test

CHANGELOG.md

@@ -5,6 +5,16 @@ All notable changes to this project will be documented in this file.
 
 The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
+## [v1.2.6] - 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
+
 ## [v1.2.5] - 2025-07-08
 
 ### Added

README.md

@@ -198,7 +198,8 @@ https://github.com/huggingface/datasets/blob/main/templates/README_guide.md
   - [Dataset Description](#dataset-description)
     - [Dataset Summary](#dataset-summary)
     - [Loading the dataset](#loading-the-dataset)
-    - [Languages:](#languages)
+    - [Languages](#languages)
+    - [Domains](#domains)
   - [Dataset Structure](#dataset-structure)
     - [Data Instances](#data-instances)
     - [Data Fields](#data-fields)
@@ -261,7 +262,7 @@ You can also load a single subset at a time:
 ds = load_dataset(name, revision="{desired revision}")
 ```
 
-### Languages:
+### Languages
 This dataset includes the following languages:
 
 - dan-Latn
@@ -270,6 +271,77 @@ This dataset includes the following languages:
 
 Language is denoted using [BCP-47](https://en.wikipedia.org/wiki/IETF_language_tag), using the langauge code ISO 639-3 and the script code ISO 15924. The last element denote the region variant.
 
+
+### Domains
+
+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.
+
+
+<!-- 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 -->
+
+
+<p align="center">
+<img src="./images/domain_distribution.png" width="400" style="margin-right: 10px;" />
+</p>
+
 ## Dataset Structure
 
 The dataset contains text from different sources which are thoroughly defined in [Source Data](#source-data).
@@ -433,7 +505,7 @@ Each source is linked to a metadata card with additional information about origi
 
 
 <details>
-<summary><b>Main table</b></summary>
+<summary><b>Overview Table (click to unfold)</b></summary>
 
 You can learn more about each dataset by pressing the link in the first column.
 

src/dynaword/tables.py

@@ -54,7 +54,7 @@ def create_overview_table(
 ) -> pd.DataFrame:
     table = {
         "Source": [],
-        "Source with link": [],
+        "Sources": [],
         "Description": [],
         "Domain": [],
         "N. Tokens": [],
@@ -70,7 +70,7 @@ def create_overview_table(
         main_domain = sheet.domains[0] if sheet.domains else ""
 
         table["Source"] += [f"{dataset_path.name}"]
-        table["Source with link"] += [f"[{dataset_path.name}]"]
+        table["Sources"] += [f"[{dataset_path.name}]"]
         table["License"] += [f"[{sheet.license_name}]"]
         table["Domain"] += [main_domain]
         table["Description"] += [sheet.short_description]
@@ -82,7 +82,7 @@ def create_overview_table(
     if add_total_row:
         total_row = {
             "Source": "**Total**",
-            "Source with link": "**Total**",
+            "Sources": "**Total**",
             "Domain": "",
             "License": "",
             "Description": "",
@@ -96,12 +96,12 @@ def create_overview_table(
             ignore_index=True,
         )
     if add_readme_references:
-        # replace Source with Source with link
-        df["Source"] = df["Source with link"]
-        df = df.drop(columns=["Source with link"])
+        # replace Source with Sources
+        df["Source"] = df["Sources"]
+        df = df.drop(columns=["Sources"])
     else:
-        # remove Source with link
-        df = df.drop(columns=["Source with link"])
+        # remove Sources
+        df = df.drop(columns=["Sources"])
 
     if add_readable_tokens:
         df["N. Tokens"] = df["N. Tokens"].apply(human_readable_large_int)
@@ -116,7 +116,7 @@ def create_grouped_table(
     add_total_row: bool = True,
 ) -> pd.DataFrame:
     table = {
-        "Source with link": [],
+        "Sources": [],
         group: [],
         "N. Tokens": [],
     }
@@ -129,20 +129,18 @@ def create_grouped_table(
         desc_stats = sheet.get_descritive_stats()
         feature = sheet.get_feature_by_string(group)
 
-        table["Source with link"] += [f"[{dataset_path.name}]"]
+        table["Sources"] += [f"[{dataset_path.name}]"]
         table[group] += [feature]
         table["N. Tokens"] += [desc_stats.number_of_tokens]
 
     if add_total_row:
-        table["Source with link"] += [""]
+        table["Sources"] += [""]
         table[group] += ["**Total**"]
         table["N. Tokens"] += [sum(table["N. Tokens"])]
 
     df = pd.DataFrame.from_dict(table)
 
-    df = df.groupby(group).agg(
-        {"Source with link": lambda x: ", ".join(x), "N. Tokens": "sum"}
-    )
+    df = df.groupby(group).agg({"Sources": lambda x: ", ".join(x), "N. Tokens": "sum"})
 
     df = df.sort_values("N. Tokens", ascending=False)
 
@@ -165,7 +163,7 @@ 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)}\n\n{readme_references}\n\n"
+    package = f"{table.to_markdown(index=False, maxcolwidths=[None, 20, None])}\n\n{readme_references}\n\n"
     return package
 
 

test_results.log

@@ -1,6 +1,6 @@
 ============================= test session starts ==============================
 platform darwin -- Python 3.12.0, pytest-8.3.4, pluggy-1.5.0
-rootdir: /Users/kristianjensen/Documents/danish-dynaword
+rootdir: /Users/au561649/Github/danish-dynaword
 configfile: pyproject.toml
 plugins: anyio-4.9.0
 collected 328 items
@@ -11,15 +11,1408 @@ src/tests/test_datasheets.py ........................................... [ 35%]
 ........................................................................ [ 57%]
 .................................................................        [ 76%]
 src/tests/test_load.py ..                                                [ 77%]
-src/tests/test_quality/test_duplicates.py .............................. [ 86%]
+src/tests/test_quality/test_duplicates.py .............FF..F.F.......... [ 86%]
 ......s                                                                  [ 88%]
-src/tests/test_quality/test_short_texts.py ............................. [ 97%]
+src/tests/test_quality/test_short_texts.py .............FF....F......... [ 97%]
 .......                                                                  [ 99%]
-src/tests/test_unique_ids.py .                                           [100%]
+src/tests/test_unique_ids.py F                                           [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: 36 warnings
-  /Users/kristianjensen/Documents/danish-dynaword/.venv/lib/python3.12/site-packages/datasets/utils/_dill.py:385: DeprecationWarning: co_lnotab is deprecated, use co_lines instead.
+src/tests/test_quality/test_short_texts.py: 33 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
-================= 327 passed, 1 skipped, 36 warnings in 52.74s =================
+=========================== 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) =======