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from typing import Dict, Any, List |
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import datasets |
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class SuperSciRepConfig(datasets.BuilderConfig): |
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"""BuilderConfig for SuperGLUE.""" |
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def __init__(self, features: Dict[str, Any], task_type: str, citation: str = "", |
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licenses: str = "", is_training: bool = False, homepage: str = "", url="", **kwargs): |
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"""BuilderConfig for SuperGLUE. |
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Args: |
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features: *list[string]*, list of the features that will appear in the |
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feature dict. Should not include "label". |
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data_url: *string*, url to download the zip file from. |
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citation: *string*, citation for the data set. |
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url: *string*, url for information about the data set. |
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label_classes: *list[string]*, the list of classes for the label if the |
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label is present as a string. Non-string labels will be cast to either |
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'False' or 'True'. |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super().__init__(version=datasets.Version("1.1.0"), **kwargs) |
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self.features = features |
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self.task_type = task_type |
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self.citation = citation |
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self.license = licenses |
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self.is_training = is_training |
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self.homepage = homepage |
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self.url = url |
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@classmethod |
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def get_features(self, feature_names: List[str], type_mapping: Dict[str, Any] = None) -> Dict[str, Any]: |
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full_text_mapping = {"full_text": datasets.features.Sequence({"title": datasets.Value("string"), "sentences": datasets.features.Sequence(datasets.Value("string"))})} |
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type_mapping = {**full_text_mapping, **type_mapping} |
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features = {name: type_mapping[name] if name in type_mapping else datasets.Value("string") for name in |
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feature_names} |
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if "corpus_id" in features: |
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features["corpus_id"] = datasets.Value("uint64") |
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return features |
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SUPERSCIREPEVAL_CONFIGS = [ |
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SuperSciRepConfig(name="fos", features=SuperSciRepConfig.get_features( |
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["doc_id", "corpus_id", "title", "abstract", "full_text", "labels", "labels_text"], |
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{"labels": datasets.Sequence(datasets.Value("int32")), |
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"labels_text": datasets.Sequence(datasets.Value("string"))}), |
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task_type="classification (multi-label)", is_training=True, description=""), |
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SuperSciRepConfig(name="cite_count", features=SuperSciRepConfig.get_features( |
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["doc_id", "corpus_id", "title", "abstract", "full_text", "venue", "n_citations", "log_citations"], |
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{"n_citations": datasets.Value("int32"), |
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"log_citations": datasets.Value("float32")}), |
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task_type="regression", is_training=True, description="" |
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), |
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SuperSciRepConfig(name="pub_year", features=SuperSciRepConfig.get_features( |
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["doc_id", "corpus_id", "title", "abstract", "full_text", "year", "venue", "norm_year", "scaled_year", "n_authors", "norm_authors"], |
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{"year": datasets.Value("int32"), "norm_year": datasets.Value("float32"), |
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"scaled_year": datasets.Value("float32"), "n_authors": datasets.Value("int32"), |
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"norm_authors": datasets.Value("float32"), }), |
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task_type="regression", is_training=True, description=""), |
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SuperSciRepConfig(name="high_influence_cite", |
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features=SuperSciRepConfig.get_features(["query", "candidates"], |
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{"query": { |
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"doc_id": datasets.Value("string"), |
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"title": datasets.Value("string"), |
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"abstract": datasets.Value( |
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"string"), |
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"full_text": datasets.features.Sequence({"title": datasets.Value("string"), "sentences": datasets.features.Sequence(datasets.Value("string"))}), |
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"corpus_id": datasets.Value("uint64")}, |
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"candidates": |
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[{"doc_id": datasets.Value("string"), |
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"title": datasets.Value("string"), |
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"abstract": datasets.Value( |
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"string"), |
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"full_text": datasets.features.Sequence({"title": datasets.Value("string"), "sentences": datasets.features.Sequence(datasets.Value("string"))}), |
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"corpus_id": datasets.Value("uint64"), |
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"score": datasets.Value("uint32")}]}), |
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task_type="proximity", is_training=True, description=""), |
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SuperSciRepConfig(name="search", |
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features=SuperSciRepConfig.get_features(["query", "doc_id", "candidates"], |
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{"candidates": |
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[{ |
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"doc_id": datasets.Value("string"), |
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"title": datasets.Value("string"), |
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"abstract": datasets.Value( |
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"string"), |
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"full_text": datasets.features.Sequence({"title": datasets.Value("string"), "sentences": datasets.features.Sequence(datasets.Value("string"))}), |
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"corpus_id": datasets.Value("uint64"), |
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"venue": datasets.Value("string"), |
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"year": datasets.Value("float64"), |
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"author_names": datasets.Sequence(datasets.Value("string")), |
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"n_citations": datasets.Value("int32"), |
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"n_key_citations": datasets.Value("int32"), |
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"score": datasets.Value("uint32")}]}), |
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task_type="search", is_training=True, description=""), |
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SuperSciRepConfig(name="feeds_1", |
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features=SuperSciRepConfig.get_features(["query", "feed_id", "candidates"], |
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{"query": { |
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"doc_id": datasets.Value("string"), |
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"title": datasets.Value("string"), |
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"abstract": datasets.Value( |
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"string"), |
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"full_text": datasets.features.Sequence({"title": datasets.Value("string"), "sentences": datasets.features.Sequence(datasets.Value("string"))}), |
