new version

README.md

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+---
+dataset_info:
+- config_name: fos
+  features:
+  - name: doc_id
+    dtype: string
+  - name: corpus_id
+    dtype: uint64
+  - name: title
+    dtype: string
+  - name: abstract
+    dtype: string
+  - name: labels
+    sequence: int32
+  - name: labels_text
+    sequence: string
+  splits:
+  - name: evaluation
+    num_bytes: 63854253
+    num_examples: 68147
+  - name: train
+    num_bytes: 509154623
+    num_examples: 541218
+  - name: validation
+    num_bytes: 63947785
+    num_examples: 67631
+  download_size: 683428084
+  dataset_size: 636956661
+- config_name: mesh_descriptors
+  features:
+  - name: doc_id
+    dtype: string
+  - name: mag_id
+    dtype: uint64
+  - name: corpus_id
+    dtype: uint64
+  - name: title
+    dtype: string
+  - name: abstract
+    dtype: string
+  - name: descriptor
+    dtype: string
+  - name: qualifier
+    dtype: string
+  splits:
+  - name: evaluation
+    num_bytes: 390178523
+    num_examples: 258678
+  - name: train
+    num_bytes: 3120117992
+    num_examples: 2069065
+  - name: validation
+    num_bytes: 390161743
+    num_examples: 258678
+  download_size: 4132614464
+  dataset_size: 3900458258
+- config_name: cite_count
+  features:
+  - name: doc_id
+    dtype: string
+  - name: corpus_id
+    dtype: uint64
+  - name: title
+    dtype: string
+  - name: abstract
+    dtype: string
+  - name: venue
+    dtype: string
+  - name: n_citations
+    dtype: int32
+  - name: log_citations
+    dtype: float32
+  splits:
+  - name: evaluation
+    num_bytes: 45741032
+    num_examples: 30058
+  - name: train
+    num_bytes: 265390284
+    num_examples: 175944
+  - name: validation
+    num_bytes: 40997159
+    num_examples: 26830
+  download_size: 378454118
+  dataset_size: 352128475
+- config_name: pub_year
+  features:
+  - name: doc_id
+    dtype: string
+  - name: corpus_id
+    dtype: uint64
+  - name: title
+    dtype: string
+  - name: abstract
+    dtype: string
+  - name: year
+    dtype: int32
+  - name: venue
+    dtype: string
+  - name: norm_year
+    dtype: float32
+  - name: scaled_year
+    dtype: float32
+  - name: n_authors
+    dtype: int32
+  - name: norm_authors
+    dtype: float32
+  splits:
+  - name: evaluation
+    num_bytes: 46195045
+    num_examples: 30000
+  - name: train
+    num_bytes: 301313882
+    num_examples: 198995
+  - name: validation
+    num_bytes: 30493617
+    num_examples: 19869
+  download_size: 411086891
+  dataset_size: 378002544
+- config_name: cite_prediction
+  features:
+  - name: query
+    struct:
+    - name: doc_id
+      dtype: string
+    - name: title
+      dtype: string
+    - name: abstract
+      dtype: string
+    - name: sha
+      dtype: string
+    - name: corpus_id
+      dtype: uint64
+  - name: pos
+    struct:
+    - name: doc_id
+      dtype: string
+    - name: title
+      dtype: string
+    - name: abstract
+      dtype: string
+    - name: sha
+      dtype: string
+    - name: corpus_id
+      dtype: uint64
+  - name: neg
+    struct:
+    - name: doc_id
+      dtype: string
+    - name: title
+      dtype: string
+    - name: abstract
+      dtype: string
+    - name: sha
+      dtype: string
+    - name: corpus_id
+      dtype: uint64
+  splits:
+  - name: train
+    num_bytes: 2582594392
+    num_examples: 676150
+  - name: validation
+    num_bytes: 549599739
+    num_examples: 143686
+  download_size: 3287219740
+  dataset_size: 3132194131
+- config_name: cite_prediction_new
+  features:
+  - name: query
+    struct:
+    - name: title
+      dtype: string
+    - name: abstract
+      dtype: string
+    - name: corpus_id
+      dtype: uint64
+  - name: pos
+    struct:
+    - name: title
+      dtype: string
+    - name: abstract
+      dtype: string
+    - name: corpus_id
+      dtype: uint64
+  - name: neg
+    struct:
+    - name: title
+      dtype: string
+    - name: abstract
+      dtype: string
+    - name: corpus_id
+      dtype: uint64
+    - name: score
+      dtype: int8
+  splits:
+  - name: train
+    num_bytes: 23829782726
+    num_examples: 6197963
+  - name: validation
+    num_bytes: 609822308
+    num_examples: 176430
+  download_size: 25842249246
+  dataset_size: 24439605034
+- config_name: cite_prediction_aug2023refresh
+  features:
+  - name: query
+    struct:
+    - name: title
+      dtype: string
+    - name: abstract
+      dtype: string
+    - name: corpus_id
+      dtype: uint64
+  - name: pos
+    struct:
+    - name: title
+      dtype: string
+    - name: abstract
+      dtype: string
+    - name: corpus_id
+      dtype: uint64
+  - name: neg
+    struct:
+    - name: title
+      dtype: string
+    - name: abstract
+      dtype: string
+    - name: corpus_id
+      dtype: uint64
+  splits:
+  - name: train
+    num_bytes: 2069439948
+    num_examples: 475656
+  download_size: 2147428459
+  dataset_size: 2069439948
