remove config script

super_scirep.py

@@ -1,191 +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 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"
-        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",
-                         "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)
-        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