Delete loading script

civil_comments.py

@@ -1,148 +0,0 @@
-# coding=utf-8
-# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
-#
-# 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.
-
-# Lint as: python3
-"""CivilComments from Jigsaw Unintended Bias Kaggle Competition."""
-
-
-import csv
-import os
-
-import datasets
-
-
-_CITATION = """
-@article{DBLP:journals/corr/abs-1903-04561,
-  author    = {Daniel Borkan and
-               Lucas Dixon and
-               Jeffrey Sorensen and
-               Nithum Thain and
-               Lucy Vasserman},
-  title     = {Nuanced Metrics for Measuring Unintended Bias with Real Data for Text
-               Classification},
-  journal   = {CoRR},
-  volume    = {abs/1903.04561},
-  year      = {2019},
-  url       = {http://arxiv.org/abs/1903.04561},
-  archivePrefix = {arXiv},
-  eprint    = {1903.04561},
-  timestamp = {Sun, 31 Mar 2019 19:01:24 +0200},
-  biburl    = {https://dblp.org/rec/bib/journals/corr/abs-1903-04561},
-  bibsource = {dblp computer science bibliography, https://dblp.org}
-}
-"""
-
-_DESCRIPTION = """
-The comments in this dataset come from an archive of the Civil Comments
-platform, a commenting plugin for independent news sites. These public comments
-were created from 2015 - 2017 and appeared on approximately 50 English-language
-news sites across the world. When Civil Comments shut down in 2017, they chose
-to make the public comments available in a lasting open archive to enable future
-research. The original data, published on figshare, includes the public comment
-text, some associated metadata such as article IDs, timestamps and
-commenter-generated "civility" labels, but does not include user ids. Jigsaw
-extended this dataset by adding additional labels for toxicity and identity
-mentions. This data set is an exact replica of the data released for the
-Jigsaw Unintended Bias in Toxicity Classification Kaggle challenge.  This
-dataset is released under CC0, as is the underlying comment text.
-"""
-
-_DOWNLOAD_URL = "https://storage.googleapis.com/jigsaw-unintended-bias-in-toxicity-classification/civil_comments.zip"
-
-
-class CivilComments(datasets.GeneratorBasedBuilder):
-    """Classification and tagging of 2M comments on news sites.
-
-    This version of the CivilComments Dataset provides access to the primary
-    seven labels that were annotated by crowd workers, the toxicity and other
-    tags are a value between 0 and 1 indicating the fraction of annotators that
-    assigned these attributes to the comment text.
-
-    The other tags, which are only available for a fraction of the input examples
-    are currently ignored, as are all of the attributes that were part of the
-    original civil comments release. See the Kaggle documentation for more
-    details about the available features.
-    """
-
-    VERSION = datasets.Version("0.9.0")
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            # datasets.features.FeatureConnectors
-            features=datasets.Features(
-                {
-                    "text": datasets.Value("string"),
-                    "toxicity": datasets.Value("float32"),
-                    "severe_toxicity": datasets.Value("float32"),
-                    "obscene": datasets.Value("float32"),
-                    "threat": datasets.Value("float32"),
-                    "insult": datasets.Value("float32"),
-                    "identity_attack": datasets.Value("float32"),
-                    "sexual_explicit": datasets.Value("float32"),
-                }
-            ),
-            # The supervised_keys version is very impoverished.
-            supervised_keys=("text", "toxicity"),
-            homepage="https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/data",
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        dl_path = dl_manager.download_and_extract(_DOWNLOAD_URL)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={"filename": os.path.join(dl_path, "train.csv"), "toxicity_label": "target"},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "filename": os.path.join(dl_path, "test_public_expanded.csv"),
-                    "toxicity_label": "toxicity",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "filename": os.path.join(dl_path, "test_private_expanded.csv"),
-                    "toxicity_label": "toxicity",
-                },
-            ),
-        ]
-
-    def _generate_examples(self, filename, toxicity_label):
-        """Yields examples.
-
-        Each example contains a text input and then seven annotation labels.
-
-        Args:
-          filename: the path of the file to be read for this split.
-          toxicity_label: indicates 'target' or 'toxicity' to capture the variation
-            in the released labels for this dataset.
-
-        Yields:
-          A dictionary of features, all floating point except the input text.
-        """
-        with open(filename, encoding="utf-8") as f:
-            reader = csv.DictReader(f)
-            for row in reader:
-                example = {}
-                example["text"] = row["comment_text"]
-                example["toxicity"] = float(row[toxicity_label])
-                for label in ["severe_toxicity", "obscene", "threat", "insult", "identity_attack", "sexual_explicit"]:
-                    example[label] = float(row[label])
-                yield row["id"], example