+- config_name: high_influence_cite
+  features:
+  - name: query
+    struct:
+    - name: doc_id
+      dtype: string
+    - name: title
+      dtype: string
+    - name: abstract
+      dtype: string
+    - name: corpus_id
+      dtype: uint64
+  - name: candidates
+    list:
+    - name: doc_id
+      dtype: string
+    - name: title
+      dtype: string
+    - name: abstract
+      dtype: string
+    - name: corpus_id
+      dtype: uint64
+    - name: score
+      dtype: uint32
+  splits:
+  - name: evaluation
+    num_bytes: 85746699
+    num_examples: 1199
+  - name: train
+    num_bytes: 2607643584
+    num_examples: 58626
+  - name: validation
+    num_bytes: 329589399
+    num_examples: 7356
+  download_size: 3149789722
+  dataset_size: 3022979682
+- config_name: same_author
+  features:
+  - name: dataset
+    dtype: string
+  - name: query
+    struct:
+    - name: doc_id
+      dtype: string
+    - name: title
+      dtype: string
+    - name: abstract
+      dtype: string
+    - name: corpus_id
+      dtype: uint64
+  - name: candidates
+    list:
+    - name: doc_id
+      dtype: string
+    - name: title
+      dtype: string
+    - name: abstract
+      dtype: string
+    - name: corpus_id
+      dtype: uint64
+    - name: score
+      dtype: uint32
+  splits:
+  - name: evaluation
+    num_bytes: 126843751
+    num_examples: 13585
+  - name: train
+    num_bytes: 602167355
+    num_examples: 67493
+  - name: validation
+    num_bytes: 84426970
+    num_examples: 8996
+  download_size: 866210529
+  dataset_size: 813438076
+- config_name: search
+  features:
+  - name: query
+    dtype: string
+  - name: doc_id
+    dtype: string
+  - name: candidates
+    list:
+    - name: doc_id
+      dtype: string
+    - name: title
+      dtype: string
+    - name: abstract
+      dtype: string
+    - name: corpus_id
+      dtype: uint64
+    - name: venue
+      dtype: string
+    - name: year
+      dtype: float64
+    - name: author_names
+      sequence: string
+    - name: n_citations
+      dtype: int32
+    - name: n_key_citations
+      dtype: int32
+    - name: score
+      dtype: uint32
+  splits:
+  - name: evaluation
+    num_bytes: 39417912
+    num_examples: 2637
+  - name: train
+    num_bytes: 6889691036
+    num_examples: 399878
+  - name: validation
+    num_bytes: 1096150259
+    num_examples: 67363
+  download_size: 9645282078
+  dataset_size: 8025259207
+- config_name: biomimicry
+  features:
+  - name: doc_id
+    dtype: string
+  - name: doi
+    dtype: string
+  - name: corpus_id
+    dtype: uint64
+  - name: title
+    dtype: string
+  - name: abstract
+    dtype: string
+  - name: label
+    dtype: uint32
+  - name: venue
+    dtype: string
+  splits:
+  - name: evaluation
+    num_bytes: 16651415
+    num_examples: 10991
+  download_size: 17437012
+  dataset_size: 16651415
+- config_name: drsm
+  features:
+  - name: doc_id
+    dtype: string
+  - name: corpus_id
+    dtype: uint64
+  - name: title
+    dtype: string
+  - name: abstract
+    dtype: string
+  - name: label_type
+    dtype: string
+  - name: label
+    dtype: string
+  - name: class
+    dtype: uint32
+  splits:
+  - name: evaluation
+    num_bytes: 12756487
+    num_examples: 8813
+  download_size: 13449713
+  dataset_size: 12756487
+- config_name: peer_review_score_hIndex
+  features:
+  - name: doc_id
+    dtype: string
+  - name: corpus_id
+    dtype: uint64
+  - name: title
+    dtype: string
+  - name: abstract
+    dtype: string
+  - name: rating
+    sequence: int32
+  - name: confidence
+    dtype: string
+  - name: authors
+    sequence: string
+  - name: decision
+    dtype: string
+  - name: mean_rating
+    dtype: float32
+  - name: hIndex
+    sequence: string
+  splits:
+  - name: evaluation
+    num_bytes: 18233728
+    num_examples: 12668
+  download_size: 19647506
+  dataset_size: 18233728
+- config_name: trec_covid
+  features:
+  - name: query
+    dtype: string
+  - name: doc_id
+    dtype: string
+  - name: candidates
+    list:
+    - name: title
+      dtype: string
+    - name: abstract
+      dtype: string
+    - name: corpus_id
+      dtype: string
+    - name: doc_id
+      dtype: string
+    - name: date
+      dtype: string
+    - name: doi
+      dtype: string
+    - name: iteration
+      dtype: string
+    - name: score
+      dtype: int32
+  splits:
+  - name: evaluation
+    num_bytes: 98757931
+    num_examples: 50
+  download_size: 104449690
+  dataset_size: 98757931
+- config_name: tweet_mentions
+  features:
+  - name: doc_id
+    dtype: string
+  - name: corpus_id
+    dtype: uint64
+  - name: title
+    dtype: string
+  - name: abstract
+    dtype: string
+  - name: index
+    dtype: int32
+  - name: retweets
+    dtype: float32
+  - name: count
+    dtype: int32
+  - name: mentions
+    dtype: float32
+  splits:
+  - name: evaluation
+    num_bytes: 25895172
+    num_examples: 25655
+  download_size: 28533162
+  dataset_size: 25895172
