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README.md

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+---
+dataset_info:
+- config_name: fos
+  features:
+  - name: paper_id
+    dtype: string
+  - name: label
+    sequence: int32
+  splits:
+  - name: test
+    num_bytes: 51276
+    num_examples: 472
+  - name: train
+    num_bytes: 5873604
+    num_examples: 54131
+  download_size: 3194762
+  dataset_size: 5924880
+- config_name: mesh_descriptors
+  features:
+  - name: paper_id
+    dtype: string
+  - name: label
+    dtype: int32
+  splits:
+  - name: test
+    num_bytes: 820660
+    num_examples: 51738
+  - name: train
+    num_bytes: 3283053
+    num_examples: 206949
+  download_size: 3203144
+  dataset_size: 4103713
+- config_name: cite_count
+  features:
+  - name: paper_id
+    dtype: string
+  - name: label
+    dtype: float64
+  splits:
+  - name: test
+    num_bytes: 121260
+    num_examples: 6012
+  - name: train
+    num_bytes: 483822
+    num_examples: 24000
+  download_size: 477603
+  dataset_size: 605082
+- config_name: pub_year
+  features:
+  - name: paper_id
+    dtype: string
+  - name: label
+    dtype: float64
+  splits:
+  - name: test
+    num_bytes: 123284
+    num_examples: 6000
+  - name: train
+    num_bytes: 493073
+    num_examples: 24000
+  download_size: 518506
+  dataset_size: 616357
+- config_name: high_influence_cite
+  features:
+  - name: query_id
+    dtype: string
+  - name: cand_id
+    dtype: string
+  - name: score
+    dtype: uint8
+  splits:
+  - name: test
+    num_bytes: 1439013
+    num_examples: 58255
+  download_size: 3477938
+  dataset_size: 1439013
+- config_name: same_author
+  features:
+  - name: query_id
+    dtype: string
+  - name: cand_id
+    dtype: string
+  - name: score
+    dtype: uint8
+  splits:
+  - name: test
+    num_bytes: 3144107
+    num_examples: 123430
+  download_size: 7464157
+  dataset_size: 3144107
+- config_name: search
+  features:
+  - name: query_id
+    dtype: string
+  - name: cand_id
+    dtype: string
+  - name: score
+    dtype: uint8
+  splits:
+  - name: test
+    num_bytes: 1283980
+    num_examples: 25850
+  download_size: 2188731
+  dataset_size: 1283980
+- config_name: drsm
+  features:
+  - name: paper_id
+    dtype: string
+  - name: label
+    dtype: int32
+  splits:
+  - name: test
+    num_bytes: 15277
+    num_examples: 955
+  - name: train
+    num_bytes: 119083
+    num_examples: 7520
+  download_size: 100492
+  dataset_size: 134360
+- config_name: feeds_1
+  features:
+  - name: query_id
+    dtype: string
+  - name: cand_id
+    dtype: string
+  - name: score
+    dtype: uint8
+  splits:
+  - name: test
+    num_bytes: 110997
+    num_examples: 4223
+  download_size: 258802
+  dataset_size: 110997
+- config_name: feeds_m
+  features:
+  - name: query_id
+    dtype: string
+  - name: cand_id
+    dtype: string
+  - name: score
+    dtype: uint8
+  splits:
+  - name: test
+    num_bytes: 2321483
+    num_examples: 87528
+  download_size: 5384963
+  dataset_size: 2321483
+- config_name: feeds_title
+  features:
+  - name: query_id
+    dtype: string
+  - name: cand_id
+    dtype: string
+  - name: score
+    dtype: uint8
+  splits:
+  - name: test
+    num_bytes: 210605
+    num_examples: 4233
+  download_size: 358760
+  dataset_size: 210605
+- config_name: peer_review_score
+  features:
+  - name: paper_id
+    dtype: string
+  - name: label
+    dtype: float64
+  splits:
+  - name: test
+    num_bytes: 89892
+    num_examples: 2043
+  - name: train
+    num_bytes: 359348
+    num_examples: 8167
+  download_size: 408432
+  dataset_size: 449240
+- config_name: hIndex
+  features:
+  - name: paper_id
+    dtype: string
+  - name: label
+    dtype: float64
+  splits:
+  - name: test
+    num_bytes: 94864
+    num_examples: 2156
+  - name: train
+    num_bytes: 382756
+    num_examples: 8699
+  download_size: 434232
+  dataset_size: 477620
+- config_name: trec_covid
+  features:
+  - name: query_id
+    dtype: string
+  - name: cand_id
+    dtype: string
+  - name: score
+    dtype: int8
+  splits:
+  - name: test
