which are thoroughly defined in [Source Data](#source-data).
@@ -392,117 +453,8 @@ This data generally contains no annotation besides the metadata attached to each
 
 ### Source Data
 
-To give a structured overview of the dataset composition, we include three summary tables:
-
-- The Domain Table groups the datasets by domain (e.g., legal, books, social media) and shows the total token count for each domain.
-- The License Table categorizes the data by license type, providing transparency into the usage rights associated with each source.
-- The Main Table offers a detailed breakdown of each dataset, including a short description, its assigned domain, token count, and license.
-
-Each source is linked to a metadata card with additional information about origin, preprocessing, and license verification.
-
-**Domain Table**
-<!-- START-DOMAIN TABLE -->
-| Domain       | Source with link                                                                                         | N. Tokens   |
-|:-------------|:---------------------------------------------------------------------------------------------------------|:------------|
-| Legal        | [cellar], [eur-lex-sum-da], [fm-udgivelser], [retsinformationdk], [skat], [retspraksis], [domsdatabasen] | 2.32B       |
-| Books        | [ncc_books], [memo], [adl], [wikibooks], [jvj], [gutenberg], [relig]                                     | 722.00M     |
-| Conversation | [danske-taler], [opensubtitles], [ep], [ft], [spont], [naat]                                             | 497.09M     |
-| Social Media | [hest]                                                                                                   | 389.32M     |
-| Other        | [ncc_parliament], [dannet], [depbank], [synne]                                                           | 340.59M     |
-| Web          | [ai-aktindsigt], [ncc_maalfrid], [miljoeportalen]                                                        | 295.87M     |
-| Encyclopedic | [wikisource], [wiki]                                                                                     | 127.35M     |
-| News         | [ncc_newspaper], [tv2r], [nordjyllandnews]                                                               | 60.63M      |
-| Medical      | [health_hovedstaden]                                                                                     | 27.07M      |
-| Readaloud    | [nota]                                                                                                   | 7.30M       |
-| Dialect      | [botxt]                                                                                                  | 847.97K     |
-| **Total**    |                                                                                                          | 4.78B       |
-
-[ai-aktindsigt]: data/ai-aktindsigt/ai-aktindsigt.md
-[cellar]: data/cellar/cellar.md
-[danske-taler]: data/danske-taler/danske-taler.md
-[ncc_books]: data/ncc_books/ncc_books.md
-[ncc_newspaper]: data/ncc_newspaper/ncc_newspaper.md
-[ncc_maalfrid]: data/ncc_maalfrid/ncc_maalfrid.md
-[ncc_parliament]: data/ncc_parliament/ncc_parliament.md
-[eur-lex-sum-da]: data/eur-lex-sum-da/eur-lex-sum-da.md
-[miljoeportalen]: data/miljoeportalen/miljoeportalen.md
-[fm-udgivelser]: data/fm-udgivelser/fm-udgivelser.md
-[memo]: data/memo/memo.md
-[opensubtitles]: data/opensubtitles/opensubtitles.md
-[retsinformationdk]: data/retsinformationdk/retsinformationdk.md
-[ep]: data/ep/ep.md
-[ft]: data/ft/ft.md
-[wikisource]: data/wikisource/wikisource.md
-[spont]: data/spont/spont.md
-[tv2r]: data/tv2r/tv2r.md
-[adl]: data/adl/adl.md
-[hest]: data/hest/hest.md
-[skat]: data/skat/skat.md
-[dannet]: data/dannet/dannet.md
-[retspraksis]: data/retspraksis/retspraksis.md
-[wikibooks]: data/wikibooks/wikibooks.md
-[jvj]: data/jvj/jvj.md
-[gutenberg]: data/gutenberg/gutenberg.md
-[botxt]: data/botxt/botxt.md
-[depbank]: data/depbank/depbank.md
-[naat]: data/naat/naat.md
-[synne]: data/synne/synne.md
-[wiki]: data/wiki/wiki.md
-[nordjyllandnews]: data/nordjyllandnews/nordjyllandnews.md
-[relig]: data/relig/relig.md
-[nota]: data/nota/nota.md
-[health_hovedstaden]: data/health_hovedstaden/health_hovedstaden.md
-[domsdatabasen]: data/domsdatabasen/domsdatabasen.md
-<!-- END-DOMAIN TABLE -->
-
-**License Table**
-<!-- START-LICENSE TABLE -->
-| License      | Source with link                                                                                                                                                                                                                                                  | N. Tokens   |
-|:-------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------|
-| cc0-1.0      | [danske-taler], [ncc_books], [ncc_newspaper], [miljoeportalen], [opensubtitles], [ep], [ft], [wikisource], [spont], [adl], [hest], [skat], [retspraksis], [wikibooks], [botxt], [naat], [synne], [wiki], [nordjyllandnews], [relig], [nota], [health_hovedstaden] | 1.99B       |
-| cc-by-sa-4.0 | [cellar], [eur-lex-sum-da], [fm-udgivelser], [memo], [tv2r], [jvj], [depbank]                                                                                                                                                                                     | 1.37B       |
-| other        | [ncc_maalfrid], [ncc_parliament], [retsinformationdk], [dannet], [gutenberg], [domsdatabasen]                                                                                                                                                                     | 1.28B       |
-| apache-2.0   | [ai-aktindsigt]                                                                                                                                                                                                                                                   | 139.23M     |
-| **Total**    |                                                                                                                                                                                                                                                                   | 4.78B       |
-
-[ai-aktindsigt]: data/ai-aktindsigt/ai-aktindsigt.md
-[cellar]: data/cellar/cellar.md
-[danske-taler]: data/danske-taler/danske-taler.md
-[ncc_books]: data/ncc_books/ncc_books.md
-[ncc_newspaper]: data/ncc_newspaper/ncc_newspaper.md
-[ncc_maalfrid]: data/ncc_maalfrid/ncc_maalfrid.md
-[ncc_parliament]: data/ncc_parliament/ncc_parliament.md
-[eur-lex-sum-da]: data/eur-lex-sum-da/eur-lex-sum-da.md
-[miljoeportalen]: data/miljoeportalen/miljoeportalen.md
-[fm-udgivelser]: data/fm-udgivelser/fm-udgivelser.md
-[memo]: data/memo/memo.md
-[opensubtitles]: data/opensubtitles/opensubtitles.md
-[retsinformationdk]: data/retsinformationdk/retsinformationdk.md
-[ep]: data/ep/ep.md
-[ft]: data/ft/ft.md
-[wikisource]: data/wikisource/wikisource.md
-[spont]: data/spont/spont.md
-[tv2r]: data/tv2r/tv2r.md
-[adl]: data/adl/adl.md
-[hest]: data/hest/hest.md
-[skat]: data/skat/skat.md
-[dannet]: data/dannet/dannet.md
-[retspraksis]: data/retspraksis/retspraksis.md
-[wikibooks]: data/wikibooks/wikibooks.md
-[jvj]: data/jvj/jvj.md
-[gutenberg]: data/gutenberg/gutenberg.md
-[botxt]: data/botxt/botxt.md
-[depbank]: data/depbank/depbank.md
-[naat]: data/naat/naat.md
-[synne]: data/synne/synne.md
-[wiki]: data/wiki/wiki.md
-[nordjyllandnews]: data/nordjyllandnews/nordjyllandnews.md
-[relig]: data/relig/relig.md
-[nota]: data/nota/nota.md
-[health_hovedstaden]: data/health_hovedstaden/health_hovedstaden.md
-[domsdatabasen]: data/domsdatabasen/domsdatabasen.md
-<!-- END-LICENSE TABLE -->
 
+Below follows a brief overview of the sources in the corpus along with their individual license. To get more information about the individual dataset click the hyperlink in the table.
 
 <details>
 <summary><b>Overview Table (click to unfold)</b></summary>
@@ -617,11 +569,7 @@ In addition to data specific processing we also run a series automated quality c
 
 
 ### Dataset Statistics
-The following plot show the domains distribution of the following within the dynaword:
-
-<p align="center">
-<img src="./images/domain_distribution.png" width="400" style="margin-right: 10px;" />
-</p>
+The following plot pr. dataset histograms displaying document lengths.
 
 <details>
 <summary>Per dataset histograms</summary>

descriptive_stats.json

@@ -2,5 +2,5 @@
   "number_of_samples": 960357,
   "average_document_length": 15301.724414983179,
   "number_of_tokens": 4784823570,
-  "revision": "7754e7a4c0a463ab5aec340a6e836c789f01bced"
+  "revision": "3d87e24d35c186fbb994478238e7ccba03a4d8a2"
 }

images/tokens_over_time.html

@@ -2,6 +2,6 @@
 <head><meta charset="utf-8" /></head>
 <body>
     <div>                        <script type="text/javascript">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>
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pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "dynaword"
-version = "1.2.5"
+version = "1.2.6"
 description = "project code for the danish dynaword project"
 readme = "README.md"
 requires-python = ">=3.12,<3.13" # 3.13 have issues with spacy and pytorch

src/dynaword/datasheet.py

@@ -117,20 +117,6 @@ class DataSheet(BaseModel):
     def to_str(self) -> str:
         return f"---\n{self.frontmatter_as_str.strip()}\n---\n\n{self.body.strip()}\n"
 