+- config_name: scidocs_mag_mesh
+  features:
+  - name: doc_id
+    dtype: string
+  - name: corpus_id
+    dtype: uint64
+  - name: title
+    dtype: string
+  - name: abstract
+    dtype: string
+  - name: authors
+    sequence: string
+  - name: cited_by
+    sequence: string
+  - name: references
+    sequence: string
+  - name: year
+    dtype: int32
+  splits:
+  - name: evaluation
+    num_bytes: 74027498
+    num_examples: 48473
+  download_size: 109426986
+  dataset_size: 74027498
+- config_name: scidocs_view_cite_read
+  features:
+  - name: doc_id
+    dtype: string
+  - name: corpus_id
+    dtype: uint64
+  - name: title
+    dtype: string
+  - name: abstract
+    dtype: string
+  - name: authors
+    sequence: string
+  - name: cited_by
+    sequence: string
+  - name: references
+    sequence: string
+  - name: year
+    dtype: int32
+  splits:
+  - name: evaluation
+    num_bytes: 240557104
+    num_examples: 142009
+  download_size: 949184683
+  dataset_size: 240557104
+- config_name: paper_reviewer_matching
+  features:
+  - name: doc_id
+    dtype: string
+  - name: title
+    dtype: string
+  - name: abstract
+    dtype: string
+  - name: corpus_id
+    dtype: uint64
+  splits:
+  - name: evaluation
+    num_bytes: 76005931
+    num_examples: 73364
+  download_size: 88124286
+  dataset_size: 76005931
+- config_name: relish
+  features:
+  - name: query
+    struct:
+    - name: doc_id
+      dtype: string
+    - name: title
+      dtype: string
+    - name: abstract
+      dtype: string
+    - name: corpus_id
+      dtype: int64
+  - name: candidates
+    list:
+    - name: doc_id
+      dtype: string
+    - name: title
+      dtype: string
+    - name: abstract
+      dtype: string
+    - name: corpus_id
+      dtype: int64
+    - name: score
+      dtype: uint32
+  splits:
+  - name: evaluation
+    num_bytes: 338282942
+    num_examples: 3190
+  download_size: 350349387
+  dataset_size: 338282942
+- config_name: nfcorpus
+  features:
+  - name: query
+    dtype: string
+  - name: doc_id
+    dtype: string
+  - name: candidates
+    list:
+    - name: doc_id
+      dtype: string
+    - name: title
+      dtype: string
+    - name: abstract
+      dtype: string
+    - name: score
+      dtype: uint32
+  splits:
+  - name: evaluation
+    num_bytes: 72184049
+    num_examples: 323
+  download_size: 74298528
+  dataset_size: 72184049
+---

scirepeval.py

@@ -0,0 +1,199 @@
+# 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 os
+import glob
+
+import datasets
+from datasets.data_files import DataFilesDict
+from .scirepeval_configs import SCIREPEVAL_CONFIGS
+#from datasets.packaged_modules.json import json
+from datasets.utils.logging import get_logger
+
+
+logger = get_logger(__name__)
+# 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 Scirepeval(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 = SCIREPEVAL_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"
+        data_urls = dict()
+        data_dir = self.config.url if self.config.url else self.config.name
+        if self.config.is_training:
+            data_urls = {"train": f"{base_url}/train/{data_dir}/train.jsonl"}
+        
+            if "refresh" not in self.config.name:
+                data_urls.update({"val": f"{base_url}/train/{data_dir}/val.jsonl"})
+        
+        if "cite_prediction" not in self.config.name:
+            data_urls.update({"test": f"{base_url}/test/{data_dir}/meta.jsonl"})
+        # print(data_urls)
+        downloaded_files = dl_manager.download_and_extract(data_urls)
+        # print(downloaded_files)
+        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.append(
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    # These kwargs will be passed to _generate_examples
+                    gen_kwargs={
+                        "filepath": downloaded_files["train"],
+                        "split": "train",
+                    },
+                ))
+        if "val" in downloaded_files:
+            splits.append(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"])
+        logger.warning(filepath)
+        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 "cite_prediction_" not in self.config.name 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

scirepeval_configs.py

@@ -0,0 +1,359 @@
+from typing import Dict, Any, List
+
+import datasets
+
+
+class ScirepevalConfig(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]:
+        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
+
+
+SCIREPEVAL_CONFIGS = [
+    ScirepevalConfig(name="fos", features=ScirepevalConfig.get_features(
+        ["doc_id", "corpus_id", "title", "abstract", "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=""),
+
+    ScirepevalConfig(name="mesh_descriptors", features=ScirepevalConfig.get_features(
+        ["doc_id", "mag_id", "corpus_id", "title", "abstract", "descriptor", "qualifier"], {"mag_id": datasets.Value("uint64")}),
