Merge branch 'main' of https://huggingface.co/datasets/howey/super_scirep

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.lz4 filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
+*.flac filter=lfs diff=lfs merge=lfs -text
+*.mp3 filter=lfs diff=lfs merge=lfs -text
+*.ogg filter=lfs diff=lfs merge=lfs -text
+*.wav filter=lfs diff=lfs merge=lfs -text
+# Image files - uncompressed
+*.bmp filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.tiff filter=lfs diff=lfs merge=lfs -text
+# Image files - compressed
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.webp filter=lfs diff=lfs merge=lfs -text
+feeds_m/dataset_dict.json filter=lfs diff=lfs merge=lfs -text
+fos/dataset_dict.json filter=lfs diff=lfs merge=lfs -text
+paper_reviewer_matching/dataset_dict.json filter=lfs diff=lfs merge=lfs -text
+search/dataset_dict.json filter=lfs diff=lfs merge=lfs -text
+tweet_mentions/dataset_dict.json filter=lfs diff=lfs merge=lfs -text
+cite_count/dataset_dict.json filter=lfs diff=lfs merge=lfs -text
+feeds_1/dataset_dict.json filter=lfs diff=lfs merge=lfs -text
+feeds_title/dataset_dict.json filter=lfs diff=lfs merge=lfs -text
+high_influence_cite/dataset_dict.json filter=lfs diff=lfs merge=lfs -text
+peer_review_score_hIndex/dataset_dict.json filter=lfs diff=lfs merge=lfs -text
+pub_year/dataset_dict.json filter=lfs diff=lfs merge=lfs -text
+scidocs_view_cite_read/dataset_dict.json filter=lfs diff=lfs merge=lfs -text
+search/validation/state.json filter=lfs diff=lfs merge=lfs -text
+cite_count/validation/state.json filter=lfs diff=lfs merge=lfs -text
+feeds_title/evaluation/state.json filter=lfs diff=lfs merge=lfs -text
+fos/validation/dataset_info.json filter=lfs diff=lfs merge=lfs -text
+pub_year/validation/dataset_info.json filter=lfs diff=lfs merge=lfs -text
+pub_year/validation/state.json filter=lfs diff=lfs merge=lfs -text
+feeds_m/evaluation/dataset_info.json filter=lfs diff=lfs merge=lfs -text
+fos/train/dataset_info.json filter=lfs diff=lfs merge=lfs -text
+fos/validation/state.json filter=lfs diff=lfs merge=lfs -text
+search/validation/dataset_info.json filter=lfs diff=lfs merge=lfs -text
+feeds_m/evaluation/state.json filter=lfs diff=lfs merge=lfs -text
+pub_year/evaluation/state.json filter=lfs diff=lfs merge=lfs -text
+search/train/state.json filter=lfs diff=lfs merge=lfs -text
+tweet_mentions/evaluation/state.json filter=lfs diff=lfs merge=lfs -text
+feeds_1/evaluation/state.json filter=lfs diff=lfs merge=lfs -text
+fos/evaluation/state.json filter=lfs diff=lfs merge=lfs -text
+fos/train/state.json filter=lfs diff=lfs merge=lfs -text
+paper_reviewer_matching/evaluation/dataset_info.json filter=lfs diff=lfs merge=lfs -text
+pub_year/train/dataset_info.json filter=lfs diff=lfs merge=lfs -text
+tweet_mentions/evaluation/dataset_info.json filter=lfs diff=lfs merge=lfs -text
+cite_count/train/dataset_info.json filter=lfs diff=lfs merge=lfs -text
+peer_review_score_hIndex/evaluation/state.json filter=lfs diff=lfs merge=lfs -text
+pub_year/evaluation/dataset_info.json filter=lfs diff=lfs merge=lfs -text
+search/evaluation/state.json filter=lfs diff=lfs merge=lfs -text
+search/train/dataset_info.json filter=lfs diff=lfs merge=lfs -text
+peer_review_score_hIndex/evaluation/dataset_info.json filter=lfs diff=lfs merge=lfs -text
+pub_year/train/state.json filter=lfs diff=lfs merge=lfs -text
+cite_count/train/state.json filter=lfs diff=lfs merge=lfs -text
+cite_count/validation/dataset_info.json filter=lfs diff=lfs merge=lfs -text
+feeds_1/evaluation/dataset_info.json filter=lfs diff=lfs merge=lfs -text
+feeds_title/evaluation/dataset_info.json filter=lfs diff=lfs merge=lfs -text
+fos/evaluation/dataset_info.json filter=lfs diff=lfs merge=lfs -text
+cite_count/evaluation/dataset_info.json filter=lfs diff=lfs merge=lfs -text
+cite_count/evaluation/state.json filter=lfs diff=lfs merge=lfs -text
+paper_reviewer_matching/evaluation/state.json filter=lfs diff=lfs merge=lfs -text
+scidocs_view_cite_read/evaluation/state.json filter=lfs diff=lfs merge=lfs -text
+scidocs_view_cite_read/evaluation/dataset_info.json filter=lfs diff=lfs merge=lfs -text
+search/evaluation/dataset_info.json filter=lfs diff=lfs merge=lfs -text