-    def get_feature_by_string(
-        self, feature_name: Literal["Domain", "Language", "License"]
-    ) -> str:
-        """Get a specific feature from the frontmatter."""
-        match feature_name:
-            case "Domain":
-                return self.domains[0] if self.domains else "N/A"
-            case "Language":
-                return ", ".join(self.language)
-            case "License":
-                return self.license
-            case _:
-                raise ValueError(f"Unknown feature: {feature_name}")
-
     def get_dataset(self, **kwargs) -> Dataset:
         ds_path = self.path.parent
         ds = load_dataset(ds_path.as_posix(), split="train", **kwargs)

src/dynaword/tables.py

@@ -109,6 +109,42 @@ def create_overview_table(
     return df
 
 
+def _get_normalized_license(ds: DataSheet) -> str:
+    non_standard_license_names = {
+        "Apache 2.0": "Other (Attribution required)",
+        "NLOD 2.0": "Other (Attribution required)",
+        "DanNet 1.0": "Other (Attribution required)",
+        "Gutenberg": "Other (Attribution required)",
+        "Danish Copyright Law": "Other (No attribution required)",
+    }
+    if (
+        ds.license_name not in non_standard_license_names
+        and ds.license_name is not None
+    ):
+        return ds.license_name
+    if ds.license_name is None:
+        raise ValueError(
+            f"Datasheet {ds.pretty_name} has no license name specified in the frontmatter."
+        )
+    return non_standard_license_names[ds.license_name]
+
+
+def _get_feature_by_string(
+    datasheet: DataSheet, feature_name: Literal["Domain", "Language", "License"]
+) -> str:
+    """Get a specific feature from the frontmatter."""
+
+    match feature_name:
+        case "Domain":
+            return datasheet.domains[0] if datasheet.domains else "N/A"
+        case "Language":
+            return ", ".join(datasheet.language)
+        case "License":
+            return _get_normalized_license(datasheet)
+        case _:
+            raise ValueError(f"Unknown feature: {feature_name}")
+
+
 def create_grouped_table(
     group: Literal["Domain", "Language", "License"] = "Domain",
     repo_path: Path = repo_path,
@@ -127,7 +163,7 @@ def create_grouped_table(
 
         sheet = DataSheet.load_from_path(readme_path)
         desc_stats = sheet.get_descritive_stats()
-        feature = sheet.get_feature_by_string(group)
+        feature = _get_feature_by_string(sheet, group)
 
         table["Sources"] += [f"[{dataset_path.name}]"]
         table[group] += [feature]
@@ -163,7 +199,7 @@ def create_grouped_table_str(
 ) -> str:
     table = create_grouped_table(group=group, repo_path=repo_path)
     readme_references = create_dataset_readme_references()
-    package = f"{table.to_markdown(index=False, maxcolwidths=[None, 20, None])}\n\n{readme_references}\n\n"
+    package = f"{table.to_markdown(index=False, maxcolwidths=[None, None, None])}\n\n{readme_references}\n\n"
     return package
 
 

test_results.log

@@ -11,1408 +11,15 @@ src/tests/test_datasheets.py ........................................... [ 35%]
 ........................................................................ [ 57%]
 .................................................................        [ 76%]
 src/tests/test_load.py ..                                                [ 77%]
-src/tests/test_quality/test_duplicates.py .............FF..F.F.......... [ 86%]
+src/tests/test_quality/test_duplicates.py .............................. [ 86%]
 ......s                                                                  [ 88%]
-src/tests/test_quality/test_short_texts.py .............FF....F......... [ 97%]
+src/tests/test_quality/test_short_texts.py ............................. [ 97%]
 .......                                                                  [ 99%]
-src/tests/test_unique_ids.py F                                           [100%]
+src/tests/test_unique_ids.py .                                           [100%]
 
