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data.jsonl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a03b0b004ab77c9ada8e4fcc6912d023ef85905cf80b2415a1bc032fdcfdf888
+size 40569656

pubmed.py

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+"""Inspec benchmark dataset for keyphrase extraction an generation."""
+
+
+import csv
+import json
+import os
+
+import datasets
+
+
+
+
+# You can copy an official description
+_DESCRIPTION = """\
+PubMed benchmark dataset for keyphrase extraction and generation.
+"""
+
+# TODO: Add a link to an official homepage for the dataset here
+_HOMEPAGE = "[schutz-2008]: https://github.com/snkim/AutomaticKeyphraseExtraction/blob/master/Schutz2008.tar.gz"
+
+_LICENSE = ""
+
+_CITATION = """@MasterThesis{Schutz:2008,
+  author = {Alexander Thorsten Schutz}, 
+  title = {Keyphrase Extraction from Single Documents in the Open Domain Exploiting Linguistic and Statistical Methods},
+  booktitle = {National University of Ireland},
+  year = {2008}
+}"""
+
+# TODO: Add link to the official dataset URLs here
+# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
+# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
+_URLS = {
+    "test": "data.jsonl",
+}
+
+# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
+class Pubmed(datasets.GeneratorBasedBuilder):
+    """TODO: Short description of my dataset."""
+
+    VERSION = datasets.Version("1.0.0")
+
+    # This is an example of a dataset with multiple configurations.
+    # If you don't want/need to define several sub-sets in your dataset,
+    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
+
+    # If you need to make complex sub-parts in the datasets with configurable options
+    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
+    # BUILDER_CONFIG_CLASS = MyBuilderConfig
+
+    # You will be able to load one or the other configurations in the following list with
+    # data = datasets.load_dataset('my_dataset', 'first_domain')
+    # data = datasets.load_dataset('my_dataset', 'second_domain')
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="raw", version=VERSION, description="This part of my dataset covers the raw data."),
+    ]
+
+    DEFAULT_CONFIG_NAME = "raw"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+
+    def _info(self):
+        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
+        if self.config.name == "raw":  # This is the name of the configuration selected in BUILDER_CONFIGS above
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "text": datasets.Value("string"),
+                    "keyphrases": datasets.features.Sequence(datasets.Value("string")),
+                    "stemmed_keyphrases": datasets.features.Sequence(datasets.Value("string"))
+                }
+            )
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # 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):
+        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
+        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
+
+        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
+        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
+        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
+        urls = _URLS
+        data_dir = dl_manager.download_and_extract(urls)
+        return [
+
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir["test"]),
+                    "split": "test"
+                },
+            )
+
+        ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, filepath, split):
+        # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
+        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
+        with open(filepath, encoding="utf-8") as f:
+            for key, row in enumerate(f):
+                data = json.loads(row)
+                # Yields examples as (key, example) tuples
+                yield key, {
+                    "id": data["id"],
+                    "text": data["text"],
+                    "keyphrases":  data["keyphrases"],
+                    "stemmed_keyphrases": data["stemmed_keyphrases"]
+                }
+