super_scirep.py

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+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# TODO: Address all TODOs and remove all explanatory comments
+"""TODO: Add a description here."""
+
+import csv
+import json
+
+import datasets
+from datasets.data_files import DataFilesDict
+from .super_scirep_config import SUPERSCIREPEVAL_CONFIGS
+
+# from datasets.packaged_modules.json import json
+
+
+# TODO: Add BibTeX citation
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@InProceedings{huggingface:dataset,
+title = {A great new dataset},
+author={huggingface, Inc.
+},
+year={2021}
+}
+"""
+
+# TODO: Add description of the dataset here
+# You can copy an official description
+_DESCRIPTION = """\
+This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
+"""
+
+# TODO: Add a link to an official homepage for the dataset here
+_HOMEPAGE = ""
+
+# TODO: Add the licence for the dataset here if you can find it
+_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 = {
+    "first_domain": "https://huggingface.co/great-new-dataset-first_domain.zip",
+    "second_domain": "https://huggingface.co/great-new-dataset-second_domain.zip",
+}
+
+
+# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
+class SuperSciRep(datasets.GeneratorBasedBuilder):
+    """TODO: Short description of my dataset."""
+
+    VERSION = datasets.Version("1.1.0")
+
+    # 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 = SUPERSCIREPEVAL_CONFIGS
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=self.config.description,
+            # This defines the different columns of the dataset and their types
+            features=datasets.Features(self.config.features),
+            # Here we define them above because they are different between the two configurations
+            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
+            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
+            # supervised_keys=("sentence", "label"),
+            # Homepage of the dataset for documentation
+            homepage="",
+            # License for the dataset if available
+            license=self.config.license,
+            # Citation for the dataset
+            citation=self.config.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
+        # base_url = "https://ai2-s2-research-public.s3.us-west-2.amazonaws.com/scirepeval"
+        base_url = "https://hhy-tue.s3.eu-central-1.amazonaws.com/data/super_scirep"
+        data_urls = dict()
+        # data_dir = self.config.url if self.config.url else self.config.name
+        data_dir = self.config.name
+        if self.config.is_training:
+            data_urls = {"train": f"{base_url}/{data_dir}/train.jsonl",
+                         "val": f"{base_url}/{data_dir}/validation.jsonl"}
+
+        if "cite_prediction" not in self.config.name:
+            data_urls.update({"test": f"{base_url}/{data_dir}/evaluation.jsonl"})
+        print(data_urls)
+        downloaded_files = dl_manager.download_and_extract(data_urls)
+        splits = []
+        if "test" in downloaded_files:
+            splits = [datasets.SplitGenerator(
+                name=datasets.Split("evaluation"),
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": downloaded_files["test"],
+                    "split": "evaluation"
+                },
+            ),
+            ]
+
+        if "train" in downloaded_files:
+            splits += [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    # These kwargs will be passed to _generate_examples
+                    gen_kwargs={
+                        "filepath": downloaded_files["train"],
+                        "split": "train",
+                    },
+                ),
+                datasets.SplitGenerator(
+                    name=datasets.Split.VALIDATION,
+                    # These kwargs will be passed to _generate_examples
+                    gen_kwargs={
+                        "filepath": downloaded_files["val"],
+                        "split": "validation",
+                    })
+            ]
+        return splits
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, filepath, split):
+        def read_data(data_path):
+            task_data = []
+            try:
+                task_data = json.load(open(data_path, "r", encoding="utf-8"))
+            except:
+                with open(data_path) as f:
+                    task_data = [json.loads(line) for line in f]
+            if type(task_data) == dict:
+                task_data = list(task_data.values())
+            return task_data
+
+        # 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.
+        # data = read_data(filepath)
+        seen_keys = set()
+        IGNORE = set(["n_key_citations", "session_id", "user_id", "user"])
+        with open(filepath, encoding="utf-8") as f:
+            for line in f:
+                d = json.loads(line)
+                d = {k: v for k, v in d.items() if k not in IGNORE}
+                key = "doc_id" if self.config.name != "cite_prediction_new" else "corpus_id"
+                if self.config.task_type == "proximity":
+                    if "cite_prediction" in self.config.name:
+                        if "arxiv_id" in d["query"]:
+                            for item in ["query", "pos", "neg"]:
+                                del d[item]["arxiv_id"]
+                                del d[item]["doi"]
+                        if "fos" in d["query"]:
+                            del d["query"]["fos"]
+                        if "score" in d["pos"]:
+                            del d["pos"]["score"]
+                        yield str(d["query"][key]) + str(d["pos"][key]) + str(d["neg"][key]), d
+                    else:
+                        if d["query"][key] not in seen_keys:
+                            seen_keys.add(d["query"][key])
+                            yield str(d["query"][key]), d
+                else:
+                    if d[key] not in seen_keys:
+                        seen_keys.add(d[key])
+                        if self.config.task_type != "search":
+                            if "corpus_id" not in d:
+                                d["corpus_id"] = None
+                            if "scidocs" in self.config.name:
+                                if "cited by" not in d:
+                                    d["cited_by"] = []
+                                if type(d["corpus_id"]) == str:
+                                    d["corpus_id"] = None
+                        yield d[key], d

super_scirep_config.py

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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}}")
+
+]