+                     task_type="classification", is_training=True,
+                     citation="@article{Lipscomb2000MedicalSH, \
+                              title={Medical Subject Headings (MeSH).}, \
+                              author={Carolyn E. Lipscomb}, \
+                              journal={Bulletin of the Medical Library Association},\
+                              year={2000}, \
+                              volume={88 3}, \
+                              pages={ \
+                                      265-6 \
+                                    } \
+                            }",
+                     description="", homepage="https://www.nlm.nih.gov/databases/download/mesh.html"
+                     ),
+
+    ScirepevalConfig(name="cite_count", features=ScirepevalConfig.get_features(
+        ["doc_id", "corpus_id", "title", "abstract", "venue", "n_citations", "log_citations"],
+        {"n_citations": datasets.Value("int32"),
+         "log_citations": datasets.Value("float32")}),
+                     task_type="regression", is_training=True, description=""
+                     ),
+
+    ScirepevalConfig(name="pub_year", features=ScirepevalConfig.get_features(
+        ["doc_id", "corpus_id", "title", "abstract", "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=""),
+
+    ScirepevalConfig(name="cite_prediction",
+                     features=ScirepevalConfig.get_features(["query", "pos", "neg"],
+                                                            {"query": {
+                                                                "doc_id": datasets.Value("string"),
+                                                                "title": datasets.Value("string"),
+                                                                "abstract": datasets.Value(
+                                                                    "string"),
+                                                                "sha": datasets.Value("string"),
+                                                                "corpus_id": datasets.Value("uint64")},
+                                                                "pos": {
+                                                                    "doc_id": datasets.Value("string"),
+                                                                    "title": datasets.Value("string"),
+                                                                    "abstract": datasets.Value(
+                                                                        "string"),
+                                                                    "sha": datasets.Value("string"),
+                                                                    "corpus_id": datasets.Value("uint64")}
+                                                                , "neg": {
+                                                                    "doc_id": datasets.Value("string"),
+                                                                "title": datasets.Value("string"),
+                                                                "abstract": datasets.Value(
+                                                                    "string"),
+                                                                "sha": datasets.Value("string"),
+                                                                "corpus_id": datasets.Value("uint64")}}),
+                     task_type="proximity", is_training=True, 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} \
+                            }", description="", homepage="https://github.com/allenai/specter"),
+    ScirepevalConfig(name="cite_prediction_new",
+                     features=ScirepevalConfig.get_features(["query", "pos", "neg"],
+                                                            {"query": {
+                                                                "title": datasets.Value("string"),
+                                                                "abstract": datasets.Value(
+                                                                    "string"),
+                                                                "corpus_id": datasets.Value("uint64")},
+                                                                "pos": {
+                                                                    "title": datasets.Value("string"),
+                                                                    "abstract": datasets.Value(
+                                                                        "string"),
+                                                                    "corpus_id": datasets.Value("uint64"),
+                                                                }
+                                                                , "neg": {
+                                                                "title": datasets.Value("string"),
