format and ensure that tests pass

data/ncc_books/create.py

@@ -218,11 +218,11 @@ def filter_with_changelog(
     return dataset
 
 
-source_filter = lambda ds: doc_type_searchword in ds["doc_type"]
-length_filter = lambda ds: len(word_tokenize(ds["text"])) >= 10
-too_long_filter = lambda ds: len(word_tokenize(ds["text"])) > 1e5
-alpha_filter = lambda ds: alpha_ratio(ds["text"]) >= 0.7
-stop_word_filter = lambda ds: count_min_target(word_tokenize(ds["text"]), spacy_sw, 2)
+source_filter = lambda ds: doc_type_searchword in ds["doc_type"]  # noqa
+length_filter = lambda ds: len(word_tokenize(ds["text"])) >= 10  # noqa
+too_long_filter = lambda ds: len(word_tokenize(ds["text"])) > 1e5  # noqa
+alpha_filter = lambda ds: alpha_ratio(ds["text"]) >= 0.7  # noqa
+stop_word_filter = lambda ds: count_min_target(word_tokenize(ds["text"]), spacy_sw, 2)  # noqa
 
 samples_pr_source: dict = defaultdict(lambda: defaultdict(int))
 

data/ncc_books/ncc_books.md

@@ -3,7 +3,7 @@ pretty_name: Norwegian Colossal Corpus (books)
 language:
 - da
 license: cc0-1.0
-license_name: CC0 1.0
+license_name: CC-0
 task_categories:
 - text-generation
 - fill-mask

data/ncc_maalfrid/create.py

@@ -5,14 +5,13 @@
 # ]
 # ///
 
+import inspect
 import logging
 import re
-import inspect
-
-from pathlib import Path
-from datetime import datetime
 from collections import defaultdict
 from collections.abc import Callable
+from datetime import datetime
+from pathlib import Path
 
 import pandas as pd
 from datasets import Dataset, load_dataset
@@ -219,11 +218,11 @@ def filter_with_changelog(
     return dataset
 
 
-source_filter = lambda ds: doc_type_searchword in ds["doc_type"]
-length_filter = lambda ds: len(word_tokenize(ds["text"])) >= 10
-too_long_filter = lambda ds: len(word_tokenize(ds["text"])) > 1e5
-alpha_filter = lambda ds: alpha_ratio(ds["text"]) >= 0.7
-stop_word_filter = lambda ds: count_min_target(word_tokenize(ds["text"]), spacy_sw, 2)
+source_filter = lambda ds: doc_type_searchword in ds["doc_type"]  # noqa
+length_filter = lambda ds: len(word_tokenize(ds["text"])) >= 10  # noqa
+too_long_filter = lambda ds: len(word_tokenize(ds["text"])) > 1e5  # noqa
+alpha_filter = lambda ds: alpha_ratio(ds["text"]) >= 0.7  # noqa
+stop_word_filter = lambda ds: count_min_target(word_tokenize(ds["text"]), spacy_sw, 2)  # noqa
 
 samples_pr_source: dict = defaultdict(lambda: defaultdict(int))
 

data/ncc_newspaper/create.py

@@ -220,11 +220,11 @@ def filter_with_changelog(
 
 
 # filters
-source_filter = lambda ds: re.sub("ncc_", "", source) in ds["doc_type"]
-length_filter = lambda ds: len(word_tokenize(ds["text"])) >= 10
-too_long_filter = lambda ds: len(word_tokenize(ds["text"])) > 1e5
-alpha_filter = lambda ds: alpha_ratio(ds["text"]) >= 0.7
-stop_word_filter = lambda ds: count_min_target(word_tokenize(ds["text"]), spacy_sw, 2)
+source_filter = lambda ds: re.sub("ncc_", "", source) in ds["doc_type"]  # noqa
+length_filter = lambda ds: len(word_tokenize(ds["text"])) >= 10  # noqa
+too_long_filter = lambda ds: len(word_tokenize(ds["text"])) > 1e5  # noqa
+alpha_filter = lambda ds: alpha_ratio(ds["text"]) >= 0.7  # noqa
+stop_word_filter = lambda ds: count_min_target(word_tokenize(ds["text"]), spacy_sw, 2)  # noqa
 
 samples_pr_source: dict = defaultdict(lambda: defaultdict(int))
 

data/ncc_newspaper/ncc_newspaper.md

@@ -3,12 +3,14 @@ pretty_name: Norwegian Colossal Corpus (newspaper)
 language:
 - da
 license: cc0-1.0
-license_name: CC0 1.0
+license_name: CC-0
 task_categories:
 - text-generation
 - fill-mask
 task_ids:
 - language-modeling
+domains:
+- News
 ---
 