+    num_bytes: 3396582
+    num_examples: 69318
+  download_size: 5822714
+  dataset_size: 3396582
+- config_name: tweet_mentions
+  features:
+  - name: paper_id
+    dtype: string
+  - name: label
+    dtype: float64
+  splits:
+  - name: test
+    num_bytes: 111212
+    num_examples: 5132
+  - name: train
+    num_bytes: 444784
+    num_examples: 20523
+  download_size: 454231
+  dataset_size: 555996
+- config_name: scidocs_mag
+  features:
+  - name: paper_id
+    dtype: string
+  - name: label
+    dtype: int32
+  splits:
+  - name: test
+    num_bytes: 180048
+    num_examples: 3751
+  - name: train
+    num_bytes: 840048
+    num_examples: 17501
+  download_size: 923863
+  dataset_size: 1020096
+- config_name: scidocs_mesh
+  features:
+  - name: paper_id
+    dtype: string
+  - name: label
+    dtype: int32
+  splits:
+  - name: test
+    num_bytes: 169488
+    num_examples: 3531
+  - name: train
+    num_bytes: 790944
+    num_examples: 16478
+  download_size: 862299
+  dataset_size: 960432
+- config_name: scidocs_view
+  features:
+  - name: query_id
+    dtype: string
+  - name: cand_id
+    dtype: string
+  - name: score
+    dtype: uint8
+  splits:
+  - name: test
+    num_bytes: 2668042
+    num_examples: 29978
+  download_size: 3717272
+  dataset_size: 2668042
+- config_name: scidocs_cite
+  features:
+  - name: query_id
+    dtype: string
+  - name: cand_id
+    dtype: string
+  - name: score
+    dtype: uint8
+  splits:
+  - name: test
+    num_bytes: 2663592
+    num_examples: 29928
+  download_size: 3711072
+  dataset_size: 2663592
+- config_name: scidocs_cocite
+  features:
+  - name: query_id
+    dtype: string
+  - name: cand_id
+    dtype: string
+  - name: score
+    dtype: uint8
+  splits:
+  - name: test
+    num_bytes: 2665461
+    num_examples: 29949
+  download_size: 3713676
+  dataset_size: 2665461
+- config_name: scidocs_read
+  features:
+  - name: query_id
+    dtype: string
+  - name: cand_id
+    dtype: string
+  - name: score
+    dtype: uint8
+  splits:
+  - name: test
+    num_bytes: 2667953
+    num_examples: 29977
+  download_size: 3717148
+  dataset_size: 2667953
+- config_name: reviewers
+  features:
+  - name: r_id
+    dtype: string
+  - name: papers
+    sequence: string
+  splits:
+  - name: metadata
+    num_bytes: 3564977
+    num_examples: 668
+  download_size: 3576339
+  dataset_size: 3564977
+- config_name: paper_reviewer_matching
+  features:
+  - name: query_id
+    dtype: string
+  - name: cand_id
+    dtype: string
+  - name: score
+    dtype: uint8
+  splits:
+  - name: test_hard
+    num_bytes: 50603
+    num_examples: 1729
+  - name: test_soft
+    num_bytes: 50603
+    num_examples: 1729
+  download_size: 222236
+  dataset_size: 101206
+- config_name: biomimicry
+  features:
+  - name: paper_id
+    dtype: string
+  - name: label
+    dtype: int32
+  splits:
+  - name: test
+    num_bytes: 44513
+    num_examples: 2748
+  - name: train
+    num_bytes: 133570
+    num_examples: 8243
+  download_size: 134151
+  dataset_size: 178083
+- config_name: relish
+  features:
+  - name: query_id
+    dtype: string
+  - name: cand_id
+    dtype: string
+  - name: score
+    dtype: uint8
+  splits:
+  - name: test
+    num_bytes: 4779565
+    num_examples: 191245
+  download_size: 11473140
+  dataset_size: 4779565
+- config_name: nfcorpus
+  features:
+  - name: query_id
+    dtype: string
+  - name: cand_id
+    dtype: string
+  - name: score
+    dtype: uint8
+  splits:
+  - name: test
+    num_bytes: 1188859
+    num_examples: 44634
+  download_size: 2751049
+  dataset_size: 1188859
+---

scirepeval_test.py

@@ -0,0 +1,197 @@
+# 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_test_configs import SCIREPEVAL_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 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=_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=_HOMEPAGE,
+            # License for the dataset if available
+            license=_LICENSE,
+            # Citation for the dataset
+            citation=_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.task_type in set(["classification", "regression"]):