-=================================== FAILURES ===================================
-______________________ test_no_within_data_duplicates[ep] ______________________
-
-self = <datasets.packaged_modules.parquet.parquet.ParquetDanish-dynaword object at 0x118b3e240>
-gen_kwargs = {'files': tracked_list(current=FilesIterable(current=/Users/au561649/Github/danish-dynaword/data/ep/ep.parquet))}
-fpath = '/Users/au561649/.cache/huggingface/datasets/danish-dynaword/ep/0.0.0/5055500453bef830.incomplete/danish-dynaword-train-JJJJJ-SSSSS-of-NNNNN.arrow'
-file_format = 'arrow', max_shard_size = 500000000, job_id = 0
-
-    def _prepare_split_single(
-        self, gen_kwargs: dict, fpath: str, file_format: str, max_shard_size: int, job_id: int
-    ) -> Iterable[Tuple[int, bool, Union[int, tuple]]]:
-        gen_kwargs = {k: tracked_list(v) if isinstance(v, list) else v for k, v in gen_kwargs.items()}
-        generator = self._generate_tables(**gen_kwargs)
-        writer_class = ParquetWriter if file_format == "parquet" else ArrowWriter
-        embed_local_files = file_format == "parquet"
-        shard_lengths = []
-        total_num_examples, total_num_bytes = 0, 0
-    
-        shard_id = 0
-        num_examples_progress_update = 0
-        try:
-            writer = writer_class(
-                features=self.info.features,
-                path=fpath.replace("SSSSS", f"{shard_id:05d}").replace("JJJJJ", f"{job_id:05d}"),
-                writer_batch_size=self._writer_batch_size,
-                storage_options=self._fs.storage_options,
-                embed_local_files=embed_local_files,
-            )
-            try:
-                _time = time.time()
-                for _, table in generator:
-                    if max_shard_size is not None and writer._num_bytes > max_shard_size:
-                        num_examples, num_bytes = writer.finalize()
-                        writer.close()
-                        shard_lengths.append(num_examples)
-                        total_num_examples += num_examples
-                        total_num_bytes += num_bytes
-                        shard_id += 1
-                        writer = writer_class(
-                            features=writer._features,
-                            path=fpath.replace("SSSSS", f"{shard_id:05d}").replace("JJJJJ", f"{job_id:05d}"),
-                            writer_batch_size=self._writer_batch_size,
-                            storage_options=self._fs.storage_options,
-                            embed_local_files=embed_local_files,
-                        )
-                    try:
->                       writer.write_table(table)
-
-.venv/lib/python3.12/site-packages/datasets/builder.py:1870: 
-_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
-.venv/lib/python3.12/site-packages/datasets/arrow_writer.py:627: in write_table
-    self.pa_writer.write_table(pa_table, writer_batch_size)
-pyarrow/ipc.pxi:529: in pyarrow.lib._CRecordBatchWriter.write_table
-    ???
-pyarrow/error.pxi:89: in pyarrow.lib.check_status
-    ???
-_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
-
-self = <fsspec.implementations.local.LocalFileOpener object at 0x114a4bfa0>
-args = (<pyarrow.Buffer address=0x5ddec020000 size=75246719 is_cpu=True is_mutable=True>,)
-kwargs = {}
-
-    def write(self, *args, **kwargs):
->       return self.f.write(*args, **kwargs)
-E       OSError: [Errno 28] No space left on device
-
-.venv/lib/python3.12/site-packages/fsspec/implementations/local.py:426: OSError
-
-The above exception was the direct cause of the following exception:
-
-dataset_name = 'ep'
-
-    @pytest.mark.parametrize("dataset_name", DATASET_NAMES)
-    def test_no_within_data_duplicates(dataset_name: str):
->       ds = load_dataset(str(repo_path.resolve()), dataset_name, split="train")
-
-src/tests/test_quality/test_duplicates.py:12: 
-_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
-.venv/lib/python3.12/site-packages/datasets/load.py:2151: in load_dataset
-    builder_instance.download_and_prepare(
-.venv/lib/python3.12/site-packages/datasets/builder.py:924: in download_and_prepare
-    self._download_and_prepare(
-.venv/lib/python3.12/site-packages/datasets/builder.py:1000: in _download_and_prepare
-    self._prepare_split(split_generator, **prepare_split_kwargs)
-.venv/lib/python3.12/site-packages/datasets/builder.py:1741: in _prepare_split
-    for job_id, done, content in self._prepare_split_single(
-_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
-
-self = <datasets.packaged_modules.parquet.parquet.ParquetDanish-dynaword object at 0x118b3e240>
-gen_kwargs = {'files': tracked_list(current=FilesIterable(current=/Users/au561649/Github/danish-dynaword/data/ep/ep.parquet))}
-fpath = '/Users/au561649/.cache/huggingface/datasets/danish-dynaword/ep/0.0.0/5055500453bef830.incomplete/danish-dynaword-train-JJJJJ-SSSSS-of-NNNNN.arrow'
-file_format = 'arrow', max_shard_size = 500000000, job_id = 0
-
-    def _prepare_split_single(
-        self, gen_kwargs: dict, fpath: str, file_format: str, max_shard_size: int, job_id: int
-    ) -> Iterable[Tuple[int, bool, Union[int, tuple]]]:
-        gen_kwargs = {k: tracked_list(v) if isinstance(v, list) else v for k, v in gen_kwargs.items()}
-        generator = self._generate_tables(**gen_kwargs)
-        writer_class = ParquetWriter if file_format == "parquet" else ArrowWriter
-        embed_local_files = file_format == "parquet"
-        shard_lengths = []
-        total_num_examples, total_num_bytes = 0, 0
-    
-        shard_id = 0
-        num_examples_progress_update = 0
-        try:
-            writer = writer_class(
-                features=self.info.features,
-                path=fpath.replace("SSSSS", f"{shard_id:05d}").replace("JJJJJ", f"{job_id:05d}"),
-                writer_batch_size=self._writer_batch_size,
-                storage_options=self._fs.storage_options,
-                embed_local_files=embed_local_files,
-            )
-            try:
-                _time = time.time()
-                for _, table in generator:
-                    if max_shard_size is not None and writer._num_bytes > max_shard_size:
-                        num_examples, num_bytes = writer.finalize()
-                        writer.close()
-                        shard_lengths.append(num_examples)
-                        total_num_examples += num_examples
-                        total_num_bytes += num_bytes
-                        shard_id += 1
-                        writer = writer_class(
-                            features=writer._features,
-                            path=fpath.replace("SSSSS", f"{shard_id:05d}").replace("JJJJJ", f"{job_id:05d}"),
-                            writer_batch_size=self._writer_batch_size,
-                            storage_options=self._fs.storage_options,
-                            embed_local_files=embed_local_files,
-                        )
-                    try:
-                        writer.write_table(table)
-                    except CastError as cast_error:
-                        raise DatasetGenerationCastError.from_cast_error(
-                            cast_error=cast_error,
-                            builder_name=self.info.builder_name,
-                            gen_kwargs=gen_kwargs,
-                            token=self.token,
-                        )
-                    num_examples_progress_update += len(table)
-                    if time.time() > _time + config.PBAR_REFRESH_TIME_INTERVAL:
-                        _time = time.time()
-                        yield job_id, False, num_examples_progress_update
-                        num_examples_progress_update = 0
-            finally:
-                yield job_id, False, num_examples_progress_update
-                num_shards = shard_id + 1
-                num_examples, num_bytes = writer.finalize()
-                writer.close()
-                shard_lengths.append(num_examples)
-                total_num_examples += num_examples
-                total_num_bytes += num_bytes
-        except Exception as e:
-            # Ignore the writer's error for no examples written to the file if this error was caused by the error in _generate_examples before the first example was yielded
-            if isinstance(e, SchemaInferenceError) and e.__context__ is not None:
-                e = e.__context__
-            if isinstance(e, DatasetGenerationError):
-                raise
->           raise DatasetGenerationError("An error occurred while generating the dataset") from e
-E           datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset
-
-.venv/lib/python3.12/site-packages/datasets/builder.py:1897: DatasetGenerationError
------------------------------ Captured stderr call -----------------------------
-
-______________________ test_no_within_data_duplicates[ft] ______________________
-
-self = <datasets.packaged_modules.parquet.parquet.ParquetDanish-dynaword object at 0x11137ed80>
-gen_kwargs = {'files': tracked_list(current=FilesIterable(current=/Users/au561649/Github/danish-dynaword/data/ft/ft.parquet))}
-fpath = '/Users/au561649/.cache/huggingface/datasets/danish-dynaword/ft/0.0.0/5055500453bef830.incomplete/danish-dynaword-train-JJJJJ-SSSSS-of-NNNNN.arrow'
-file_format = 'arrow', max_shard_size = 500000000, job_id = 0
-
-    def _prepare_split_single(
-        self, gen_kwargs: dict, fpath: str, file_format: str, max_shard_size: int, job_id: int
-    ) -> Iterable[Tuple[int, bool, Union[int, tuple]]]:
-        gen_kwargs = {k: tracked_list(v) if isinstance(v, list) else v for k, v in gen_kwargs.items()}
-        generator = self._generate_tables(**gen_kwargs)
-        writer_class = ParquetWriter if file_format == "parquet" else ArrowWriter
-        embed_local_files = file_format == "parquet"
-        shard_lengths = []
-        total_num_examples, total_num_bytes = 0, 0
-    
-        shard_id = 0
-        num_examples_progress_update = 0
-        try:
-            writer = writer_class(
-                features=self.info.features,
-                path=fpath.replace("SSSSS", f"{shard_id:05d}").replace("JJJJJ", f"{job_id:05d}"),
-                writer_batch_size=self._writer_batch_size,
-                storage_options=self._fs.storage_options,
-                embed_local_files=embed_local_files,
-            )
-            try:
-                _time = time.time()
-                for _, table in generator:
-                    if max_shard_size is not None and writer._num_bytes > max_shard_size:
-                        num_examples, num_bytes = writer.finalize()
-                        writer.close()
-                        shard_lengths.append(num_examples)
-                        total_num_examples += num_examples
-                        total_num_bytes += num_bytes
-                        shard_id += 1
-                        writer = writer_class(
-                            features=writer._features,
-                            path=fpath.replace("SSSSS", f"{shard_id:05d}").replace("JJJJJ", f"{job_id:05d}"),
-                            writer_batch_size=self._writer_batch_size,
-                            storage_options=self._fs.storage_options,
-                            embed_local_files=embed_local_files,
-                        )
-                    try:
->                       writer.write_table(table)
-
-.venv/lib/python3.12/site-packages/datasets/builder.py:1870: 
-_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
-.venv/lib/python3.12/site-packages/datasets/arrow_writer.py:627: in write_table
-    self.pa_writer.write_table(pa_table, writer_batch_size)
-pyarrow/ipc.pxi:529: in pyarrow.lib._CRecordBatchWriter.write_table
-    ???
-pyarrow/error.pxi:89: in pyarrow.lib.check_status
-    ???
-_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
-
-self = <fsspec.implementations.local.LocalFileOpener object at 0x1137dd150>
-args = (<pyarrow.Buffer address=0x5de9c020000 size=274397630 is_cpu=True is_mutable=True>,)
-kwargs = {}
-
-    def write(self, *args, **kwargs):
->       return self.f.write(*args, **kwargs)
-E       OSError: [Errno 28] No space left on device
-
-.venv/lib/python3.12/site-packages/fsspec/implementations/local.py:426: OSError
-
-The above exception was the direct cause of the following exception:
-
-dataset_name = 'ft'
-
-    @pytest.mark.parametrize("dataset_name", DATASET_NAMES)
-    def test_no_within_data_duplicates(dataset_name: str):
->       ds = load_dataset(str(repo_path.resolve()), dataset_name, split="train")
-
