Upload keyword_pubmed.py

keyword_pubmed.py

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+""" Loading script for the Keyword PubMed dataset."""
+
+
+import os
+from pathlib import Path
+import re
+
+import datasets
+
+
+class KeywordPubmedDataset(datasets.GeneratorBasedBuilder):
+
+    VERSION = datasets.Version("1.0.0")
+
+
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="sentence", version=VERSION, description="Comprises sentences that contain a keyword"),
+        datasets.BuilderConfig(name="document", version=VERSION, description="Contains all the sentences in a document that contains at least a keyword"),
+    ]
+
+    DEFAULT_CONFIG_NAME = "document"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+
+    def _info(self):
+        if self.config.name == "sentence":
+            features = datasets.Features(
+                {
+                    "sentence": datasets.Value("string"),
+                    "pmcid": datasets.Value("string"),
+                    "keyword_rank": datasets.Value("int32"),
+                }
+            )
+        else:
+            features = datasets.Features(
+                {
+                    "sentence": datasets.Value("string"),
+                    "pmcid": datasets.Value("string"),
+                    "keyword_rank": datasets.Value("int32"),
+                }
+            )
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description= "Dataset for MLM comprising sentences that contain a keyword relevant to the domain",
+            # This defines the different columns of the dataset and their types
+            features=features,  # Here we define them above because they are different between the two configurations
+            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
+            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
+            # supervised_keys=("sentence", "label"),
+            # Homepage of the dataset for documentation
+            # homepage=_HOMEPAGE,
+            # License for the dataset if available
+            # license=_LICENSE,
+            # Citation for the dataset
+            # citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        
+
+        if self.config.data_dir:
+            data_dir = self.config.data_dir
+        else:
+            data_dir = dl_manager.download_and_extract('data_files.tar.gz')
+
+        # Load the keywords from the file
+        with open(os.path.join(data_dir, 'keywords.txt'), 'r') as f:
+            keyword_ranks = {line.strip().split(":")[0].lower():rank for rank, line in enumerate(f)}
+            keywords = set(keyword_ranks.keys())
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "dirpath": os.path.join(data_dir, "train"),
+                    "keywords": keywords,
+                    "ranks": keyword_ranks,
+                },
+            ),
+
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "dirpath": os.path.join(data_dir, "dev"),
+                    "keywords": keywords,
+                    "ranks": keyword_ranks,
+                },
+            ),
+        ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, dirpath, keywords, ranks):
+        item_ix = 0
+        for filepath in Path(dirpath).iterdir():
+            filepath = Path(filepath)
+            if filepath.suffix == ".txt":
+                pmcid = filepath.name.split(".")[0]
+                with filepath.open(encoding="utf-8") as f:
+                    for sentence in f:
+                        sentence = sentence.strip()
+                        if sentence: # Ignore blanks
+                            sentence = re.sub("\s+", " ", sentence)
+                            has_keyword,  rank = self._has_keyword(sentence, keywords, ranks) 
+                            if self.config.name == "sentence":
+                                # Yields examples as (key, example) tuples
+                                if has_keyword:
+                                    yield item_ix, {
+                                        "sentence": sentence,
+                                        "keyword_rank": rank,
+                                        "pmcid": pmcid
+                                    }
+                                    item_ix += 1
+
+                            else: # Else document
+                                yield item_ix, {
+                                    "sentence": sentence,
+                                    "keyword_rank": rank,
+                                    "pmcid": pmcid
+                                }
+                                item_ix += 1
+
+    def _has_keyword(self, sentence, keywords, ranks):
+        # Lowercase and split the sentence
+        words = sentence.lower().split()
+        # Check every word until it finds a keyword
+        for word in words:
+            if word in keywords:
+                return True, ranks[word]
+        return False, -1
+
+
+if __name__  == "__main__":
+    ds = KeywordPubmedDataset()
+    ds.download_and_prepare()