+            data_urls.update({"train": f"{base_url}/test/{data_dir}/train.csv"})
+            data_urls.update({"test": f"{base_url}/test/{data_dir}/test.csv"})
+        elif self.config.task_type == "metadata":
+            data_urls.update({"metadata": f"{base_url}/test/{data_dir}/reviewer_metadata.jsonl"})
+        elif "reviewer_matching" in self.config.name:
+            data_urls.update({"test_hard": f"{base_url}/test/{data_dir}/test_hard_qrel.jsonl", 
+                             "test_soft": f"{base_url}/test/{data_dir}/test_soft_qrel.jsonl"})
+        else:
+            data_urls.update({"test": f"{base_url}/test/{data_dir}/test_qrel.jsonl"})
+        
+        downloaded_files = dl_manager.download_and_extract(data_urls)
+        splits = []
+        if self.config.task_type == "metadata":
+            splits = [datasets.SplitGenerator(
+                        name=datasets.Split("metadata"),
+                        # These kwargs will be passed to _generate_examples
+                        gen_kwargs={
+                            "filepath": downloaded_files["metadata"],
+                            "split": "metadata"
+                        },
+                    ),
+                     ]
+        elif "reviewer_matching" in self.config.name:
+            splits = [datasets.SplitGenerator(
+                            name=datasets.Split("test_hard"),
+                            # These kwargs will be passed to _generate_examples
+                            gen_kwargs={
+                                "filepath": downloaded_files["test_hard"],
+                                "split": "test"
+                            },
+                        ),
+                      datasets.SplitGenerator(
+                            name=datasets.Split("test_soft"),
+                            # These kwargs will be passed to _generate_examples
+                            gen_kwargs={
+                                "filepath": downloaded_files["test_soft"],
+                                "split": "test"
+                            },
+                        )
+                         ]
+        else:
+            splits = [datasets.SplitGenerator(
+                            name=datasets.Split.TEST,
+                            # These kwargs will be passed to _generate_examples
+                            gen_kwargs={
+                                "filepath": downloaded_files["test"],
+                                "split": "test"
+                            },
+                        ),
+                         ]
+
+        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",
+                    },
+                )]
+        return splits
+                
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, filepath, split):
+        # 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)
+        if self.config.task_type in set(["classification", "regression"]):
+            import csv
+            import ast
+            with open(filepath, encoding="utf-8") as f:
+                reader = csv.reader(f)
+                for id_, row in enumerate(reader):
+                    if id_ == 0:
+                        continue
+                    yield id_, {
+                        "paper_id": row[0],
+                        "label": ast.literal_eval(",".join(row[1:])) if self.config.name=="fos" else row[1]
+                    }
+        elif self.config.task_type == "metadata":
+            with open(filepath, encoding="utf-8") as f:
+                for line in f:
+                    d = json.loads(line)
+                    yield d["r_id"], d
+        else:
+            with open(filepath, encoding="utf-8") as f:
+                for i, line in enumerate(f):
+                    d = json.loads(line)
+                    yield i, d
+                    

scirepeval_test_configs.py

@@ -0,0 +1,99 @@
+from typing import Dict, Any, List
+
+import datasets
+
+
+class ScirepevalConfig(datasets.BuilderConfig):
+    """BuilderConfig for SuperGLUE."""
