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