-src/tests/test_quality/test_duplicates.py:12: 
-_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
-.venv/lib/python3.12/site-packages/datasets/load.py:2151: in load_dataset
-    builder_instance.download_and_prepare(
-.venv/lib/python3.12/site-packages/datasets/builder.py:924: in download_and_prepare
-    self._download_and_prepare(
-.venv/lib/python3.12/site-packages/datasets/builder.py:1000: in _download_and_prepare
-    self._prepare_split(split_generator, **prepare_split_kwargs)
-.venv/lib/python3.12/site-packages/datasets/builder.py:1741: in _prepare_split
-    for job_id, done, content in self._prepare_split_single(
-_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
-
-self = <datasets.packaged_modules.parquet.parquet.ParquetDanish-dynaword object at 0x11137ed80>
-gen_kwargs = {'files': tracked_list(current=FilesIterable(current=/Users/au561649/Github/danish-dynaword/data/ft/ft.parquet))}
-fpath = '/Users/au561649/.cache/huggingface/datasets/danish-dynaword/ft/0.0.0/5055500453bef830.incomplete/danish-dynaword-train-JJJJJ-SSSSS-of-NNNNN.arrow'
-file_format = 'arrow', max_shard_size = 500000000, job_id = 0
-
-    def _prepare_split_single(
-        self, gen_kwargs: dict, fpath: str, file_format: str, max_shard_size: int, job_id: int
-    ) -> Iterable[Tuple[int, bool, Union[int, tuple]]]:
-        gen_kwargs = {k: tracked_list(v) if isinstance(v, list) else v for k, v in gen_kwargs.items()}
-        generator = self._generate_tables(**gen_kwargs)
-        writer_class = ParquetWriter if file_format == "parquet" else ArrowWriter
-        embed_local_files = file_format == "parquet"
-        shard_lengths = []
-        total_num_examples, total_num_bytes = 0, 0
-    
-        shard_id = 0
-        num_examples_progress_update = 0
-        try:
-            writer = writer_class(
-                features=self.info.features,
-                path=fpath.replace("SSSSS", f"{shard_id:05d}").replace("JJJJJ", f"{job_id:05d}"),
-                writer_batch_size=self._writer_batch_size,
-                storage_options=self._fs.storage_options,
-                embed_local_files=embed_local_files,
-            )
-            try:
-                _time = time.time()
-                for _, table in generator:
-                    if max_shard_size is not None and writer._num_bytes > max_shard_size:
-                        num_examples, num_bytes = writer.finalize()
-                        writer.close()
-                        shard_lengths.append(num_examples)
-                        total_num_examples += num_examples
-                        total_num_bytes += num_bytes
-                        shard_id += 1
-                        writer = writer_class(
-                            features=writer._features,
-                            path=fpath.replace("SSSSS", f"{shard_id:05d}").replace("JJJJJ", f"{job_id:05d}"),
-                            writer_batch_size=self._writer_batch_size,
-                            storage_options=self._fs.storage_options,
-                            embed_local_files=embed_local_files,
-                        )
-                    try:
-                        writer.write_table(table)
-                    except CastError as cast_error:
-                        raise DatasetGenerationCastError.from_cast_error(
-                            cast_error=cast_error,
-                            builder_name=self.info.builder_name,
-                            gen_kwargs=gen_kwargs,
-                            token=self.token,
-                        )
-                    num_examples_progress_update += len(table)
-                    if time.time() > _time + config.PBAR_REFRESH_TIME_INTERVAL:
-                        _time = time.time()
-                        yield job_id, False, num_examples_progress_update
-                        num_examples_progress_update = 0
-            finally:
-                yield job_id, False, num_examples_progress_update
-                num_shards = shard_id + 1
-                num_examples, num_bytes = writer.finalize()
-                writer.close()
-                shard_lengths.append(num_examples)
-                total_num_examples += num_examples
-                total_num_bytes += num_bytes
-        except Exception as e:
-            # Ignore the writer's error for no examples written to the file if this error was caused by the error in _generate_examples before the first example was yielded
-            if isinstance(e, SchemaInferenceError) and e.__context__ is not None:
-                e = e.__context__
-            if isinstance(e, DatasetGenerationError):
-                raise
->           raise DatasetGenerationError("An error occurred while generating the dataset") from e
-E           datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset
-
-.venv/lib/python3.12/site-packages/datasets/builder.py:1897: DatasetGenerationError
------------------------------ Captured stderr call -----------------------------
-
-_____________________ test_no_within_data_duplicates[tv2r] _____________________
-
-self = <datasets.packaged_modules.parquet.parquet.ParquetDanish-dynaword object at 0x114c07bc0>
-gen_kwargs = {'files': tracked_list(current=FilesIterable(current=/Users/au561649/Github/danish-dynaword/data/tv2r/tv2r.parquet))}
-fpath = '/Users/au561649/.cache/huggingface/datasets/danish-dynaword/tv2r/0.0.0/5055500453bef830.incomplete/danish-dynaword-train-JJJJJ-SSSSS-of-NNNNN.arrow'
-file_format = 'arrow', max_shard_size = 500000000, job_id = 0
-
-    def _prepare_split_single(
-        self, gen_kwargs: dict, fpath: str, file_format: str, max_shard_size: int, job_id: int
-    ) -> Iterable[Tuple[int, bool, Union[int, tuple]]]:
-        gen_kwargs = {k: tracked_list(v) if isinstance(v, list) else v for k, v in gen_kwargs.items()}
-        generator = self._generate_tables(**gen_kwargs)
-        writer_class = ParquetWriter if file_format == "parquet" else ArrowWriter
-        embed_local_files = file_format == "parquet"
-        shard_lengths = []
-        total_num_examples, total_num_bytes = 0, 0
-    
-        shard_id = 0
-        num_examples_progress_update = 0
-        try:
-            writer = writer_class(
-                features=self.info.features,
-                path=fpath.replace("SSSSS", f"{shard_id:05d}").replace("JJJJJ", f"{job_id:05d}"),
-                writer_batch_size=self._writer_batch_size,
-                storage_options=self._fs.storage_options,
-                embed_local_files=embed_local_files,
-            )
-            try:
-                _time = time.time()
-                for _, table in generator:
-                    if max_shard_size is not None and writer._num_bytes > max_shard_size:
-                        num_examples, num_bytes = writer.finalize()
-                        writer.close()
-                        shard_lengths.append(num_examples)
-                        total_num_examples += num_examples
-                        total_num_bytes += num_bytes
-                        shard_id += 1
-                        writer = writer_class(
-                            features=writer._features,
-                            path=fpath.replace("SSSSS", f"{shard_id:05d}").replace("JJJJJ", f"{job_id:05d}"),
-                            writer_batch_size=self._writer_batch_size,
-                            storage_options=self._fs.storage_options,
-                            embed_local_files=embed_local_files,
-                        )
-                    try:
->                       writer.write_table(table)
-
-.venv/lib/python3.12/site-packages/datasets/builder.py:1870: 
-_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
-.venv/lib/python3.12/site-packages/datasets/arrow_writer.py:627: in write_table
-    self.pa_writer.write_table(pa_table, writer_batch_size)
-pyarrow/ipc.pxi:529: in pyarrow.lib._CRecordBatchWriter.write_table
-    ???
-pyarrow/error.pxi:89: in pyarrow.lib.check_status
-    ???
-_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
-
-self = <fsspec.implementations.local.LocalFileOpener object at 0x11379d9f0>
-args = (<pyarrow.Buffer address=0x5cf2c0d0000 size=4000 is_cpu=True is_mutable=True>,)
-kwargs = {}
-
-    def write(self, *args, **kwargs):
->       return self.f.write(*args, **kwargs)
-E       OSError: [Errno 28] No space left on device
-
-.venv/lib/python3.12/site-packages/fsspec/implementations/local.py:426: OSError
-
-During handling of the above exception, another exception occurred:
-
-self = <datasets.packaged_modules.parquet.parquet.ParquetDanish-dynaword object at 0x114c07bc0>
-gen_kwargs = {'files': tracked_list(current=FilesIterable(current=/Users/au561649/Github/danish-dynaword/data/tv2r/tv2r.parquet))}
-fpath = '/Users/au561649/.cache/huggingface/datasets/danish-dynaword/tv2r/0.0.0/5055500453bef830.incomplete/danish-dynaword-train-JJJJJ-SSSSS-of-NNNNN.arrow'
-file_format = 'arrow', max_shard_size = 500000000, job_id = 0
-
-    def _prepare_split_single(
-        self, gen_kwargs: dict, fpath: str, file_format: str, max_shard_size: int, job_id: int
-    ) -> Iterable[Tuple[int, bool, Union[int, tuple]]]:
-        gen_kwargs = {k: tracked_list(v) if isinstance(v, list) else v for k, v in gen_kwargs.items()}
-        generator = self._generate_tables(**gen_kwargs)
-        writer_class = ParquetWriter if file_format == "parquet" else ArrowWriter
-        embed_local_files = file_format == "parquet"
-        shard_lengths = []
-        total_num_examples, total_num_bytes = 0, 0
-    
-        shard_id = 0
-        num_examples_progress_update = 0
-        try:
-            writer = writer_class(
-                features=self.info.features,
-                path=fpath.replace("SSSSS", f"{shard_id:05d}").replace("JJJJJ", f"{job_id:05d}"),
-                writer_batch_size=self._writer_batch_size,
-                storage_options=self._fs.storage_options,
-                embed_local_files=embed_local_files,
-            )
-            try:
-                _time = time.time()
-                for _, table in generator:
-                    if max_shard_size is not None and writer._num_bytes > max_shard_size:
-                        num_examples, num_bytes = writer.finalize()
-                        writer.close()
-                        shard_lengths.append(num_examples)
-                        total_num_examples += num_examples
-                        total_num_bytes += num_bytes
-                        shard_id += 1
-                        writer = writer_class(
-                            features=writer._features,
-                            path=fpath.replace("SSSSS", f"{shard_id:05d}").replace("JJJJJ", f"{job_id:05d}"),
-                            writer_batch_size=self._writer_batch_size,
-                            storage_options=self._fs.storage_options,
-                            embed_local_files=embed_local_files,
-                        )
-                    try:
-                        writer.write_table(table)
-                    except CastError as cast_error:
-                        raise DatasetGenerationCastError.from_cast_error(
-                            cast_error=cast_error,
-                            builder_name=self.info.builder_name,
-                            gen_kwargs=gen_kwargs,
-                            token=self.token,
-                        )
-                    num_examples_progress_update += len(table)
-                    if time.time() > _time + config.PBAR_REFRESH_TIME_INTERVAL:
-                        _time = time.time()
-                        yield job_id, False, num_examples_progress_update
-                        num_examples_progress_update = 0
-            finally:
-                yield job_id, False, num_examples_progress_update
-                num_shards = shard_id + 1
->               num_examples, num_bytes = writer.finalize()
-
-.venv/lib/python3.12/site-packages/datasets/builder.py:1886: 
-_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
-.venv/lib/python3.12/site-packages/datasets/arrow_writer.py:644: in finalize
-    self.stream.close()
-_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
-
-self = <fsspec.implementations.local.LocalFileOpener object at 0x11379d9f0>
-
-    def close(self):
->       return self.f.close()
-E       OSError: [Errno 28] No space left on device
-
-.venv/lib/python3.12/site-packages/fsspec/implementations/local.py:444: OSError
-
-The above exception was the direct cause of the following exception:
-
-dataset_name = 'tv2r'
-
-    @pytest.mark.parametrize("dataset_name", DATASET_NAMES)
