Delete loading script

glucose.py

@@ -1,160 +0,0 @@
-# coding=utf-8
-# 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.
-"""GLUCOSE: GeneraLized and COntextualized Story Explanations, is a novel conceptual framework and dataset for commonsense reasoning. Given a short story and a sentence X in the story, GLUCOSE captures ten dimensions of causal explanation related to X. These dimensions, inspired by human cognitive psychology, cover often-implicit causes and effects of X, including events, location, possession, and other attributes."""
-
-
-import csv
-import os
-
-import datasets
-
-
-# Find for instance the citation on arxiv or on the dataset repo/website
-_CITATION = """\
-@inproceedings{mostafazadeh2020glucose,
-      title={GLUCOSE: GeneraLized and COntextualized Story Explanations},
-      author={Nasrin Mostafazadeh and Aditya Kalyanpur and Lori Moon and David Buchanan and Lauren Berkowitz and Or Biran and Jennifer Chu-Carroll},
-      year={2020},
-      booktitle={The Conference on Empirical Methods in Natural Language Processing},
-      publisher={Association for Computational Linguistics}
-}
-"""
-
-# You can copy an official description
-_DESCRIPTION = """\
-When humans read or listen, they make implicit commonsense inferences that frame their understanding of what happened and why. As a step toward AI systems that can build similar mental models, we introduce GLUCOSE, a large-scale dataset of implicit commonsense causal knowledge, encoded as causal mini-theories about the world, each grounded in a narrative context.
-"""
-
-_HOMEPAGE = "https://github.com/ElementalCognition/glucose"
-
-_LICENSE = "Creative Commons Attribution-NonCommercial 4.0 International Public License"
-
-_URLs = {
-    "glucose": {
-        "test": "https://raw.githubusercontent.com/ElementalCognition/glucose/master/test/test_set_no_answers.csv",
-        "train": "https://github.com/TevenLeScao/glucose/blob/master/GLUCOSE_training_data.zip?raw=true",
-    }
-}
-
-
-class Glucose(datasets.GeneratorBasedBuilder):
-    """GLUCOSE: GeneraLized and COntextualized Story Explanations, is a novel conceptual framework and dataset for commonsense reasoning."""
-
-    VERSION = datasets.Version("1.1.0")
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="glucose", description="Main dataset"),
-    ]
-
-    def _info(self):
-        feature_dict = {
-            "experiment_id": datasets.Value("string"),
-            "story_id": datasets.Value("string"),
-            # The train set contains only one ID in numeric form
-            "worker_id": datasets.Value("int64"),
-            # The test set contains several IDs in string form
-            "worker_ids": datasets.Value("string"),
-            "submission_time_normalized": datasets.Value("string"),
-            "worker_quality_assessment": datasets.Value("int64"),
-            "selected_sentence_index": datasets.Value("int64"),
-            "story": datasets.Value("string"),
-            "selected_sentence": datasets.Value("string"),
-            "number_filled_in": datasets.Value("int64"),
-        }
-        for i in range(1, 11):
-            feature_dict[f"{i}_specificNL"] = datasets.Value("string")
-            feature_dict[f"{i}_specificStructured"] = datasets.Value("string")
-            feature_dict[f"{i}_generalNL"] = datasets.Value("string")
-            feature_dict[f"{i}_generalStructured"] = datasets.Value("string")
-        features = datasets.Features(feature_dict)
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            supervised_keys=None,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        train_url = _URLs[self.config.name]["train"]
-        test_url = _URLs[self.config.name]["test"]
-        train_data = dl_manager.download_and_extract(train_url)
-        test_data = dl_manager.download_and_extract(test_url)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "filepath": os.path.join(train_data, "GLUCOSE_training_data_final.csv"),
-                    "split": "train",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={"filepath": test_data, "split": "test"},
-            ),
-        ]
-
-    def _generate_examples(self, filepath, split):
-        with open(filepath, encoding="utf8") as f:
-            data = csv.reader(f)
-            next(data)
-            for id_, row in enumerate(data):
-                if split == "train":
-                    yield id_, train_dict_from_row(row)
-                else:
-                    yield id_, test_dict_from_row(row)
-
-
-def train_dict_from_row(row):
-    return_dict = {
-        "experiment_id": row[0],
-        "story_id": row[1],
-        "worker_id": row[2],
-        "worker_ids": "",
-        "submission_time_normalized": row[3],
-        "worker_quality_assessment": row[4],
-        "selected_sentence_index": row[5],
-        "story": row[6],
-        "selected_sentence": row[7],
-        "number_filled_in": row[48],
-    }
-    for i in range(1, 11):
-        return_dict[f"{i}_specificNL"] = row[4 * i + 4]
-        return_dict[f"{i}_specificStructured"] = row[4 * i + 5]
-        return_dict[f"{i}_generalNL"] = row[4 * i + 6]
-        return_dict[f"{i}_generalStructured"] = row[4 * i + 7]
-    return return_dict
-
-
-def test_dict_from_row(row):
-    return_dict = {
-        "experiment_id": "",
-        "story_id": row[0],
-        "worker_id": -1,
-        "worker_ids": row[3],
-        "submission_time_normalized": "",
-        "worker_quality_assessment": -1,
-        "selected_sentence_index": -1,
-        "story": row[1],
-        "selected_sentence": row[2],
-        "number_filled_in": -1,
-    }
-    for i in range(1, 11):
-        return_dict[f"{i}_specificNL"] = row[2 * i + 2]
-        return_dict[f"{i}_generalNL"] = row[2 * i + 3]
-        return_dict[f"{i}_specificStructured"] = ""
-        return_dict[f"{i}_generalStructured"] = ""
-    return return_dict