Remove loading script; card no longer says loading is broken

README.md

@@ -136,7 +136,19 @@ def majority_vote(example):
 
 ## Loading
 
-This repo still ships a legacy loading script, so `load_dataset("biglam/contentious_contexts")` raises `RuntimeError: Dataset scripts are no longer supported` on `datasets` v4 and later. Until it is converted, read the CSVs directly from the source repo — `Extracts.csv` and `Annotations.csv` join on `extract_id`, and `Metadata.csv` adds newspaper title, date and place for 2,719 of the 2,720 extracts.
+```python
+from datasets import load_dataset
+
+ds = load_dataset("biglam/contentious_contexts", split="train")
+row = ds[0]
+row["text"]                          # the newspaper extract
+row["target"]                        # the flagged compound, bolded in `text`
+row["annotator_responses_dutch"]     # [{'id': ..., 'response': 'Niet omstreden'}, ...]
+row["annotator_responses_english"]   # the same responses, translated
+```
+
+`Metadata.csv` in the [source repo](https://github.com/cultural-ai/ConConCor) adds newspaper
+title, date and place for 2,719 of the 2,720 extracts; it is not carried here.
 
 ## Licence
 

contentious_contexts.py

@@ -1,133 +0,0 @@
-# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""This dataset contains extracts from historical Dutch newspapers which have been containing keywords of potentially contentious words (according to present-day sensibilities). 
-The dataset contains multiple annotations per instance, given the option to quantify agreement scores for annotations."""
-
-import pandas as pd
-import datasets
-
-
-_CITATION = """@misc{ContentiousContextsCorpus2021,
-  author = {Cultural AI},
-  title = {Contentious Contexts Corpus},
-  year = {2021},
-  publisher = {GitHub},
-  journal = {GitHub repository},
-  howpublished = {\\url{https://github.com/cultural-ai/ConConCor}},
-}
-"""
-
-_DESCRIPTION = """This dataset contains extracts from historical Dutch newspapers which have been containing keywords of potentially contentious words (according to present-day sensibilities). 
-The dataset contains multiple annotations per instance, given the option to quantify agreement scores for annotations. This dataset can be used to track how words and their meanings have changed over time
-"""
-
-
-_HOMEPAGE = "https://github.com/cultural-ai/ConConCor"
-
-
-_LICENSE = "CC-BY"
-
-_URLS = [
-    "https://raw.githubusercontent.com/cultural-ai/ConConCor/main/Dataset/Annotations.csv",
-    "https://raw.githubusercontent.com/cultural-ai/ConConCor/main/Dataset/Extracts.csv",
-]
-response_mapping = {
-    "Omstreden naar huidige maatstaven": "Contentious according to current standards",
-    "Niet omstreden": "Not contentious",
-    "Weet ik niet": "I don't know",
-    "Onleesbare OCR": "Illegible OCR",
-}
-
-logger = datasets.utils.logging.get_logger(__name__)
-
-
-class ContentiousContexts(datasets.GeneratorBasedBuilder):
-    """This dataset contains extracts from historical Dutch newspapers which have been containing keywords of potentially contentious words"""
-
-    VERSION = datasets.Version("1.0.0")
-
-    def _info(self):
-        features = datasets.Features(
-            {
-                "extract_id": datasets.Value("string"),
-                "text": datasets.Value("string"),
-                "target": datasets.Value("string"),
-                "annotator_responses_english": [
-                    {
-                        "id": datasets.Value("string"),
-                        "response": datasets.Value("string"),
-                    }
-                ],
-                "annotator_responses_dutch": [
-                    {
-                        "id": datasets.Value("string"),
-                        "response": datasets.Value("string"),
-                    }
-                ],
-                "annotator_suggestions": [
-                    {
-                        "id": datasets.Value("string"),
-                        "suggestion": datasets.Value("string"),
-                    }
-                ],
-            }
-        )
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        ann_file = dl_manager.download(_URLS[0])
-        text_file = dl_manager.download(_URLS[1])
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={"ann_file": ann_file, "text_file": text_file},
-            ),
-        ]
-
-    def _generate_examples(self, ann_file, text_file):
-        annotations = pd.read_csv(open(ann_file), dtype="object")
-        texts = pd.read_csv(open(text_file), dtype="object")
-        annotations.fillna("", inplace=True)
-        texts.fillna("", inplace=True)
-        for _, row in texts.iterrows():
-            data_point = {
-                "extract_id": row["extract_id"],
-                "target": row["target_compound_bolded"],
-                "text": row["text"],
-            }
-            annotator_responses = annotations[
-                annotations["extract_id"] == row["extract_id"]
-            ]
-            resp_en_list = []
-            resp_nl_list = []
-            sugg_list = []
-            for _, ann_row in annotator_responses.iterrows():
-                ann_id = ann_row["anonymised_participant_id"]
-                response_dutch = ann_row["response"]
-                response_english = response_mapping[response_dutch]
-                suggestion = ann_row["suggestion"]
-                resp_en_list.append({"id": ann_id, "response": response_english})
-                resp_nl_list.append({"id": ann_id, "response": response_dutch})
-                sugg_list.append({"id": ann_id, "suggestion": suggestion})
-
-            data_point["annotator_responses_english"] = resp_en_list
-            data_point["annotator_responses_dutch"] = resp_nl_list
-            data_point["annotator_suggestions"] = sugg_list
-            yield data_point["extract_id"], data_point