-    def test_no_within_data_duplicates(dataset_name: str):
->       ds = load_dataset(str(repo_path.resolve()), dataset_name, split="train")
-
-src/tests/test_quality/test_duplicates.py:12: 
-_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
-.venv/lib/python3.12/site-packages/datasets/load.py:2151: in load_dataset
-    builder_instance.download_and_prepare(
-.venv/lib/python3.12/site-packages/datasets/builder.py:924: in download_and_prepare
-    self._download_and_prepare(
-.venv/lib/python3.12/site-packages/datasets/builder.py:1000: in _download_and_prepare
-    self._prepare_split(split_generator, **prepare_split_kwargs)
-.venv/lib/python3.12/site-packages/datasets/builder.py:1741: in _prepare_split
-    for job_id, done, content in self._prepare_split_single(
-_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
-
-self = <datasets.packaged_modules.parquet.parquet.ParquetDanish-dynaword object at 0x114c07bc0>
-gen_kwargs = {'files': tracked_list(current=FilesIterable(current=/Users/au561649/Github/danish-dynaword/data/tv2r/tv2r.parquet))}
-fpath = '/Users/au561649/.cache/huggingface/datasets/danish-dynaword/tv2r/0.0.0/5055500453bef830.incomplete/danish-dynaword-train-JJJJJ-SSSSS-of-NNNNN.arrow'
-file_format = 'arrow', max_shard_size = 500000000, job_id = 0
-
-    def _prepare_split_single(
-        self, gen_kwargs: dict, fpath: str, file_format: str, max_shard_size: int, job_id: int
-    ) -> Iterable[Tuple[int, bool, Union[int, tuple]]]:
-        gen_kwargs = {k: tracked_list(v) if isinstance(v, list) else v for k, v in gen_kwargs.items()}
-        generator = self._generate_tables(**gen_kwargs)
-        writer_class = ParquetWriter if file_format == "parquet" else ArrowWriter
-        embed_local_files = file_format == "parquet"
-        shard_lengths = []
-        total_num_examples, total_num_bytes = 0, 0
-    
-        shard_id = 0
-        num_examples_progress_update = 0
-        try:
-            writer = writer_class(
-                features=self.info.features,
-                path=fpath.replace("SSSSS", f"{shard_id:05d}").replace("JJJJJ", f"{job_id:05d}"),
-                writer_batch_size=self._writer_batch_size,
-                storage_options=self._fs.storage_options,
-                embed_local_files=embed_local_files,
-            )
-            try:
-                _time = time.time()
-                for _, table in generator:
-                    if max_shard_size is not None and writer._num_bytes > max_shard_size:
-                        num_examples, num_bytes = writer.finalize()
-                        writer.close()
-                        shard_lengths.append(num_examples)
-                        total_num_examples += num_examples
-                        total_num_bytes += num_bytes
-                        shard_id += 1
-                        writer = writer_class(
-                            features=writer._features,
-                            path=fpath.replace("SSSSS", f"{shard_id:05d}").replace("JJJJJ", f"{job_id:05d}"),
-                            writer_batch_size=self._writer_batch_size,
-                            storage_options=self._fs.storage_options,
-                            embed_local_files=embed_local_files,
-                        )
-                    try:
-                        writer.write_table(table)
-                    except CastError as cast_error:
-                        raise DatasetGenerationCastError.from_cast_error(
-                            cast_error=cast_error,
-                            builder_name=self.info.builder_name,
-                            gen_kwargs=gen_kwargs,
-                            token=self.token,
-                        )
-                    num_examples_progress_update += len(table)
-                    if time.time() > _time + config.PBAR_REFRESH_TIME_INTERVAL:
-                        _time = time.time()
-                        yield job_id, False, num_examples_progress_update
-                        num_examples_progress_update = 0
-            finally:
-                yield job_id, False, num_examples_progress_update
-                num_shards = shard_id + 1
-                num_examples, num_bytes = writer.finalize()
-                writer.close()
-                shard_lengths.append(num_examples)
-                total_num_examples += num_examples
-                total_num_bytes += num_bytes
-        except Exception as e:
-            # Ignore the writer's error for no examples written to the file if this error was caused by the error in _generate_examples before the first example was yielded
-            if isinstance(e, SchemaInferenceError) and e.__context__ is not None:
-                e = e.__context__
-            if isinstance(e, DatasetGenerationError):
-                raise
->           raise DatasetGenerationError("An error occurred while generating the dataset") from e
-E           datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset
-
-.venv/lib/python3.12/site-packages/datasets/builder.py:1897: DatasetGenerationError
------------------------------ Captured stderr call -----------------------------
-
-_____________________ test_no_within_data_duplicates[hest] _____________________
-
-self = <datasets.packaged_modules.parquet.parquet.ParquetDanish-dynaword object at 0x1137b2360>
-gen_kwargs = {'files': tracked_list(current=FilesIterable(current=/Users/au561649/Github/danish-dynaword/data/hest/hest.parquet))}
-fpath = '/Users/au561649/.cache/huggingface/datasets/danish-dynaword/hest/0.0.0/5055500453bef830.incomplete/danish-dynaword-train-JJJJJ-SSSSS-of-NNNNN.arrow'
-file_format = 'arrow', max_shard_size = 500000000, job_id = 0
-
-    def _prepare_split_single(
-        self, gen_kwargs: dict, fpath: str, file_format: str, max_shard_size: int, job_id: int
-    ) -> Iterable[Tuple[int, bool, Union[int, tuple]]]:
-        gen_kwargs = {k: tracked_list(v) if isinstance(v, list) else v for k, v in gen_kwargs.items()}
-        generator = self._generate_tables(**gen_kwargs)
-        writer_class = ParquetWriter if file_format == "parquet" else ArrowWriter
-        embed_local_files = file_format == "parquet"
-        shard_lengths = []
-        total_num_examples, total_num_bytes = 0, 0
-    
-        shard_id = 0
-        num_examples_progress_update = 0
-        try:
-            writer = writer_class(
-                features=self.info.features,
-                path=fpath.replace("SSSSS", f"{shard_id:05d}").replace("JJJJJ", f"{job_id:05d}"),
-                writer_batch_size=self._writer_batch_size,
-                storage_options=self._fs.storage_options,
-                embed_local_files=embed_local_files,
-            )
-            try:
-                _time = time.time()
-                for _, table in generator:
-                    if max_shard_size is not None and writer._num_bytes > max_shard_size:
-                        num_examples, num_bytes = writer.finalize()
-                        writer.close()
-                        shard_lengths.append(num_examples)
-                        total_num_examples += num_examples
-                        total_num_bytes += num_bytes
-                        shard_id += 1
-                        writer = writer_class(
-                            features=writer._features,
-                            path=fpath.replace("SSSSS", f"{shard_id:05d}").replace("JJJJJ", f"{job_id:05d}"),
-                            writer_batch_size=self._writer_batch_size,
-                            storage_options=self._fs.storage_options,
-                            embed_local_files=embed_local_files,
-                        )
-                    try:
->                       writer.write_table(table)
-
-.venv/lib/python3.12/site-packages/datasets/builder.py:1870: 
-_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
-.venv/lib/python3.12/site-packages/datasets/arrow_writer.py:627: in write_table
-    self.pa_writer.write_table(pa_table, writer_batch_size)
-pyarrow/ipc.pxi:529: in pyarrow.lib._CRecordBatchWriter.write_table
-    ???
-pyarrow/error.pxi:89: in pyarrow.lib.check_status
-    ???
-_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
-
-self = <fsspec.implementations.local.LocalFileOpener object at 0x114af1390>
-args = (<pyarrow.Buffer address=0x5e004020000 size=147880457 is_cpu=True is_mutable=True>,)
-kwargs = {}
-
-    def write(self, *args, **kwargs):
->       return self.f.write(*args, **kwargs)
-E       OSError: [Errno 28] No space left on device
-
-.venv/lib/python3.12/site-packages/fsspec/implementations/local.py:426: OSError
-
-The above exception was the direct cause of the following exception:
-
-dataset_name = 'hest'
-
-    @pytest.mark.parametrize("dataset_name", DATASET_NAMES)
-    def test_no_within_data_duplicates(dataset_name: str):
->       ds = load_dataset(str(repo_path.resolve()), dataset_name, split="train")
-
-src/tests/test_quality/test_duplicates.py:12: 
-_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
-.venv/lib/python3.12/site-packages/datasets/load.py:2151: in load_dataset
-    builder_instance.download_and_prepare(
-.venv/lib/python3.12/site-packages/datasets/builder.py:924: in download_and_prepare
-    self._download_and_prepare(
-.venv/lib/python3.12/site-packages/datasets/builder.py:1000: in _download_and_prepare
-    self._prepare_split(split_generator, **prepare_split_kwargs)
-.venv/lib/python3.12/site-packages/datasets/builder.py:1741: in _prepare_split
-    for job_id, done, content in self._prepare_split_single(
-_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
-
-self = <datasets.packaged_modules.parquet.parquet.ParquetDanish-dynaword object at 0x1137b2360>
-gen_kwargs = {'files': tracked_list(current=FilesIterable(current=/Users/au561649/Github/danish-dynaword/data/hest/hest.parquet))}
-fpath = '/Users/au561649/.cache/huggingface/datasets/danish-dynaword/hest/0.0.0/5055500453bef830.incomplete/danish-dynaword-train-JJJJJ-SSSSS-of-NNNNN.arrow'
-file_format = 'arrow', max_shard_size = 500000000, job_id = 0
-
-    def _prepare_split_single(
-        self, gen_kwargs: dict, fpath: str, file_format: str, max_shard_size: int, job_id: int
-    ) -> Iterable[Tuple[int, bool, Union[int, tuple]]]:
-        gen_kwargs = {k: tracked_list(v) if isinstance(v, list) else v for k, v in gen_kwargs.items()}
-        generator = self._generate_tables(**gen_kwargs)
-        writer_class = ParquetWriter if file_format == "parquet" else ArrowWriter
-        embed_local_files = file_format == "parquet"
-        shard_lengths = []
-        total_num_examples, total_num_bytes = 0, 0
-    
-        shard_id = 0
-        num_examples_progress_update = 0
-        try:
-            writer = writer_class(
-                features=self.info.features,
-                path=fpath.replace("SSSSS", f"{shard_id:05d}").replace("JJJJJ", f"{job_id:05d}"),
-                writer_batch_size=self._writer_batch_size,
-                storage_options=self._fs.storage_options,
-                embed_local_files=embed_local_files,
-            )
-            try:
-                _time = time.time()
-                for _, table in generator:
-                    if max_shard_size is not None and writer._num_bytes > max_shard_size:
-                        num_examples, num_bytes = writer.finalize()
-                        writer.close()
-                        shard_lengths.append(num_examples)
-                        total_num_examples += num_examples
-                        total_num_bytes += num_bytes
-                        shard_id += 1
-                        writer = writer_class(
-                            features=writer._features,
-                            path=fpath.replace("SSSSS", f"{shard_id:05d}").replace("JJJJJ", f"{job_id:05d}"),
-                            writer_batch_size=self._writer_batch_size,
-                            storage_options=self._fs.storage_options,
-                            embed_local_files=embed_local_files,
-                        )
-                    try:
-                        writer.write_table(table)
-                    except CastError as cast_error:
-                        raise DatasetGenerationCastError.from_cast_error(
-                            cast_error=cast_error,
-                            builder_name=self.info.builder_name,
-                            gen_kwargs=gen_kwargs,
-                            token=self.token,
-                        )
-                    num_examples_progress_update += len(table)
