Delete loading script

scirepeval_test.py

@@ -1,197 +0,0 @@
-# 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
-