+
+    def __init__(self, task_type: str, features: Dict[str, Any]=None, 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.url = url
+
+
+SCIREPEVAL_CONFIGS = [
+    ScirepevalConfig(name="fos", features={"paper_id": datasets.Value("string"),
+         "label": datasets.Sequence(datasets.Value("int32"))}, task_type="classification"),
+
+    ScirepevalConfig(name="mesh_descriptors", features={"paper_id": datasets.Value("string"),
+         "label": datasets.Value("int32")}, task_type="classification"),
+    
+    ScirepevalConfig(name="biomimicry", features={"paper_id": datasets.Value("string"),
+         "label": datasets.Value("int32")}, task_type="classification"),
+
+    ScirepevalConfig(name="cite_count", features={"paper_id": datasets.Value("string"),
+         "label": datasets.Value("float64")}, task_type="regression"),
+
+    ScirepevalConfig(name="pub_year", features={"paper_id": datasets.Value("string"),
+         "label": datasets.Value("float64")}, task_type="regression"),
+
+    ScirepevalConfig(name="high_influence_cite", features={"query_id": datasets.Value("string"),
+         "cand_id": datasets.Value("string"), "score": datasets.Value("uint8")}, task_type="proximity"),
+
+    ScirepevalConfig(name="same_author", features={"query_id": datasets.Value("string"),
+         "cand_id": datasets.Value("string"), "score": datasets.Value("uint8")}, task_type="proximity"),
+
+    ScirepevalConfig(name="search", features={"query_id": datasets.Value("string"),
+         "cand_id": datasets.Value("string"), "score": datasets.Value("uint8")}, task_type="search"),
+
+    ScirepevalConfig(name="drsm", task_type="classification", features={"paper_id": datasets.Value("string"),
+         "label": datasets.Value("int32")}),
+
+    ScirepevalConfig(name="relish", features={"query_id": datasets.Value("string"),
+         "cand_id": datasets.Value("string"), "score": datasets.Value("uint8")}, task_type="proximity"),
+
+    ScirepevalConfig(name="nfcorpus", features={"query_id": datasets.Value("string"),
+         "cand_id": datasets.Value("string"), "score": datasets.Value("uint8")}, task_type="search"),
+
+    ScirepevalConfig(name="peer_review_score", task_type="regression", url="peer_review_score_hIndex/peer_review_score", features={"paper_id": datasets.Value("string"),
+         "label": datasets.Value("float64")}),
+    
+    ScirepevalConfig(name="hIndex", task_type="regression", url="peer_review_score_hIndex/hIndex", features={"paper_id": datasets.Value("string"),
+         "label": datasets.Value("float64")}),
+
+    ScirepevalConfig(name="trec_covid", features={"query_id": datasets.Value("string"),
+         "cand_id": datasets.Value("string"), "score": datasets.Value("int8")}, task_type="search"),
+
+    ScirepevalConfig(name="tweet_mentions", task_type="regression", features={"paper_id": datasets.Value("string"),
+         "label": datasets.Value("float64")}),
+
+    ScirepevalConfig(name="scidocs_mag", task_type="classification", url="scidocs/mag_mesh/mag", features={"paper_id": datasets.Value("string"),
+         "label": datasets.Value("int32")}),
+    
+    ScirepevalConfig(name="scidocs_mesh", task_type="classification", url="scidocs/mag_mesh/mesh", features={"paper_id": datasets.Value("string"),
+         "label": datasets.Value("int32")}),
+
+    ScirepevalConfig(name="scidocs_view", features={"query_id": datasets.Value("string"),
+         "cand_id": datasets.Value("string"), "score": datasets.Value("uint8")}, task_type="proximity", url="scidocs/view_cite_read/coview"),
+    
+    ScirepevalConfig(name="scidocs_cite", features={"query_id": datasets.Value("string"),
+         "cand_id": datasets.Value("string"), "score": datasets.Value("uint8")}, task_type="proximity", url="scidocs/view_cite_read/cite"),
+    
+    ScirepevalConfig(name="scidocs_cocite", features={"query_id": datasets.Value("string"),
+         "cand_id": datasets.Value("string"), "score": datasets.Value("uint8")}, task_type="proximity", url="scidocs/view_cite_read/cocite"),
+    
+    ScirepevalConfig(name="scidocs_read", features={"query_id": datasets.Value("string"),
+         "cand_id": datasets.Value("string"), "score": datasets.Value("uint8")}, task_type="proximity", url="scidocs/view_cite_read/coread"),
+
+    ScirepevalConfig(name="reviewers", task_type="metadata", url="paper_reviewer_matching", features={"r_id": datasets.Value("string"),
+         "papers": datasets.Sequence(datasets.Value("string"))}),
+    
+    ScirepevalConfig(name="paper_reviewer_matching", features={"query_id": datasets.Value("string"),
+         "cand_id": datasets.Value("string"), "score": datasets.Value("uint8")}, task_type="proximity"),
+
+]