-                    if time.time() > _time + config.PBAR_REFRESH_TIME_INTERVAL:
-                        _time = time.time()
-                        yield job_id, False, num_examples_progress_update
-                        num_examples_progress_update = 0
-            finally:
-                yield job_id, False, num_examples_progress_update
-                num_shards = shard_id + 1
-                num_examples, num_bytes = writer.finalize()
-                writer.close()
-                shard_lengths.append(num_examples)
-                total_num_examples += num_examples
-                total_num_bytes += num_bytes
-        except Exception as e:
-            # Ignore the writer's error for no examples written to the file if this error was caused by the error in _generate_examples before the first example was yielded
-            if isinstance(e, SchemaInferenceError) and e.__context__ is not None:
-                e = e.__context__
-            if isinstance(e, DatasetGenerationError):
-                raise
->           raise DatasetGenerationError("An error occurred while generating the dataset") from e
-E           datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset
-
-.venv/lib/python3.12/site-packages/datasets/builder.py:1897: DatasetGenerationError
------------------------------ Captured stderr call -----------------------------
-
-________________________ test_no_one_word_documents[ep] ________________________
-
-self = <datasets.packaged_modules.parquet.parquet.ParquetDanish-dynaword object at 0x114c1bb90>
-gen_kwargs = {'files': tracked_list(current=FilesIterable(current=/Users/au561649/Github/danish-dynaword/data/ep/ep.parquet))}
-fpath = '/Users/au561649/.cache/huggingface/datasets/danish-dynaword/ep/0.0.0/5055500453bef830.incomplete/danish-dynaword-train-JJJJJ-SSSSS-of-NNNNN.arrow'
-file_format = 'arrow', max_shard_size = 500000000, job_id = 0
-
-    def _prepare_split_single(
-        self, gen_kwargs: dict, fpath: str, file_format: str, max_shard_size: int, job_id: int
-    ) -> Iterable[Tuple[int, bool, Union[int, tuple]]]:
-        gen_kwargs = {k: tracked_list(v) if isinstance(v, list) else v for k, v in gen_kwargs.items()}
-        generator = self._generate_tables(**gen_kwargs)
-        writer_class = ParquetWriter if file_format == "parquet" else ArrowWriter
-        embed_local_files = file_format == "parquet"
-        shard_lengths = []
-        total_num_examples, total_num_bytes = 0, 0
-    
-        shard_id = 0
-        num_examples_progress_update = 0
-        try:
-            writer = writer_class(
-                features=self.info.features,
-                path=fpath.replace("SSSSS", f"{shard_id:05d}").replace("JJJJJ", f"{job_id:05d}"),
-                writer_batch_size=self._writer_batch_size,
-                storage_options=self._fs.storage_options,
-                embed_local_files=embed_local_files,
-            )
-            try:
-                _time = time.time()
-                for _, table in generator:
-                    if max_shard_size is not None and writer._num_bytes > max_shard_size:
-                        num_examples, num_bytes = writer.finalize()
-                        writer.close()
-                        shard_lengths.append(num_examples)
-                        total_num_examples += num_examples
-                        total_num_bytes += num_bytes
-                        shard_id += 1
-                        writer = writer_class(
-                            features=writer._features,
-                            path=fpath.replace("SSSSS", f"{shard_id:05d}").replace("JJJJJ", f"{job_id:05d}"),
-                            writer_batch_size=self._writer_batch_size,
-                            storage_options=self._fs.storage_options,
-                            embed_local_files=embed_local_files,
-                        )
-                    try:
->                       writer.write_table(table)
-
-.venv/lib/python3.12/site-packages/datasets/builder.py:1870: 
-_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
-.venv/lib/python3.12/site-packages/datasets/arrow_writer.py:627: in write_table
-    self.pa_writer.write_table(pa_table, writer_batch_size)
-pyarrow/ipc.pxi:529: in pyarrow.lib._CRecordBatchWriter.write_table
-    ???
-pyarrow/error.pxi:89: in pyarrow.lib.check_status
-    ???
-_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
-
-self = <fsspec.implementations.local.LocalFileOpener object at 0x113e86290>
-args = (<pyarrow.Buffer address=0x5e1f0020000 size=76944794 is_cpu=True is_mutable=True>,)
-kwargs = {}
-
-    def write(self, *args, **kwargs):
->       return self.f.write(*args, **kwargs)
-E       OSError: [Errno 28] No space left on device
-
-.venv/lib/python3.12/site-packages/fsspec/implementations/local.py:426: OSError
-
-The above exception was the direct cause of the following exception:
-
-dataset_name = 'ep'
-
-    @pytest.mark.parametrize("dataset_name", DATASET_NAMES)
-    # @pytest.mark.skip("This tests currently fails")
-    def test_no_one_word_documents(dataset_name: str):
->       ds = load_dataset(str(repo_path.resolve()), dataset_name, split="train")
-
-src/tests/test_quality/test_short_texts.py:14: 
-_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
-.venv/lib/python3.12/site-packages/datasets/load.py:2151: in load_dataset
-    builder_instance.download_and_prepare(
-.venv/lib/python3.12/site-packages/datasets/builder.py:924: in download_and_prepare
-    self._download_and_prepare(
-.venv/lib/python3.12/site-packages/datasets/builder.py:1000: in _download_and_prepare
-    self._prepare_split(split_generator, **prepare_split_kwargs)
-.venv/lib/python3.12/site-packages/datasets/builder.py:1741: in _prepare_split
-    for job_id, done, content in self._prepare_split_single(
-_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
-
-self = <datasets.packaged_modules.parquet.parquet.ParquetDanish-dynaword object at 0x114c1bb90>
-gen_kwargs = {'files': tracked_list(current=FilesIterable(current=/Users/au561649/Github/danish-dynaword/data/ep/ep.parquet))}
-fpath = '/Users/au561649/.cache/huggingface/datasets/danish-dynaword/ep/0.0.0/5055500453bef830.incomplete/danish-dynaword-train-JJJJJ-SSSSS-of-NNNNN.arrow'
-file_format = 'arrow', max_shard_size = 500000000, job_id = 0
-
-    def _prepare_split_single(
-        self, gen_kwargs: dict, fpath: str, file_format: str, max_shard_size: int, job_id: int
-    ) -> Iterable[Tuple[int, bool, Union[int, tuple]]]:
-        gen_kwargs = {k: tracked_list(v) if isinstance(v, list) else v for k, v in gen_kwargs.items()}
-        generator = self._generate_tables(**gen_kwargs)
-        writer_class = ParquetWriter if file_format == "parquet" else ArrowWriter
-        embed_local_files = file_format == "parquet"
-        shard_lengths = []
-        total_num_examples, total_num_bytes = 0, 0
-    
-        shard_id = 0
-        num_examples_progress_update = 0
-        try:
-            writer = writer_class(
-                features=self.info.features,
-                path=fpath.replace("SSSSS", f"{shard_id:05d}").replace("JJJJJ", f"{job_id:05d}"),
-                writer_batch_size=self._writer_batch_size,
-                storage_options=self._fs.storage_options,
-                embed_local_files=embed_local_files,
-            )
-            try:
-                _time = time.time()
-                for _, table in generator:
-                    if max_shard_size is not None and writer._num_bytes > max_shard_size:
-                        num_examples, num_bytes = writer.finalize()
-                        writer.close()
-                        shard_lengths.append(num_examples)
-                        total_num_examples += num_examples
-                        total_num_bytes += num_bytes
-                        shard_id += 1
-                        writer = writer_class(
-                            features=writer._features,
-                            path=fpath.replace("SSSSS", f"{shard_id:05d}").replace("JJJJJ", f"{job_id:05d}"),
-                            writer_batch_size=self._writer_batch_size,
-                            storage_options=self._fs.storage_options,
-                            embed_local_files=embed_local_files,
-                        )
-                    try:
-                        writer.write_table(table)
-                    except CastError as cast_error:
-                        raise DatasetGenerationCastError.from_cast_error(
-                            cast_error=cast_error,
-                            builder_name=self.info.builder_name,
-                            gen_kwargs=gen_kwargs,
-                            token=self.token,
-                        )
-                    num_examples_progress_update += len(table)
-                    if time.time() > _time + config.PBAR_REFRESH_TIME_INTERVAL:
-                        _time = time.time()
-                        yield job_id, False, num_examples_progress_update
-                        num_examples_progress_update = 0
-            finally:
-                yield job_id, False, num_examples_progress_update
-                num_shards = shard_id + 1
-                num_examples, num_bytes = writer.finalize()
-                writer.close()
-                shard_lengths.append(num_examples)
-                total_num_examples += num_examples
-                total_num_bytes += num_bytes
-        except Exception as e:
-            # Ignore the writer's error for no examples written to the file if this error was caused by the error in _generate_examples before the first example was yielded
-            if isinstance(e, SchemaInferenceError) and e.__context__ is not None:
-                e = e.__context__
-            if isinstance(e, DatasetGenerationError):
-                raise
->           raise DatasetGenerationError("An error occurred while generating the dataset") from e
-E           datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset
-
-.venv/lib/python3.12/site-packages/datasets/builder.py:1897: DatasetGenerationError
------------------------------ Captured stderr call -----------------------------
-
-________________________ test_no_one_word_documents[ft] ________________________
-
-self = <datasets.packaged_modules.parquet.parquet.ParquetDanish-dynaword object at 0x12e558620>
-gen_kwargs = {'files': tracked_list(current=FilesIterable(current=/Users/au561649/Github/danish-dynaword/data/ft/ft.parquet))}
-fpath = '/Users/au561649/.cache/huggingface/datasets/danish-dynaword/ft/0.0.0/5055500453bef830.incomplete/danish-dynaword-train-JJJJJ-SSSSS-of-NNNNN.arrow'
-file_format = 'arrow', max_shard_size = 500000000, job_id = 0
-
-    def _prepare_split_single(
-        self, gen_kwargs: dict, fpath: str, file_format: str, max_shard_size: int, job_id: int
-    ) -> Iterable[Tuple[int, bool, Union[int, tuple]]]:
-        gen_kwargs = {k: tracked_list(v) if isinstance(v, list) else v for k, v in gen_kwargs.items()}
-        generator = self._generate_tables(**gen_kwargs)
-        writer_class = ParquetWriter if file_format == "parquet" else ArrowWriter
-        embed_local_files = file_format == "parquet"
-        shard_lengths = []
-        total_num_examples, total_num_bytes = 0, 0
-    
-        shard_id = 0
-        num_examples_progress_update = 0
-        try:
-            writer = writer_class(
-                features=self.info.features,
-                path=fpath.replace("SSSSS", f"{shard_id:05d}").replace("JJJJJ", f"{job_id:05d}"),
-                writer_batch_size=self._writer_batch_size,
-                storage_options=self._fs.storage_options,
-                embed_local_files=embed_local_files,
-            )
-            try:
-                _time = time.time()
-                for _, table in generator:
-                    if max_shard_size is not None and writer._num_bytes > max_shard_size:
-                        num_examples, num_bytes = writer.finalize()
-                        writer.close()
-                        shard_lengths.append(num_examples)
-                        total_num_examples += num_examples
-                        total_num_bytes += num_bytes
-                        shard_id += 1
-                        writer = writer_class(
-                            features=writer._features,
-                            path=fpath.replace("SSSSS", f"{shard_id:05d}").replace("JJJJJ", f"{job_id:05d}"),
-                            writer_batch_size=self._writer_batch_size,
-                            storage_options=self._fs.storage_options,
-                            embed_local_files=embed_local_files,
