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README.md

@@ -1,3 +1,22 @@
 ---
-license: cc-by-nc-4.0
+annotations_creators:
+- unknown
+language_creators:
+- unknown
+language:
+- en
+license:
+- cc-by-nc-4.0
+multilinguality:
+- monolingual
+task_categories:
+- text-mining
+- text-generation
+task_ids:
+- keyphrase-generation
+- keyphrase-extraction
+size_categories:
+- 100K<n<1M
+pretty_name: 
+- KP-Biomed
 ---

kpbiomed.py

@@ -0,0 +1,215 @@
+"""KPBiomed benchmark dataset for keyphrase extraction an generation."""
+
+
+import csv
+import json
+import os
+
+import datasets
+
+
+# TODO: Add BibTeX citation
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+
+"""
+
+# You can copy an official description
+_DESCRIPTION = """\
+KPBiomed benchmark dataset for keyphrase extraction an generation.
+"""
+
+# TODO: Add a link to an official homepage for the dataset here
+_HOMEPAGE = ""
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = "Apache 2.0 License"
+
+# 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 = {
+    "train_large": "train_large.jsonl",
+    "train_medium" : "train_medium.jsonl",
+    "train_small" : "train_small.jsonl",
+    "val" : "val.jsonl",
+    "test" : "test.jsonl"
+
+}
+
+
+# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
+class KPBiomed(datasets.GeneratorBasedBuilder):
+    """TODO: Short description of my dataset."""
+
+    VERSION = datasets.Version("0.0.1")
+
+    # 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="large", version=VERSION, description="This part of my dataset covers the large training data."),
+        datasets.BuilderConfig(name="medium", version=VERSION, description="This part of my dataset covers the medium training data."),
+        datasets.BuilderConfig(name="small", version=VERSION, description="This part of my dataset covers the small training data."),
+        
+    ]
+
+    DEFAULT_CONFIG_NAME = "small"  # 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 == "small":  # This is the name of the configuration selected in BUILDER_CONFIGS above
+        features = datasets.Features(
+            {
+                "id": datasets.Value("string"),
+                "title": datasets.Value("string"),
+                "abstract": datasets.Value("string"),
+                "authors": datasets.Value("string"),
+                "mesh_terms": datasets.features.Sequence(datasets.Value("string")),
+                "year": datasets.Value("string"),
+                "keyphrases": datasets.features.Sequence(datasets.Value("string")),
+                "prmu": 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)
+        if self.config.name == "large":
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    # These kwargs will be passed to _generate_examples
+                    gen_kwargs={
+                        "filepath": os.path.join(data_dir["train_large"]),
+                        "split": "train",
+                    },
+                ),
+                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",
+                    },
+                ),
+
+                datasets.SplitGenerator(
+                    name=datasets.Split.VALIDATION,
+                    # These kwargs will be passed to _generate_examples
+                    gen_kwargs={
+                        "filepath": os.path.join(data_dir["val"]),
+                        "split": "test",
+                    },
+                ),
+
+            ]
+        elif self.config.name == "medium":
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    # These kwargs will be passed to _generate_examples
+                    gen_kwargs={
+                        "filepath": os.path.join(data_dir["train_medium"]),
+                        "split": "train",
+                    },
+                ),
+                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",
+                    },
+                ),
+
+                datasets.SplitGenerator(
+                    name=datasets.Split.VALIDATION,
+                    # These kwargs will be passed to _generate_examples
+                    gen_kwargs={
+                        "filepath": os.path.join(data_dir["val"]),
+                        "split": "test",
+                    },
+                ),
+
+            ]
+
+        else:
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    # These kwargs will be passed to _generate_examples
+                    gen_kwargs={
+                        "filepath": os.path.join(data_dir["train_small"]),
+                        "split": "train",
+                    },
+                ),
+                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",
+                    },
+                ),
+
+                datasets.SplitGenerator(
+                    name=datasets.Split.VALIDATION,
+                    # These kwargs will be passed to _generate_examples
+                    gen_kwargs={
+                        "filepath": os.path.join(data_dir["val"]),
+                        "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"],
+                    "title": data["title"],
+                    "abstract": data["abstract"],
+                    "authors" : data["authors"],
+                    "mesh_terms" : data["mesh_terms"],
+                    "year" : data["year"],
+                    "keyphrases":  data["keyphrases"],
+                    "prmu": data["prmu"],
+                }
+
+

test.jsonl

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+oid sha256:cfccd4be512c3c0ea6816975a1831955fa8430f6f6e8e91556b130aec825c9da
+size 43524648

train_large.jsonl

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+oid sha256:caed51ed5152871e4513046ed1238eacd6bb6df437e2fca02240a0e1429f1e50
+size 12333280683

train_medium.jsonl

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+oid sha256:b1bc296b121c4fbb44d4bf2b0f18bb114f12777da1e0c22bd3ce77624178c7bf
+size 4416210275

train_small.jsonl

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+oid sha256:ebedc42e492c121bfb8aef3ade5237a5e48b220d7e55957ec57c0fd18d0fd9d0
+size 1086716933

val.jsonl

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+oid sha256:26e9b435f567a7c24d57fe6039c1810ad76feab75fbf10a1252c85ea801b15d2
+size 43603200