Delete loading script

go_emotions.py

@@ -1,158 +0,0 @@
-# coding=utf-8
-# Copyright 2020 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
-"""GoEmotions dataset"""
-
-
-import csv
-import os
-
-import datasets
-
-
-_DESCRIPTION = """\
-The GoEmotions dataset contains 58k carefully curated Reddit comments labeled for 27 emotion categories or Neutral.
-The emotion categories are admiration, amusement, anger, annoyance, approval, caring, confusion, curiosity, desire,
-disappointment, disapproval, disgust, embarrassment, excitement, fear, gratitude, grief, joy, love, nervousness,
-optimism, pride, realization, relief, remorse, sadness, surprise.
-"""
-
-_CITATION = """\
-@inproceedings{demszky2020goemotions,
- author = {Demszky, Dorottya and Movshovitz-Attias, Dana and Ko, Jeongwoo and Cowen, Alan and Nemade, Gaurav and Ravi, Sujith},
- booktitle = {58th Annual Meeting of the Association for Computational Linguistics (ACL)},
- title = {{GoEmotions: A Dataset of Fine-Grained Emotions}},
- year = {2020}
-}
-"""
-
-_CLASS_NAMES = [
-    "admiration",
-    "amusement",
-    "anger",
-    "annoyance",
-    "approval",
-    "caring",
-    "confusion",
-    "curiosity",
-    "desire",
-    "disappointment",
-    "disapproval",
-    "disgust",
-    "embarrassment",
-    "excitement",
-    "fear",
-    "gratitude",
-    "grief",
-    "joy",
-    "love",
-    "nervousness",
-    "optimism",
-    "pride",
-    "realization",
-    "relief",
-    "remorse",
-    "sadness",
-    "surprise",
-    "neutral",
-]
-
-_BASE_DOWNLOAD_URL = "https://github.com/google-research/google-research/raw/master/goemotions/data/"
-_RAW_DOWNLOAD_URLS = [
-    "https://storage.googleapis.com/gresearch/goemotions/data/full_dataset/goemotions_1.csv",
-    "https://storage.googleapis.com/gresearch/goemotions/data/full_dataset/goemotions_2.csv",
-    "https://storage.googleapis.com/gresearch/goemotions/data/full_dataset/goemotions_3.csv",
-]
-_HOMEPAGE = "https://github.com/google-research/google-research/tree/master/goemotions"
-
-
-class GoEmotionsConfig(datasets.BuilderConfig):
-    @property
-    def features(self):
-        if self.name == "simplified":
-            return {
-                "text": datasets.Value("string"),
-                "labels": datasets.Sequence(datasets.ClassLabel(names=_CLASS_NAMES)),
-                "id": datasets.Value("string"),
-            }
-        elif self.name == "raw":
-            d = {
-                "text": datasets.Value("string"),
-                "id": datasets.Value("string"),
-                "author": datasets.Value("string"),
-                "subreddit": datasets.Value("string"),
-                "link_id": datasets.Value("string"),
-                "parent_id": datasets.Value("string"),
-                "created_utc": datasets.Value("float"),
-                "rater_id": datasets.Value("int32"),
-                "example_very_unclear": datasets.Value("bool"),
-            }
-            d.update({label: datasets.Value("int32") for label in _CLASS_NAMES})
-            return d
-
-
-class GoEmotions(datasets.GeneratorBasedBuilder):
-    """GoEmotions dataset"""
-
-    BUILDER_CONFIGS = [
-        GoEmotionsConfig(
-            name="raw",
-        ),
-        GoEmotionsConfig(
-            name="simplified",
-        ),
-    ]
-    BUILDER_CONFIG_CLASS = GoEmotionsConfig
-    DEFAULT_CONFIG_NAME = "simplified"
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(self.config.features),
-            homepage=_HOMEPAGE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        if self.config.name == "raw":
-            paths = dl_manager.download_and_extract(_RAW_DOWNLOAD_URLS)
-            return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepaths": paths, "raw": True})]
-        if self.config.name == "simplified":
-            train_path = dl_manager.download_and_extract(os.path.join(_BASE_DOWNLOAD_URL, "train.tsv"))
-            dev_path = dl_manager.download_and_extract(os.path.join(_BASE_DOWNLOAD_URL, "dev.tsv"))
-            test_path = dl_manager.download_and_extract(os.path.join(_BASE_DOWNLOAD_URL, "test.tsv"))
-            return [
-                datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepaths": [train_path]}),
-                datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepaths": [dev_path]}),
-                datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepaths": [test_path]}),
-            ]
-
-    def _generate_examples(self, filepaths, raw=False):
-        """Generate AG News examples."""
-        for file_idx, filepath in enumerate(filepaths):
-            with open(filepath, "r", encoding="utf-8") as f:
-                if raw:
-                    reader = csv.DictReader(f)
-                else:
-                    reader = csv.DictReader(f, delimiter="\t", fieldnames=list(self.config.features.keys()))
-
-                for row_idx, row in enumerate(reader):
-                    if raw:
-                        row["example_very_unclear"] = row["example_very_unclear"] == "TRUE"
-                    else:
-                        row["labels"] = [int(ind) for ind in row["labels"].split(",")]
-
-                    yield f"{file_idx}_{row_idx}", row