+                                                                "abstract": datasets.Value(
+                                                                    "string"),
+                                                                "corpus_id": datasets.Value("uint64"),
+                                                                "score": datasets.Value("int8")}}),
+                     task_type="proximity", is_training=True, 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} \
+                            }", description="", homepage="https://github.com/allenai/specter"),
+    ScirepevalConfig(name="cite_prediction_aug2023refresh",
+                     features=ScirepevalConfig.get_features(["query", "pos", "neg"],
+                                                            {"query": {
+                                                                "title": datasets.Value("string"),
+                                                                "abstract": datasets.Value(
+                                                                    "string"),
+                                                                "corpus_id": datasets.Value("uint64")},
+                                                                "pos": {
+                                                                    "title": datasets.Value("string"),
+                                                                    "abstract": datasets.Value(
+                                                                        "string"),
+                                                                    "corpus_id": datasets.Value("uint64"),
+                                                                }
+                                                                , "neg": {
+                                                                "title": datasets.Value("string"),
+                                                                "abstract": datasets.Value(
+                                                                    "string"),
+                                                                "corpus_id": datasets.Value("uint64")}}),
+                     task_type="proximity", is_training=True, 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} \
+                            }", description="", homepage="https://github.com/allenai/specter"),
+
+    ScirepevalConfig(name="high_influence_cite",
+                     features=ScirepevalConfig.get_features(["query", "candidates"],
+                                                            {"query": {
+                                                                "doc_id": datasets.Value("string"),
+                                                                "title": datasets.Value("string"),
+                                                                "abstract": datasets.Value(
+                                                                    "string"),
+                                                                "corpus_id": datasets.Value("uint64")},
+                                                                "candidates":
+                                                                    [{"doc_id": datasets.Value("string"),
+                                                                        "title": datasets.Value("string"),
+                                                                        "abstract": datasets.Value(
+                                                                            "string"),
+                                                                        "corpus_id": datasets.Value("uint64"),
+                                                                        "score": datasets.Value("uint32")}]}),
+                     task_type="proximity", is_training=True, description=""),
+
+    ScirepevalConfig(name="same_author",
+                     features=ScirepevalConfig.get_features(["dataset", "query", "candidates"],
+                                                            {"query": {
+                                                                "doc_id": datasets.Value("string"),
+                                                                "title": datasets.Value("string"),
+                                                                "abstract": datasets.Value(
+                                                                    "string"),
+                                                                "corpus_id": datasets.Value("uint64")},
+                                                                "candidates":
+                                                                    [{
+                                                                        "doc_id": datasets.Value("string"),
+                                                                        "title": datasets.Value("string"),
