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import json |
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import datasets |
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_CITATION = """\ |
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@inproceedings{10.5555/1859664.1859668, |
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author = {Kim, Su Nam and Medelyan, Olena and Kan, Min-Yen and Baldwin, Timothy}, |
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title = {SemEval-2010 Task 5: Automatic Keyphrase Extraction from Scientific Articles}, |
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year = {2010}, |
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publisher = {Association for Computational Linguistics}, |
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address = {USA}, |
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abstract = {This paper describes Task 5 of the Workshop on Semantic Evaluation 2010 (SemEval-2010). Systems are to automatically assign keyphrases or keywords to given scientific articles. The participating systems were evaluated by matching their extracted keyphrases against manually assigned ones. We present the overall ranking of the submitted systems and discuss our findings to suggest future directions for this task.}, |
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booktitle = {Proceedings of the 5th International Workshop on Semantic Evaluation}, |
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pages = {21–26}, |
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numpages = {6}, |
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location = {Los Angeles, California}, |
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series = {SemEval '10} |
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} |
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""" |
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_DESCRIPTION = """\ |
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""" |
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_HOMEPAGE = "" |
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_LICENSE = "" |
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_URLS = { |
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"test": "test.jsonl" |
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} |
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class SemEval2010(datasets.GeneratorBasedBuilder): |
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"""TODO: Short description of my dataset.""" |
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VERSION = datasets.Version("0.0.1") |
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BUILDER_CONFIGS = [ |
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datasets.BuilderConfig(name="extraction", version=VERSION, |
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description="This part of my dataset covers extraction"), |
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datasets.BuilderConfig(name="generation", version=VERSION, |
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description="This part of my dataset covers generation"), |
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datasets.BuilderConfig(name="raw", version=VERSION, description="This part of my dataset covers the raw data"), |
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] |
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DEFAULT_CONFIG_NAME = "extraction" |
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def _info(self): |
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if self.config.name == "extraction": |
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features = datasets.Features( |
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{ |
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"id": datasets.Value("string"), |
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"document": datasets.features.Sequence(datasets.Value("string")), |
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"doc_bio_tags": datasets.features.Sequence(datasets.Value("string")) |
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} |
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) |
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elif self.config.name == "generation": |
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features = datasets.Features( |
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{ |
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"id": datasets.Value("string"), |
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"document": datasets.features.Sequence(datasets.Value("string")), |
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"extractive_keyphrases": datasets.features.Sequence(datasets.Value("string")), |
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"abstractive_keyphrases": datasets.features.Sequence(datasets.Value("string")) |
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} |
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) |
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else: |
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features = datasets.Features( |
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{ |
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"id": datasets.Value("string"), |
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"document": datasets.features.Sequence(datasets.Value("string")), |
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"doc_bio_tags": datasets.features.Sequence(datasets.Value("string")), |
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"extractive_keyphrases": datasets.features.Sequence(datasets.Value("string")), |
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"abstractive_keyphrases": datasets.features.Sequence(datasets.Value("string")), |
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"other_metadata": datasets.features.Sequence( |
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{ |
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"text": datasets.features.Sequence(datasets.Value("string")), |
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"bio_tags": datasets.features.Sequence(datasets.Value("string")) |
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} |
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) |
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} |
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) |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=features, |
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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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data_dir = dl_manager.download_and_extract(_URLS) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={ |
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"filepath": data_dir['test'], |
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"split": "test" |
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}, |
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), |
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] |
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def _generate_examples(self, filepath, split): |
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with open(filepath, encoding="utf-8") as f: |
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for key, row in enumerate(f): |
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data = json.loads(row) |
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if self.config.name == "extraction": |
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yield key, { |
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"id": data['paper_id'], |
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"document": data["document"], |
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"doc_bio_tags": data.get("doc_bio_tags") |
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} |
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elif self.config.name == "generation": |
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yield key, { |
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"id": data['paper_id'], |
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"document": data["document"], |
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"extractive_keyphrases": data.get("extractive_keyphrases"), |
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"abstractive_keyphrases": data.get("abstractive_keyphrases") |
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} |
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else: |
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yield key, { |
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"id": data['paper_id'], |
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"document": data["document"], |
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"doc_bio_tags": data.get("doc_bio_tags"), |
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"extractive_keyphrases": data.get("extractive_keyphrases"), |
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"abstractive_keyphrases": data.get("abstractive_keyphrases"), |
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"other_metadata": data.get("other_metadata") |
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} |
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