 # Dataset Card for Norwegian Colossal Corpus (newspaper)

data/ncc_parliament/create.py

@@ -5,14 +5,13 @@
 # ]
 # ///
 
+import inspect
 import logging
 import re
-import inspect
-
-from pathlib import Path
-from datetime import datetime
 from collections import defaultdict
 from collections.abc import Callable
+from datetime import datetime
+from pathlib import Path
 
 import pandas as pd
 from datasets import Dataset, load_dataset
@@ -219,11 +218,11 @@ def filter_with_changelog(
     return dataset
 
 
-source_filter = lambda ds: doc_type_searchword in ds["doc_type"]
-length_filter = lambda ds: len(word_tokenize(ds["text"])) >= 10
-too_long_filter = lambda ds: len(word_tokenize(ds["text"])) > 1e5
-alpha_filter = lambda ds: alpha_ratio(ds["text"]) >= 0.7
-stop_word_filter = lambda ds: count_min_target(word_tokenize(ds["text"]), spacy_sw, 2)
+source_filter = lambda ds: doc_type_searchword in ds["doc_type"]  # noqa
+length_filter = lambda ds: len(word_tokenize(ds["text"])) >= 10  # noqa
+too_long_filter = lambda ds: len(word_tokenize(ds["text"])) > 1e5  # noqa
+alpha_filter = lambda ds: alpha_ratio(ds["text"]) >= 0.7  # noqa
+stop_word_filter = lambda ds: count_min_target(word_tokenize(ds["text"]), spacy_sw, 2)  # noqa
 
 samples_pr_source: dict = defaultdict(lambda: defaultdict(int))
 

src/tests/test_dataset_schema.py

@@ -2,7 +2,6 @@ import pytest
 from datasets import load_dataset
 
 from dynaword.dataset_structure import SampleSchema
-from dynaword.datasheet import DEFAULT_SECTION_TAGS, DataSheet
 from dynaword.paths import repo_path
 
 from .conftest import DATASET_NAMES
@@ -19,7 +18,6 @@ def test_sample_schema(dataset_name: str):
     SampleSchema(**sample)
 
 
-
 @pytest.mark.parametrize("dataset_name", DATASET_NAMES)
 def test_dataset_folder_structure(dataset_name: str):
     """tests that the dataset folder structure is as follows.

src/tests/test_datasheets.py

@@ -11,7 +11,9 @@ def test_datasheet_load(dataset_name: str):
     """tests that the dataset frontmatter and markdown follows the correct format."""
 
     readme = repo_path / "data" / dataset_name / f"{dataset_name}.md"
-    ds_sheet = DataSheet.load_from_path(readme)  # will fail if format is not correct
+    ds_sheet = DataSheet.load_from_path(  # noqa: F841
+        readme
+    )  # will fail if format is not correct
 
 
 @pytest.mark.parametrize("dataset_name", DATASET_NAMES)
@@ -20,7 +22,6 @@ def test_datasheet_content_tags(dataset_name: str):
     ds_sheet = DataSheet.load_from_path(readme)
 
     # ensure tags:
-    body = ds_sheet.body
     tags = [v.value for v in DEFAULT_SECTION_TAGS]
     for tag in tags:
         ds_sheet.get_tag_idx(tag)

src/tests/test_duplicates.py

@@ -15,7 +15,9 @@ def test_no_within_data_duplicates(dataset_name: str):
     assert len(set(ds["text"])) == len(ds)
 
 
-@pytest.mark.skip("This tests takes too long to run") # there seems to be some duplicate across
+@pytest.mark.skip(
+    "This tests takes too long to run"
+)  # there seems to be some duplicate across
 def test_no_data_duplicates():
     ds = load_dataset(str(repo_path.resolve()), split="train")
     ds = cast(Dataset, ds)

test_results.log

@@ -1,13 +1,16 @@
 ============================= test session starts ==============================
-platform linux -- Python 3.12.3, pytest-8.3.4, pluggy-1.5.0
-rootdir: /work/githubs/tmp/danish-dynaword
+platform darwin -- Python 3.12.0, pytest-8.3.4, pluggy-1.5.0
+rootdir: /Users/au561649/Github/danish-dynaword
 configfile: pyproject.toml
-collected 124 items
+collected 276 items
 
-src/tests/test_dataset_schema.py ....................................... [ 31%]
-...................................................                      [ 72%]
-src/tests/test_duplicates.py sssssssssssssssssssssssssssssss             [ 97%]
+src/tests/test_dataset_schema.py ....................................... [ 14%]
+.............................                                            [ 24%]
+src/tests/test_datasheets.py ........................................... [ 40%]
+........................................................................ [ 66%]
+.......................................................                  [ 86%]
+src/tests/test_duplicates.py ..................................s         [ 98%]
 src/tests/test_load.py ..                                                [ 99%]
 src/tests/test_unique_ids.py .                                           [100%]
 
-======================= 93 passed, 31 skipped in 20.62s ========================
+======================= 275 passed, 1 skipped in 54.24s ========================