+                                                                        "abstract": datasets.Value(
+                                                                            "string"),
+                                                                        "corpus_id": datasets.Value("uint64"),
+                                                                        "score": datasets.Value("uint32")}]}),
+                     task_type="proximity", is_training=True, description=""),
+
+    ScirepevalConfig(name="search",
+                     features=ScirepevalConfig.get_features(["query", "doc_id", "candidates"],
+                                                            {"candidates":
+                                                                [{
+                                                                    "doc_id": datasets.Value("string"),
+                                                                    "title": datasets.Value("string"),
+                                                                    "abstract": 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=""),
+
+    ScirepevalConfig(name="biomimicry", features=ScirepevalConfig.get_features(
+        ["doc_id", "doi", "corpus_id", "title", "abstract", "label", "venue"], {"label": datasets.Value("uint32")}),
+                     task_type="classification",
+                     citation="@Article{vikram2019petal,\
+AUTHOR = {Shyam, Vikram and Friend, Lauren and Whiteaker, Brian and Bense, Nicholas and Dowdall, Jonathan and Boktor, Bishoy and Johny, Manju and Reyes, Isaias and Naser, Angeera and Sakhamuri, Nikhitha and Kravets, Victoria and Calvin, Alexandra and Gabus, Kaylee and Goodman, Delonte and Schilling, Herbert and Robinson, Calvin and Reid II, Robert Omar and Unsworth, Colleen},\
+TITLE = {PeTaL (Periodic Table of Life) and Physiomimetics},\
+JOURNAL = {Designs},\
+VOLUME = {3},\
+YEAR = {2019},\
+NUMBER = {3},\
+ARTICLE-NUMBER = {43},\
+URL = {https://www.mdpi.com/2411-9660/3/3/43},\
+ISSN = {2411-9660},\
+ABSTRACT = {The Periodic Table of Life (PeTaL) is a system design tool and open source framework that uses artificial intelligence (AI) to aid in the systematic inquiry of nature for its application to human systems. This paper defines PeTaL’s architecture and workflow. Biomimicry, biophysics, biomimetics, bionics and numerous other terms refer to the use of biology and biological principles to inform practices in other disciplines. For the most part, the domain of inquiry in these fields has been confined to extant biological models with the proponents of biomimicry often citing the evolutionary success of extant organisms relative to extinct ones. An objective of this paper is to expand the domain of inquiry for human processes that seek to model those that are, were or could be found in nature with examples that relate to the field of aerospace and to spur development of tools that can work together to accelerate the use of artificial intelligence, topology optimization and conventional modeling in problem solving. Specifically, specialized fields such as paleomimesis, anthropomimesis and physioteleology are proposed in conjunction with artificial evolution. The overarching philosophy outlined here can be thought of as physiomimetics, a holistic and systematic way of learning from natural history. The backbone of PeTaL integrates an unstructured database with an ontological model consisting of function, morphology, environment, state of matter and ecosystem. Tools that support PeTaL include machine learning, natural language processing and computer vision. Applications of PeTaL include guiding human space exploration, understanding human and geological history, and discovering new or extinct life. Also discussed is the formation of V.I.N.E. (Virtual Interchange for Nature-inspired Exploration), a virtual collaborative aimed at generating data, research and applications centered on nature. Details of implementation will be presented in subsequent publications. Recommendations for future work are also presented.},\
+DOI = {10.3390/designs3030043}\
+}",
+                     description="",
+                     homepage="https://github.com/nasa-petal/PeTaL-db"
+                     ),
+
+    ScirepevalConfig(name="drsm", features=ScirepevalConfig.get_features(
+        ["doc_id", "corpus_id", "title", "abstract", "label_type", "label", "class"],
+        {"class": datasets.Value("uint32")}),
+                     task_type="classification", description="",
+                     homepage="https://github.com/chanzuckerberg/DRSM-corpus"
+                     ),
+
+    ScirepevalConfig(name="relish",
+                     features=ScirepevalConfig.get_features(["query", "candidates"],
+                                                            {"query": {
+                                                                "doc_id": datasets.Value("string"),
