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a/super_scirep.py b/super_scirep.py index 90d6aafc6cf7637474ba94a738e11a142c1361d4..8e05fadc41dd0513d6ce7626d71130dcd2cfd6f4 100644 --- a/super_scirep.py +++ b/super_scirep.py @@ -21,7 +21,7 @@ import glob import datasets from datasets.data_files import DataFilesDict -from .scirepeval_configs import SCIREPEVAL_CONFIGS +from .super_scirep_config import SUPERSCIREPEVAL_CONFIGS # from datasets.packaged_modules.json import json @@ -75,7 +75,7 @@ class SuperSciRep(datasets.GeneratorBasedBuilder): # 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 = SCIREPEVAL_CONFIGS + BUILDER_CONFIGS = SUPERSCIREPEVAL_CONFIGS def _info(self): return datasets.DatasetInfo( diff --git a/super_scirep_config.py b/super_scirep_config.py new file mode 100644 index 0000000000000000000000000000000000000000..85293506397aeb4814f262ea0501984cbfd86907 --- /dev/null +++ b/super_scirep_config.py @@ -0,0 +1,218 @@ +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.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.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.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.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.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.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.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.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.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}}") + +] diff --git a/tweet_mentions/evaluation/dataset.arrow b/tweet_mentions/evaluation/dataset.arrow new file mode 100644 index 0000000000000000000000000000000000000000..da69066a3e4f1db02ca6d465a5bf088a12c61269 --- /dev/null +++ b/tweet_mentions/evaluation/dataset.arrow @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:45991d695568f8ea6b87fee5f280773948bb7d60218a7ae0cfa43a11ecdaad10 +size 383614536 diff --git a/tweet_mentions/evaluation/dataset_info.json b/tweet_mentions/evaluation/dataset_info.json new file mode 100644 index 0000000000000000000000000000000000000000..a2a86630a7beff50f7843e71fd76850dd7cfc010 --- /dev/null +++ b/tweet_mentions/evaluation/dataset_info.json @@ -0,0 +1,92 @@ +{ + "builder_name": "generator", + "citation": "", + "config_name": "default", + "dataset_size": 383611320, + "description": "", + "download_checksums": {}, + "download_size": 0, + "features": { + "doc_id": { + "dtype": "string", + "id": null, + "_type": "Value" + }, + "corpus_id": { + "dtype": "int64", + "id": null, + "_type": "Value" + }, + "title": { + "dtype": "string", + "id": null, + "_type": "Value" + }, + "abstract": { + "dtype": "string", + "id": null, + "_type": "Value" + }, + "index": { + "dtype": "int64", + "id": null, + "_type": "Value" + }, + "retweets": { + "dtype": "float64", + "id": null, + "_type": "Value" + }, + "count": { + "dtype": "int64", + "id": null, + "_type": "Value" + }, + "mentions": { + "dtype": "float64", + "id": null, + "_type": "Value" + }, + "full_text": [ + { + "sentences": { + "feature": { + "dtype": "string", + "id": null, + "_type": "Value" + }, + "length": -1, + "id": null, + "_type": "Sequence" + }, + "title": { + "dtype": "string", + "id": null, + "_type": "Value" + } + } + ] + }, + "homepage": "", + "license": "", + "post_processed": null, + "post_processing_size": null, + "size_in_bytes": 383611320, + "splits": { + "train": { + "name": "train", + "num_bytes": 383611320, + "num_examples": 12160, + "dataset_name": "generator" + } + }, + "supervised_keys": null, + "task_templates": null, + "version": { + "version_str": "0.0.0", + "description": null, + "major": 0, + "minor": 0, + "patch": 0 + } +} \ No newline at end of file diff --git a/tweet_mentions/evaluation/state.json b/tweet_mentions/evaluation/state.json new file mode 100644 index 0000000000000000000000000000000000000000..b16201a6ed32e3af60ca1f26da05a1b809d944dd --- /dev/null +++ b/tweet_mentions/evaluation/state.json @@ -0,0 +1,14 @@ +{ + "_data_files": [ + { + "filename": "dataset.arrow" + } + ], + "_fingerprint": "32fb5448bc49268b", + "_format_columns": null, + "_format_kwargs": {}, + "_format_type": null, + "_indexes": {}, + "_output_all_columns": false, + "_split": "train" +} \ No newline at end of file