-                        )
-                    try:
->                       writer.write_table(table)
-
-.venv/lib/python3.12/site-packages/datasets/builder.py:1870: 
-_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
-.venv/lib/python3.12/site-packages/datasets/arrow_writer.py:627: in write_table
-    self.pa_writer.write_table(pa_table, writer_batch_size)
-pyarrow/ipc.pxi:529: in pyarrow.lib._CRecordBatchWriter.write_table
-    ???
-pyarrow/error.pxi:89: in pyarrow.lib.check_status
-    ???
-_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
-
-self = <fsspec.implementations.local.LocalFileOpener object at 0x113eb1d50>
-args = (<pyarrow.Buffer address=0x5e238020000 size=274397630 is_cpu=True is_mutable=True>,)
-kwargs = {}
-
-    def write(self, *args, **kwargs):
->       return self.f.write(*args, **kwargs)
-E       OSError: [Errno 28] No space left on device
-
-.venv/lib/python3.12/site-packages/fsspec/implementations/local.py:426: OSError
-
-The above exception was the direct cause of the following exception:
-
-dataset_name = 'ft'
-
-    @pytest.mark.parametrize("dataset_name", DATASET_NAMES)
-    # @pytest.mark.skip("This tests currently fails")
-    def test_no_one_word_documents(dataset_name: str):
->       ds = load_dataset(str(repo_path.resolve()), dataset_name, split="train")
-
-src/tests/test_quality/test_short_texts.py:14: 
-_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
-.venv/lib/python3.12/site-packages/datasets/load.py:2151: in load_dataset
-    builder_instance.download_and_prepare(
-.venv/lib/python3.12/site-packages/datasets/builder.py:924: in download_and_prepare
-    self._download_and_prepare(
-.venv/lib/python3.12/site-packages/datasets/builder.py:1000: in _download_and_prepare
-    self._prepare_split(split_generator, **prepare_split_kwargs)
-.venv/lib/python3.12/site-packages/datasets/builder.py:1741: in _prepare_split
-    for job_id, done, content in self._prepare_split_single(
-_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
-
-self = <datasets.packaged_modules.parquet.parquet.ParquetDanish-dynaword object at 0x12e558620>
-gen_kwargs = {'files': tracked_list(current=FilesIterable(current=/Users/au561649/Github/danish-dynaword/data/ft/ft.parquet))}
-fpath = '/Users/au561649/.cache/huggingface/datasets/danish-dynaword/ft/0.0.0/5055500453bef830.incomplete/danish-dynaword-train-JJJJJ-SSSSS-of-NNNNN.arrow'
-file_format = 'arrow', max_shard_size = 500000000, job_id = 0
-
-    def _prepare_split_single(
-        self, gen_kwargs: dict, fpath: str, file_format: str, max_shard_size: int, job_id: int
-    ) -> Iterable[Tuple[int, bool, Union[int, tuple]]]:
-        gen_kwargs = {k: tracked_list(v) if isinstance(v, list) else v for k, v in gen_kwargs.items()}
-        generator = self._generate_tables(**gen_kwargs)
-        writer_class = ParquetWriter if file_format == "parquet" else ArrowWriter
-        embed_local_files = file_format == "parquet"
-        shard_lengths = []
-        total_num_examples, total_num_bytes = 0, 0
-    
-        shard_id = 0
-        num_examples_progress_update = 0
-        try:
-            writer = writer_class(
-                features=self.info.features,
-                path=fpath.replace("SSSSS", f"{shard_id:05d}").replace("JJJJJ", f"{job_id:05d}"),
-                writer_batch_size=self._writer_batch_size,
-                storage_options=self._fs.storage_options,
-                embed_local_files=embed_local_files,
-            )
-            try:
-                _time = time.time()
-                for _, table in generator:
-                    if max_shard_size is not None and writer._num_bytes > max_shard_size:
-                        num_examples, num_bytes = writer.finalize()
-                        writer.close()
-                        shard_lengths.append(num_examples)
-                        total_num_examples += num_examples
-                        total_num_bytes += num_bytes
-                        shard_id += 1
-                        writer = writer_class(
-                            features=writer._features,
-                            path=fpath.replace("SSSSS", f"{shard_id:05d}").replace("JJJJJ", f"{job_id:05d}"),
-                            writer_batch_size=self._writer_batch_size,
-                            storage_options=self._fs.storage_options,
-                            embed_local_files=embed_local_files,
-                        )
-                    try:
-                        writer.write_table(table)
-                    except CastError as cast_error:
-                        raise DatasetGenerationCastError.from_cast_error(
-                            cast_error=cast_error,
-                            builder_name=self.info.builder_name,
-                            gen_kwargs=gen_kwargs,
-                            token=self.token,
-                        )
-                    num_examples_progress_update += len(table)
-                    if time.time() > _time + config.PBAR_REFRESH_TIME_INTERVAL:
-                        _time = time.time()
-                        yield job_id, False, num_examples_progress_update
-                        num_examples_progress_update = 0
-            finally:
-                yield job_id, False, num_examples_progress_update
-                num_shards = shard_id + 1
-                num_examples, num_bytes = writer.finalize()
-                writer.close()
-                shard_lengths.append(num_examples)
-                total_num_examples += num_examples
-                total_num_bytes += num_bytes
-        except Exception as e:
-            # Ignore the writer's error for no examples written to the file if this error was caused by the error in _generate_examples before the first example was yielded
-            if isinstance(e, SchemaInferenceError) and e.__context__ is not None:
-                e = e.__context__
-            if isinstance(e, DatasetGenerationError):
-                raise
->           raise DatasetGenerationError("An error occurred while generating the dataset") from e
-E           datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset
-
-.venv/lib/python3.12/site-packages/datasets/builder.py:1897: DatasetGenerationError
------------------------------ Captured stderr call -----------------------------
-
-_______________________ test_no_one_word_documents[hest] _______________________
-
-self = <datasets.packaged_modules.parquet.parquet.ParquetDanish-dynaword object at 0x118b3f1a0>
-gen_kwargs = {'files': tracked_list(current=FilesIterable(current=/Users/au561649/Github/danish-dynaword/data/hest/hest.parquet))}
-fpath = '/Users/au561649/.cache/huggingface/datasets/danish-dynaword/hest/0.0.0/5055500453bef830.incomplete/danish-dynaword-train-JJJJJ-SSSSS-of-NNNNN.arrow'
-file_format = 'arrow', max_shard_size = 500000000, job_id = 0
-
-    def _prepare_split_single(
-        self, gen_kwargs: dict, fpath: str, file_format: str, max_shard_size: int, job_id: int
-    ) -> Iterable[Tuple[int, bool, Union[int, tuple]]]:
-        gen_kwargs = {k: tracked_list(v) if isinstance(v, list) else v for k, v in gen_kwargs.items()}
-        generator = self._generate_tables(**gen_kwargs)
-        writer_class = ParquetWriter if file_format == "parquet" else ArrowWriter
-        embed_local_files = file_format == "parquet"
-        shard_lengths = []
-        total_num_examples, total_num_bytes = 0, 0
-    
-        shard_id = 0
-        num_examples_progress_update = 0
-        try:
-            writer = writer_class(
-                features=self.info.features,
-                path=fpath.replace("SSSSS", f"{shard_id:05d}").replace("JJJJJ", f"{job_id:05d}"),
-                writer_batch_size=self._writer_batch_size,
-                storage_options=self._fs.storage_options,
-                embed_local_files=embed_local_files,
-            )
-            try:
-                _time = time.time()
-                for _, table in generator:
-                    if max_shard_size is not None and writer._num_bytes > max_shard_size:
-                        num_examples, num_bytes = writer.finalize()
-                        writer.close()
-                        shard_lengths.append(num_examples)
-                        total_num_examples += num_examples
-                        total_num_bytes += num_bytes
-                        shard_id += 1
-                        writer = writer_class(
-                            features=writer._features,
-                            path=fpath.replace("SSSSS", f"{shard_id:05d}").replace("JJJJJ", f"{job_id:05d}"),
-                            writer_batch_size=self._writer_batch_size,
-                            storage_options=self._fs.storage_options,
-                            embed_local_files=embed_local_files,
-                        )
-                    try:
->                       writer.write_table(table)
-
-.venv/lib/python3.12/site-packages/datasets/builder.py:1870: 
-_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
-.venv/lib/python3.12/site-packages/datasets/arrow_writer.py:627: in write_table
-    self.pa_writer.write_table(pa_table, writer_batch_size)
-pyarrow/ipc.pxi:529: in pyarrow.lib._CRecordBatchWriter.write_table
-    ???
-pyarrow/error.pxi:89: in pyarrow.lib.check_status
-    ???
-_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
-
-self = <fsspec.implementations.local.LocalFileOpener object at 0x113e85810>
-args = (<pyarrow.Buffer address=0x5e3c8020000 size=95688808 is_cpu=True is_mutable=True>,)
-kwargs = {}
-
-    def write(self, *args, **kwargs):
->       return self.f.write(*args, **kwargs)
-E       OSError: [Errno 28] No space left on device
-
-.venv/lib/python3.12/site-packages/fsspec/implementations/local.py:426: OSError
-
-The above exception was the direct cause of the following exception:
-
-dataset_name = 'hest'
-
-    @pytest.mark.parametrize("dataset_name", DATASET_NAMES)
-    # @pytest.mark.skip("This tests currently fails")
-    def test_no_one_word_documents(dataset_name: str):
->       ds = load_dataset(str(repo_path.resolve()), dataset_name, split="train")
-
-src/tests/test_quality/test_short_texts.py:14: 
-_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
-.venv/lib/python3.12/site-packages/datasets/load.py:2151: in load_dataset
-    builder_instance.download_and_prepare(
-.venv/lib/python3.12/site-packages/datasets/builder.py:924: in download_and_prepare
-    self._download_and_prepare(
-.venv/lib/python3.12/site-packages/datasets/builder.py:1000: in _download_and_prepare
-    self._prepare_split(split_generator, **prepare_split_kwargs)
-.venv/lib/python3.12/site-packages/datasets/builder.py:1741: in _prepare_split
-    for job_id, done, content in self._prepare_split_single(
-_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
-
-self = <datasets.packaged_modules.parquet.parquet.ParquetDanish-dynaword object at 0x118b3f1a0>
-gen_kwargs = {'files': tracked_list(current=FilesIterable(current=/Users/au561649/Github/danish-dynaword/data/hest/hest.parquet))}
-fpath = '/Users/au561649/.cache/huggingface/datasets/danish-dynaword/hest/0.0.0/5055500453bef830.incomplete/danish-dynaword-train-JJJJJ-SSSSS-of-NNNNN.arrow'
-file_format = 'arrow', max_shard_size = 500000000, job_id = 0
-
-    def _prepare_split_single(
-        self, gen_kwargs: dict, fpath: str, file_format: str, max_shard_size: int, job_id: int
-    ) -> Iterable[Tuple[int, bool, Union[int, tuple]]]:
-        gen_kwargs = {k: tracked_list(v) if isinstance(v, list) else v for k, v in gen_kwargs.items()}
-        generator = self._generate_tables(**gen_kwargs)
-        writer_class = ParquetWriter if file_format == "parquet" else ArrowWriter
-        embed_local_files = file_format == "parquet"
-        shard_lengths = []
-        total_num_examples, total_num_bytes = 0, 0
-    
-        shard_id = 0
-        num_examples_progress_update = 0
-        try:
-            writer = writer_class(
-                features=self.info.features,
-                path=fpath.replace("SSSSS", f"{shard_id:05d}").replace("JJJJJ", f"{job_id:05d}"),
-                writer_batch_size=self._writer_batch_size,
-                storage_options=self._fs.storage_options,
-                embed_local_files=embed_local_files,
-            )
-            try:
-                _time = time.time()
-                for _, table in generator:
-                    if max_shard_size is not None and writer._num_bytes > max_shard_size:
-                        num_examples, num_bytes = writer.finalize()
