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README.md

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----
-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, A Large-Scale Dataset for keyphrase generation
-## About
-  
-This dataset is made of 5.6 million abstracts with author assigned keyphrases.
-
-Details about the dataset can be found in the original paper:
-Maël Houbre, Florian Boudin and Béatrice Daille. 2022. [A Large-Scale Dataset for Biomedical Keyphrase Generation](https://arxiv.org/abs/2211.12124). In Proceedings of the 13th International Workshop on Health Text Mining and Information Analysis (LOUHI 2022).
-
-  
-Reference (author-assigned) keyphrases are also categorized under the PRMU (<u>P</u>resent-<u>R</u>eordered-<u>M</u>ixed-<u>U</u>nseen) scheme as proposed in the following paper:
- - Florian Boudin and Ygor Gallina. 2021.
-   [Redefining Absent Keyphrases and their Effect on Retrieval Effectiveness](https://aclanthology.org/2021.naacl-main.330/). 
-   In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4185–4193, Online. Association for Computational Linguistics.
-   
-Text pre-processing (tokenization) is carried out using spacy (en_core_web_sm model) with a special rule to avoid splitting words with hyphens (e.g. graph-based is kept as one token). Stemming (Porter's stemmer implementation provided in nltk) is applied before reference keyphrases are matched against the source text.   
-  
-## Content
-
-The details of the dataset are in the table below:
-
-| Split        | # documents | # keyphrases by document (average) | % Present | % Reordered | % Mixed | % Unseen |
-| :----------- | ----------: | ---------------------------------: | --------: | ----------: | ------: | -------: |
-| Train small  |    500k     |               5.24                 |   66.31   |    7.16     |  12.60  |  13.93   |
-| Train medium |    2M       |               5.24                 |   66.30   |    7.18     |  12.57  |  13.95   |
-| Train large  |    5.6M     |               5.23                 |   66.32   |    7.18     |  12.55  |  13.95   |
-| Validation   |    20k      |               5.25                 |   66.44   |    7.07     |  12.45  |  14.05   |
-| Test         |    20k      |               5.22                 |   66.59   |    7.22     |  12.44  |  13.75   |
-
-
-
-The following data fields are available:
-- **id**: unique identifier of the document.
-- **title**: title of the document.
-- **abstract**: abstract of the document.
-- **keyphrases**: list of reference keyphrases.
-- **mesh terms**: list of indexer assigned MeSH terms if available (around 68% of the articles)
-- **prmu**: list of <u>P</u>resent-<u>R</u>eordered-<u>M</u>ixed-<u>U</u>nseen categories for reference keyphrases.
-- **authors**: list of the article's authors
-- **year**: publication year
-
-**NB**: The present keyphrases (represented by the "P" label in the PRMU column) are sorted by their apparition order in the text (title + text).
-
-

kpbiomed.py

@@ -1,215 +0,0 @@
-"""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"],
-                }
-
-

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