Update medical_wikipedia.py

medical_wikipedia.py

@@ -1,96 +1,95 @@
-# 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.
-"""MedWiki."""
-
-import os
-
-import datasets
-import pandas as pd
-
-
-_CITATION = """\
-@article{corbeil2024iryonlp,
-  title={IryoNLP at MEDIQA-CORR 2024: Tackling the Medical Error Detection & Correction Task On the Shoulders of Medical Agents},
-  author={Jean-Philippe Corbeil},
-  journal={arXiv preprint arXiv:2404.15488},
-  year={2024}
-}
-"""
-
-_DESCRIPTION = """\
-This is a filtered version of the `Cohere/wikipedia-22-12` on medical topic articles using `MaartenGr/BERTopic_Wikipedia`. Keep note that some articles in the viewer might seem off topic, but usually they are related in some way (e.g. World War I is linked to the Spanish Flu). This is artefacts of some noise in the topic modelling.
-"""
-_HOMEPAGE = ""
-_LICENSE = "CC-BY-SA"
-_URLS = {
-    "first_domain": "Cohere/wikipedia-22-12",
-}
-
-
-class MedWikiDataset(datasets.GeneratorBasedBuilder):
-    """Medical Wikipedia Articles."""
-
-    VERSION = datasets.Version("0.0.1")
-
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="first_domain", version=VERSION, description="This part of my dataset covers a first domain"),
-    ]
-
-    DEFAULT_CONFIG_NAME = "first_domain"
-
-    def _info(self):
-        features = datasets.Features(
-            {
-                "wiki_id": datasets.Value("int32"),
-                "title": datasets.Value("string"),
-                "text": datasets.Value("string"),
-                "paragraph_id": datasets.Value("int32"),
-            }
-        )
-
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        urls = _URLS[self.config.name]
-        dataset = datasets.load_dataset(urls, "en", trust_remote_code=True, cache_dir="/Users/jcorbeil/Documents/datasets/TEMP")
-        dl_manager.download_and_extract(urls + "/med_topics.csv")
-        df = pd.read_csv(os.path.join(self.cache_dir, "med_topics.csv"))
-        med_wiki_ids = set(df["wiki_id"].values.tolist())
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "dataset": dataset["train"],
-                    "med_wiki_ids": med_wiki_ids,
-                },
-            ),
-        ]
-
-    def _generate_examples(self, dataset, med_wiki_ids):
-        count = -1
-        for data in dataset:
-            if data["wiki_id"] in med_wiki_ids:
-                count += 1
-                yield count, {
-                    "wiki_id": data["wiki_id"],
-                    "title": data["title"],
-                    "text": data["text"],
-                    "paragraph_id": data["paragraph_id"],
-                }
+# 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.
+"""MedWiki."""
+
+import os
+
+import datasets
+import pandas as pd
+
+
+_CITATION = """\
+@article{corbeil2024iryonlp,
+  title={IryoNLP at MEDIQA-CORR 2024: Tackling the Medical Error Detection & Correction Task On the Shoulders of Medical Agents},
+  author={Jean-Philippe Corbeil},
+  journal={arXiv preprint arXiv:2404.15488},
+  year={2024}
+}
+"""
+
+_DESCRIPTION = """\
+This is a filtered version of the `Cohere/wikipedia-22-12` on medical topic articles using `MaartenGr/BERTopic_Wikipedia`. Keep note that some articles in the viewer might seem off topic, but usually they are related in some way (e.g. World War I is linked to the Spanish Flu). This is artefacts of some noise in the topic modelling.
+"""
+_HOMEPAGE = ""
+_LICENSE = "CC-BY-SA"
+_URLS = {
+    "first_domain": "Cohere/wikipedia-22-12",
+}
+
+
+class MedWikiDataset(datasets.GeneratorBasedBuilder):
+    """Medical Wikipedia Articles."""
+
+    VERSION = datasets.Version("0.0.1")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="first_domain", version=VERSION, description="This part of my dataset covers a first domain"),
+    ]
+
+    DEFAULT_CONFIG_NAME = "first_domain"
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "wiki_id": datasets.Value("int32"),
+                "title": datasets.Value("string"),
+                "text": datasets.Value("string"),
+                "paragraph_id": datasets.Value("int32"),
+            }
+        )
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        urls = _URLS[self.config.name]
+        dataset = datasets.load_dataset(urls, "en", trust_remote_code=True, cache_dir="/Users/jcorbeil/Documents/datasets/TEMP")
+        df = pd.read_csv(os.path.join(self.cache_dir, "med_topics.csv"))
+        med_wiki_ids = set(df["wiki_id"].values.tolist())
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "dataset": dataset["train"],
+                    "med_wiki_ids": med_wiki_ids,
+                },
+            ),
+        ]
+
+    def _generate_examples(self, dataset, med_wiki_ids):
+        count = -1
+        for data in dataset:
+            if data["wiki_id"] in med_wiki_ids:
+                count += 1
+                yield count, {
+                    "wiki_id": data["wiki_id"],
+                    "title": data["title"],
+                    "text": data["text"],
+                    "paragraph_id": data["paragraph_id"],
+                }