-                        writer.close()
-                        shard_lengths.append(num_examples)
-                        total_num_examples += num_examples
-                        total_num_bytes += num_bytes
-                        shard_id += 1
-                        writer = writer_class(
-                            features=writer._features,
-                            path=fpath.replace("SSSSS", f"{shard_id:05d}").replace("JJJJJ", f"{job_id:05d}"),
-                            writer_batch_size=self._writer_batch_size,
-                            storage_options=self._fs.storage_options,
-                            embed_local_files=embed_local_files,
-                        )
-                    try:
-                        writer.write_table(table)
-                    except CastError as cast_error:
-                        raise DatasetGenerationCastError.from_cast_error(
-                            cast_error=cast_error,
-                            builder_name=self.info.builder_name,
-                            gen_kwargs=gen_kwargs,
-                            token=self.token,
-                        )
-                    num_examples_progress_update += len(table)
-                    if time.time() > _time + config.PBAR_REFRESH_TIME_INTERVAL:
-                        _time = time.time()
-                        yield job_id, False, num_examples_progress_update
-                        num_examples_progress_update = 0
-            finally:
-                yield job_id, False, num_examples_progress_update
-                num_shards = shard_id + 1
-                num_examples, num_bytes = writer.finalize()
-                writer.close()
-                shard_lengths.append(num_examples)
-                total_num_examples += num_examples
-                total_num_bytes += num_bytes
-        except Exception as e:
-            # Ignore the writer's error for no examples written to the file if this error was caused by the error in _generate_examples before the first example was yielded
-            if isinstance(e, SchemaInferenceError) and e.__context__ is not None:
-                e = e.__context__
-            if isinstance(e, DatasetGenerationError):
-                raise
->           raise DatasetGenerationError("An error occurred while generating the dataset") from e
-E           datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset
-
-.venv/lib/python3.12/site-packages/datasets/builder.py:1897: DatasetGenerationError
------------------------------ Captured stderr call -----------------------------
-
-__________________________ test_ensure_ids_are_unique __________________________
-
-self = <datasets.packaged_modules.parquet.parquet.ParquetDanish-dynaword object at 0x113ec1970>
-gen_kwargs = {'files': tracked_list(current=FilesIterable(current=/Users/au561649/Github/danish-dynaword/data/cellar/cellar.parquet))}
-fpath = '/Users/au561649/.cache/huggingface/datasets/danish-dynaword/default/0.0.0/5055500453bef830.incomplete/danish-dynaword-train-JJJJJ-SSSSS-of-NNNNN.arrow'
-file_format = 'arrow', max_shard_size = 500000000, job_id = 0
-
-    def _prepare_split_single(
-        self, gen_kwargs: dict, fpath: str, file_format: str, max_shard_size: int, job_id: int
-    ) -> Iterable[Tuple[int, bool, Union[int, tuple]]]:
-        gen_kwargs = {k: tracked_list(v) if isinstance(v, list) else v for k, v in gen_kwargs.items()}
-        generator = self._generate_tables(**gen_kwargs)
-        writer_class = ParquetWriter if file_format == "parquet" else ArrowWriter
-        embed_local_files = file_format == "parquet"
-        shard_lengths = []
-        total_num_examples, total_num_bytes = 0, 0
-    
-        shard_id = 0
-        num_examples_progress_update = 0
-        try:
-            writer = writer_class(
-                features=self.info.features,
-                path=fpath.replace("SSSSS", f"{shard_id:05d}").replace("JJJJJ", f"{job_id:05d}"),
-                writer_batch_size=self._writer_batch_size,
-                storage_options=self._fs.storage_options,
-                embed_local_files=embed_local_files,
-            )
-            try:
-                _time = time.time()
-                for _, table in generator:
-                    if max_shard_size is not None and writer._num_bytes > max_shard_size:
-                        num_examples, num_bytes = writer.finalize()
-                        writer.close()
-                        shard_lengths.append(num_examples)
-                        total_num_examples += num_examples
-                        total_num_bytes += num_bytes
-                        shard_id += 1
-                        writer = writer_class(
-                            features=writer._features,
-                            path=fpath.replace("SSSSS", f"{shard_id:05d}").replace("JJJJJ", f"{job_id:05d}"),
-                            writer_batch_size=self._writer_batch_size,
-                            storage_options=self._fs.storage_options,
-                            embed_local_files=embed_local_files,
-                        )
-                    try:
->                       writer.write_table(table)
-
-.venv/lib/python3.12/site-packages/datasets/builder.py:1870: 
-_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
-.venv/lib/python3.12/site-packages/datasets/arrow_writer.py:627: in write_table
-    self.pa_writer.write_table(pa_table, writer_batch_size)
-pyarrow/ipc.pxi:529: in pyarrow.lib._CRecordBatchWriter.write_table
-    ???
-pyarrow/error.pxi:89: in pyarrow.lib.check_status
-    ???
-_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
-
-self = <fsspec.implementations.local.LocalFileOpener object at 0x113aaffd0>
-args = (<pyarrow.Buffer address=0x5e500020000 size=81139164 is_cpu=True is_mutable=True>,)
-kwargs = {}
-
-    def write(self, *args, **kwargs):
->       return self.f.write(*args, **kwargs)
-E       OSError: [Errno 28] No space left on device
-
-.venv/lib/python3.12/site-packages/fsspec/implementations/local.py:426: OSError
-
-The above exception was the direct cause of the following exception:
-
-    def test_ensure_ids_are_unique():
-        name = str(repo_path.resolve())
->       ds = load_dataset(name, split="train")
-
-src/tests/test_unique_ids.py:11: 
-_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
-.venv/lib/python3.12/site-packages/datasets/load.py:2151: in load_dataset
-    builder_instance.download_and_prepare(
-.venv/lib/python3.12/site-packages/datasets/builder.py:924: in download_and_prepare
-    self._download_and_prepare(
-.venv/lib/python3.12/site-packages/datasets/builder.py:1000: in _download_and_prepare
-    self._prepare_split(split_generator, **prepare_split_kwargs)
-.venv/lib/python3.12/site-packages/datasets/builder.py:1741: in _prepare_split
-    for job_id, done, content in self._prepare_split_single(
-_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
-
-self = <datasets.packaged_modules.parquet.parquet.ParquetDanish-dynaword object at 0x113ec1970>
-gen_kwargs = {'files': tracked_list(current=FilesIterable(current=/Users/au561649/Github/danish-dynaword/data/cellar/cellar.parquet))}
-fpath = '/Users/au561649/.cache/huggingface/datasets/danish-dynaword/default/0.0.0/5055500453bef830.incomplete/danish-dynaword-train-JJJJJ-SSSSS-of-NNNNN.arrow'
-file_format = 'arrow', max_shard_size = 500000000, job_id = 0
-
-    def _prepare_split_single(
-        self, gen_kwargs: dict, fpath: str, file_format: str, max_shard_size: int, job_id: int
-    ) -> Iterable[Tuple[int, bool, Union[int, tuple]]]:
-        gen_kwargs = {k: tracked_list(v) if isinstance(v, list) else v for k, v in gen_kwargs.items()}
-        generator = self._generate_tables(**gen_kwargs)
-        writer_class = ParquetWriter if file_format == "parquet" else ArrowWriter
-        embed_local_files = file_format == "parquet"
-        shard_lengths = []
-        total_num_examples, total_num_bytes = 0, 0
-    
-        shard_id = 0
-        num_examples_progress_update = 0
-        try:
-            writer = writer_class(
-                features=self.info.features,
-                path=fpath.replace("SSSSS", f"{shard_id:05d}").replace("JJJJJ", f"{job_id:05d}"),
-                writer_batch_size=self._writer_batch_size,
-                storage_options=self._fs.storage_options,
-                embed_local_files=embed_local_files,
-            )
-            try:
-                _time = time.time()
-                for _, table in generator:
-                    if max_shard_size is not None and writer._num_bytes > max_shard_size:
-                        num_examples, num_bytes = writer.finalize()
-                        writer.close()
-                        shard_lengths.append(num_examples)
-                        total_num_examples += num_examples
-                        total_num_bytes += num_bytes
-                        shard_id += 1
-                        writer = writer_class(
-                            features=writer._features,
-                            path=fpath.replace("SSSSS", f"{shard_id:05d}").replace("JJJJJ", f"{job_id:05d}"),
-                            writer_batch_size=self._writer_batch_size,
-                            storage_options=self._fs.storage_options,
-                            embed_local_files=embed_local_files,
-                        )
-                    try:
-                        writer.write_table(table)
-                    except CastError as cast_error:
-                        raise DatasetGenerationCastError.from_cast_error(
-                            cast_error=cast_error,
-                            builder_name=self.info.builder_name,
-                            gen_kwargs=gen_kwargs,
-                            token=self.token,
-                        )
-                    num_examples_progress_update += len(table)
-                    if time.time() > _time + config.PBAR_REFRESH_TIME_INTERVAL:
-                        _time = time.time()
-                        yield job_id, False, num_examples_progress_update
-                        num_examples_progress_update = 0
-            finally:
-                yield job_id, False, num_examples_progress_update
-                num_shards = shard_id + 1
-                num_examples, num_bytes = writer.finalize()
-                writer.close()
-                shard_lengths.append(num_examples)
-                total_num_examples += num_examples
-                total_num_bytes += num_bytes
-        except Exception as e:
-            # Ignore the writer's error for no examples written to the file if this error was caused by the error in _generate_examples before the first example was yielded
-            if isinstance(e, SchemaInferenceError) and e.__context__ is not None:
-                e = e.__context__
-            if isinstance(e, DatasetGenerationError):
-                raise
->           raise DatasetGenerationError("An error occurred while generating the dataset") from e
-E           datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset
-
-.venv/lib/python3.12/site-packages/datasets/builder.py:1897: DatasetGenerationError
------------------------------ Captured stderr call -----------------------------
-
-
 =============================== warnings summary ===============================
-src/tests/test_quality/test_short_texts.py: 33 warnings
+src/tests/test_quality/test_short_texts.py: 36 warnings
   /Users/au561649/Github/danish-dynaword/.venv/lib/python3.12/site-packages/datasets/utils/_dill.py:385: DeprecationWarning: co_lnotab is deprecated, use co_lines instead.
 
 -- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
-=========================== short test summary info ============================
-FAILED src/tests/test_quality/test_duplicates.py::test_no_within_data_duplicates[ep]
-FAILED src/tests/test_quality/test_duplicates.py::test_no_within_data_duplicates[ft]
-FAILED src/tests/test_quality/test_duplicates.py::test_no_within_data_duplicates[tv2r]
-FAILED src/tests/test_quality/test_duplicates.py::test_no_within_data_duplicates[hest]
-FAILED src/tests/test_quality/test_short_texts.py::test_no_one_word_documents[ep]
-FAILED src/tests/test_quality/test_short_texts.py::test_no_one_word_documents[ft]
-FAILED src/tests/test_quality/test_short_texts.py::test_no_one_word_documents[hest]
-FAILED src/tests/test_unique_ids.py::test_ensure_ids_are_unique - datasets.ex...
-====== 8 failed, 319 passed, 1 skipped, 33 warnings in 365.20s (0:06:05) =======
+================= 327 passed, 1 skipped, 36 warnings in 53.74s =================