+                                                                "title": datasets.Value("string"),
+                                                                "abstract": datasets.Value(
+                                                                    "string"),
+                                                                "corpus_id": datasets.Value("int64")},
+                                                                "candidates":
+                                                                    [{
+                                                                        "doc_id": datasets.Value("string"),
+                                                                        "title": datasets.Value("string"),
+                                                                        "abstract": datasets.Value(
+                                                                            "string"),
+                                                                        "corpus_id": datasets.Value("int64"),
+                                                                        "score": datasets.Value("uint32")}]}),
+                     task_type="proximity", description=""),
+
+    ScirepevalConfig(name="nfcorpus",
+                     features=ScirepevalConfig.get_features(["query", "doc_id", "candidates"],
+                                                            {"candidates":
+                                                                [{
+                                                                    "doc_id": datasets.Value("string"),
+                                                                    "title": datasets.Value("string"),
+                                                                    "abstract": datasets.Value(
+                                                                        "string"),
+                                                                    "score": datasets.Value("uint32")}]}),
+                     task_type="search", description=""),
+
+    ScirepevalConfig(name="peer_review_score_hIndex", features=ScirepevalConfig.get_features(
+        ["doc_id", "corpus_id", "title", "abstract", "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=""
+                     ),
+
+    ScirepevalConfig(name="trec_covid",
+                     features=ScirepevalConfig.get_features(["query", "doc_id", "candidates"],
+                                                            {"candidates":
+                                                                [{
+                                                                    "title": datasets.Value("string"),
+                                                                    "abstract": datasets.Value(
+                                                                        "string"),
+                                                                    "corpus_id": datasets.Value("string"),
+                                                                    "doc_id": datasets.Value("string"),
+                                                                    "date": datasets.Value("string"),
+                                                                    "doi": datasets.Value("string"),
+                                                                    "iteration": datasets.Value("string"),
+                                                                    "score": datasets.Value("int32")}]}),
+                     task_type="search", description="", homepage="https://ir.nist.gov/trec-covid/", citation="@article{Voorhees2020TRECCOVIDCA,\
+  title={TREC-COVID: Constructing a Pandemic Information Retrieval Test Collection},\
+  author={Ellen M. Voorhees and Tasmeer Alam and Steven Bedrick and Dina Demner-Fushman and William R. Hersh and Kyle Lo and Kirk Roberts and Ian Soboroff and Lucy Lu Wang},\
+  journal={ArXiv},\
+  year={2020},\
+  volume={abs/2005.04474}\
+}"),
+
+    ScirepevalConfig(name="tweet_mentions", features=ScirepevalConfig.get_features(
+        ["doc_id", "corpus_id", "title", "abstract", "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}\
+}"),
+
+    ScirepevalConfig(name="scidocs_mag_mesh", features=ScirepevalConfig.get_features(
+        ["doc_id", "corpus_id", "title", "abstract", "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="classification ", description="", url="scidocs/mag_mesh",
+                     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}\
+}"),
+
+    ScirepevalConfig(name="scidocs_view_cite_read", features=ScirepevalConfig.get_features(
+        ["doc_id", "corpus_id", "title", "abstract", "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}\
+}"),
+
+    ScirepevalConfig(name="paper_reviewer_matching", features=ScirepevalConfig.get_features(
+        ["doc_id", "title", "abstract", "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}}")
+
+]