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"corpus_id": datasets.Value("uint64")}, |
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"candidates": |
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[{ |
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"doc_id": datasets.Value("string"), |
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"title": datasets.Value("string"), |
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"abstract": datasets.Value( |
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"string"), |
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"full_text": datasets.features.Sequence({"title": datasets.Value("string"), "sentences": datasets.features.Sequence(datasets.Value("string"))}), |
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"corpus_id": datasets.Value("uint64"), |
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"score": datasets.Value("uint32")}]}), |
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task_type="proximity", description="", url="feeds/feeds_1"), |
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SuperSciRepConfig(name="feeds_m", |
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features=SuperSciRepConfig.get_features(["query", "feed_id", "candidates"], |
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{"query": { |
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"doc_id": datasets.Value("string"), |
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"title": datasets.Value("string"), |
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"abstract": datasets.Value( |
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"string"), |
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"full_text": datasets.features.Sequence({"title": datasets.Value("string"), "sentences": datasets.features.Sequence(datasets.Value("string"))}), |
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"corpus_id": datasets.Value("uint64")}, |
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"candidates": |
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[{ |
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"doc_id": datasets.Value("string"), |
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"title": datasets.Value("string"), |
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"abstract": datasets.Value( |
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"string"), |
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"full_text": datasets.features.Sequence({"title": datasets.Value("string"), "sentences": datasets.features.Sequence(datasets.Value("string"))}), |
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"corpus_id": datasets.Value("uint64"), |
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"score": datasets.Value("uint32")}]}), |
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task_type="proximity", description="", url="feeds/feeds_m"), |
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SuperSciRepConfig(name="feeds_title", |
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features=SuperSciRepConfig.get_features(["query", "doc_id", "feed_id", "abbreviations", "candidates"], |
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{"candidates": |
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[{ |
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"doc_id": datasets.Value("string"), |
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"title": datasets.Value("string"), |
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"abstract": datasets.Value( |
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"string"), |
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"full_text": datasets.features.Sequence({"title": datasets.Value("string"), "sentences": datasets.features.Sequence(datasets.Value("string"))}), |
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"corpus_id": datasets.Value("uint64"), |
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"score": datasets.Value("uint32")}]}), |
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task_type="search", description="", url="feeds/feeds_title"), |
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SuperSciRepConfig(name="peer_review_score_hIndex", features=SuperSciRepConfig.get_features( |
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["doc_id", "corpus_id", "title", "abstract", "full_text", "rating", "confidence", "authors", "decision", "mean_rating", "hIndex"], |
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{"mean_rating": datasets.Value("float32"), |
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"rating": datasets.Sequence(datasets.Value("int32")), |
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"authors": datasets.Sequence(datasets.Value("string")), |
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"hIndex": datasets.Sequence(datasets.Value("string")) |
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}), |
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task_type="regression", description="" |
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), |
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SuperSciRepConfig(name="tweet_mentions", features=SuperSciRepConfig.get_features( |
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["doc_id", "corpus_id", "title", "abstract", "full_text", "index", "retweets", "count", "mentions"], |
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{"index": datasets.Value("int32"), "count": datasets.Value("int32"), |
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"retweets": datasets.Value("float32"), "mentions": datasets.Value("float32")}), |
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task_type="regression", description="", |
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citation="@article{Jain2021TweetPapAD,\ |
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title={TweetPap: A Dataset to Study the Social Media Discourse of Scientific Papers},\ |
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author={Naman Jain and Mayank Kumar Singh},\ |
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journal={2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL)},\ |
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year={2021},\ |
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pages={328-329}\ |
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}"), |
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SuperSciRepConfig(name="scidocs_view_cite_read", features=SuperSciRepConfig.get_features( |
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["doc_id", "corpus_id", "title", "abstract", "full_text", "authors", "cited_by", "references", "year"], |
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{"year": datasets.Value("int32"), |
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"authors": datasets.Sequence(datasets.Value("string")), |
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"cited_by": datasets.Sequence(datasets.Value("string")), |
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"references": datasets.Sequence(datasets.Value("string")) |
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}), |
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task_type="metadata", description="", url="scidocs/view_cite_read", |
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homepage="https://github.com/allenai/scidocs", citation="@inproceedings{specter2020cohan,\ |
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title={SPECTER: Document-level Representation Learning using Citation-informed Transformers},\ |
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author={Arman Cohan and Sergey Feldman and Iz Beltagy and Doug Downey and Daniel S. Weld},\ |
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booktitle={ACL},\ |
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year={2020}\ |
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}"), |
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SuperSciRepConfig(name="paper_reviewer_matching", features=SuperSciRepConfig.get_features( |
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["doc_id", "title", "abstract", "full_text", "corpus_id"], |
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{}), |
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task_type="metadata", description="", citation="@inproceedings{Mimno2007ExpertiseMF,\ |
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title={Expertise modeling for matching papers with reviewers},\ |
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author={David Mimno and Andrew McCallum},\ |
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booktitle={KDD '07},\ |
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year={2007}\ |
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}, @ARTICLE{9714338,\ |
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author={Zhao, Yue and Anand, Ajay and Sharma, Gaurav},\ |
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journal={IEEE Access}, \ |
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title={Reviewer Recommendations Using Document Vector Embeddings and a Publisher Database: Implementation and Evaluation}, \ |
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year={2022},\ |
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volume={10},\ |
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number={},\ |
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pages={21798-21811},\ |
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doi={10.1109/ACCESS.2022.3151640}}") |
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] |
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