Create medwiki file

dataset.py

@@ -0,0 +1,121 @@
+import os
+import re
+from pathlib import Path
+
+import datasets
+
+
+logger = datasets.logging.get_logger(__name__)
+
+TXT_PATTERN = r"^.*\.txt$"
+
+_CITATION = """\
+@misc{https://doi.org/10.48550/arxiv.2210.06345,
+  doi = {10.48550/ARXIV.2210.06345},
+  
+  url = {https://arxiv.org/abs/2210.06345},
+  
+  author = {Liévin, Valentin and Motzfeldt, Andreas Geert and Jensen, Ida Riis and Winther, Ole},
+  
+  keywords = {Computation and Language (cs.CL), Information Retrieval (cs.IR), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences, I.2.7; H.3.3; I.2.1},
+  
+  title = {Variational Open-Domain Question Answering},
+  
+  publisher = {arXiv},
+  
+  year = {2022},
+  
+  copyright = {arXiv.org perpetual, non-exclusive license}
+}
+"""
+
+_VERSION = "0.0.1"
+_HOMEPAGE = "https://github.com/VodLM"
+
+_DESCRIPTION = """\
+The MedWiki corpus, which is distributed under the MIT license, 
+consists of a subset of 4.5% of the English Wikipedia articles 
+and has been specifically curated for the MedMCQA and USMLE datasets. 
+The collection was created by utilizing the Wikipedia API to search 
+for articles related to each answer option present in the MedMCQA and 
+USMLE datasets. The top ten Wikipedia articles for each answer option 
+were selected and included in the final corpus. This subset covers a 
+wide range of topics in the field of medicine that could be relevant 
+for answering questions in this domain.
+questions.
+"""
+
+_URL = "https://f001.backblazeb2.com/file/FindZebraData/fz-openqa/datasets/medwiki_v6.zip"
+
+
+class MedWikipediaCorpusConfig(datasets.BuilderConfig):
+    """BuilderConfig for the MedQa English Corpus objecxt."""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for the Corpus object.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(MedWikipediaCorpusConfig, self).__init__(**kwargs)
+
+class MedWikipediaCorpusGenerator(datasets.GeneratorBasedBuilder):
+    """MedWikipediaCorpus Dataset. Version 0.0.1"""
+
+    BUILDER_CONFIGS = [
+        MedWikipediaCorpusConfig(
+            name="plain_text",
+            version=datasets.Version(_VERSION, ""),
+            description="Plain text",
+        ),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "document.idx": datasets.Value("int32"),
+                    "document.text": datasets.Value("string"),
+                    "document.title": datasets.Value("string"),
+                }
+            ),
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        downloaded_file = dl_manager.download_and_extract(_URL)
+        if not Path(downloaded_file).is_dir():
+            raise Exception(
+                f"Could not download the dataset Content of `downloaded_file`:"
+                f"{open(downloaded_file, 'r').read()}"
+            )
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"output_dir": Path(downloaded_file)},
+            )
+        ]
+
+    def _generate_examples(self, output_dir: str):
+        logger.info("generating examples from = %s", output_dir)
+        paths = [
+            p
+            for p in Path(output_dir).iterdir()
+            if p.is_dir() and (p.name.startswith("med_x_wiki") or p.name.startswith("wikipedia"))
+        ]
+        assert len(paths) == 1, f"Found {len(paths)} directories in {output_dir}: {paths}"
+        path = paths[0]
+
+        # list files
+        data_files = [os.path.join(path, p) for p in os.listdir(path) if re.findall(TXT_PATTERN, p)]
+
+        # iterate and yield documents
+        for i, fn in enumerate(data_files):
+            with open(fn, "r") as f:
+                # the first line is the title
+                title = f.readline()
+                text = f.read()
+                yield i, {"document.text": text, "document.idx": i, "document.title": title}