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.gitattributes

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 *.mp3 filter=lfs diff=lfs merge=lfs -text
 *.ogg filter=lfs diff=lfs merge=lfs -text
 *.wav filter=lfs diff=lfs merge=lfs -text
+validation.json filter=lfs diff=lfs merge=lfs -text
+test.json filter=lfs diff=lfs merge=lfs -text
+train.json filter=lfs diff=lfs merge=lfs -text

README.md

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+# KP20k dataset for Keyphrase Generation
+
+## About
+KP20k is a dataset for benchmarking keyphrase extraction and generation models.
+The data is composed of 570 809 abstracts and their associated titles from scientific articles.
+
+Details about the dataset can be found in the original paper:
+- Meng et al 2017.
+  [Deep keyphrase Generation](https://aclanthology.org/P17-1054.pdf)
+  Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, pages 582–592
+  
+## Content
+
+The dataset is divided into the following three splits:
+
+| Split      | # documents | # keyphrases by document (average) | % Present | % Reordered | % Mixed | % Unseen |
+| :--------- | ----------: | -----------:                       | --------: | ----------: | ------: | -------: |
+| Train      | 530 809     |                5.28                |   40.65   | 7.58        | 24.43   |   27.34  |
+| Test       | 20 000      |                5.29                |   40.70   | 7.63        | 24.31   |   27.35  |
+| Validation | 20 000      |                5.27                |   40.80   | 7.56        | 24.52   |   27.12  |
+
+
+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.
+- **prmu**: list of <u>P</u>resent-<u>R</u>eordered-<u>M</u>ixed-<u>U</u>nseen categories for reference keyphrases.

dataset_infos.json

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+{"KP20k": {"description": "", "citation": "", "homepage": "", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"},"title": {"dtype": "string", "id": null, "_type": "Value"},"abstract": {"dtype": "string", "id": null, "_type": "Value"},  "keyphrases": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "prmu": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "json", "config_name": "KP20k", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 654714676, "num_examples": 530809, "dataset_name": "json"}, "test": {"name": "test", "num_bytes": 24675779, "num_examples": 20000, "dataset_name": "json"}, "validation": {"name": "validation", "num_bytes": 24657665, "num_examples": 20000, "dataset_name": "json"}}, "download_size": 720581004, "post_processing_size": null, "dataset_size": 704048120, "size_in_bytes": 1424629124}, "KP20k": {"description": "", "citation": "", "homepage": "", "license": "", "features": {"abstract": {"dtype": "string", "id": null, "_type": "Value"}, "title": {"dtype": "string", "id": null, "_type": "Value"}, "id": {"dtype": "string", "id": null, "_type": "Value"}, "keyphrases": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "prmu": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "json", "config_name": "KP20k", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 654714676, "num_examples": 530809, "dataset_name": "json"}, "test": {"name": "test", "num_bytes": 24675779, "num_examples": 20000, "dataset_name": "json"}, "validation": {"name": "validation", "num_bytes": 24657665, "num_examples": 20000, "dataset_name": "json"}}, "download_size": 720581004, "post_processing_size": null, "dataset_size": 704048120, "size_in_bytes": 1424629124}}

kp20k.py

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+import csv
+import json
+import os
+
+logger = datasets.logging.get_logger(__name__)
+
+import datasets
+
+
+
+_CITATION = """\
+@InProceedings{meng-EtAl:2017:Long,
+  author    = {Meng, Rui  and  Zhao, Sanqiang  and  Han, Shuguang  and  He, Daqing  and  Brusilovsky, Peter  and  Chi, Yu},
+  title     = {Deep Keyphrase Generation},
+  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
+  month     = {July},
+  year      = {2017},
+  address   = {Vancouver, Canada},
+  publisher = {Association for Computational Linguistics},
+  pages     = {582--592},
+  url       = {http://aclweb.org/anthology/P17-1054}
+}
+"""
+
+# You can copy an official description
+_DESCRIPTION = """\
+KP20k dataset for keyphrase extraction and generation in scientific paper.
+"""
+
+_HOMEPAGE = "http://memray.me/uploads/acl17-keyphrase-generation.pdf"
+
+# License information from the original source page https://github.com/memray/seq2seq-keyphrase
+_LICENSE = "MIT 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 = {
+    "test": "test.json",
+    "train": "train.json",
+    "validation": "validation.json"
+}
+
+class KP20kConfig(datasets.BuilderConfig):
+
+    def __init__(self, **kwargs):
+        
+        super(KP20kConfig, self).__init__(**kwargs)
+
+# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
+class KP20k(datasets.GeneratorBasedBuilder):
+
+    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 = [
+        KP20kConfig(
+            name="raw", 
+            version=VERSION, 
+            description="This part of the 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
+        print(self.config)
+        features = datasets.Features(
+            {
+                'id': datasets.Value(dtype="string"),
+                "title": datasets.Value("string"),
+                "abstract": 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,  
+            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.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir,"train.json"),
+                    "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.json"),
+                    "split": "test"
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir,"validation.json"),
+                    "split": "validation",
+                },
+            ),
+        ]
+
+    # 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"],
+                    "keyphrases":  data["keyphrases"],
+                    "prmu": data["prmu"],
+                }

test.json

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+size 25255559

train.json

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+oid sha256:59a20765c76126945e9eb298d7837175e886403f113c1a23c0cab7dc3cd9496d
+size 670087948

validation.json

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