Delete csfever_v2.py

csfever_v2.py

@@ -1,286 +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
-"""CsFEVERv2 dataset"""
-
-
-import csv
-import json
-import os
-
-import datasets
-
-
-# TODO: Add description of the dataset here
-# You can copy an official description
-_DESCRIPTION = """\
-This new dataset is aimed on Czech fact-checking task.
-"""
-#TODO
-_CITATION = ""
-# 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 = ""
-
-# 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 = {
-    "original": {"train": "./original/train.jsonl",
-                 "dev" : "./original/dev.jsonl",
-                 "test": "./original/test.jsonl"},
-    "f1": {"train": "./f1/train.jsonl",
-                 "dev" : "./f1/dev.jsonl",
-                 "test": "./f1/test.jsonl"},
-    "precision": {"train": "./precision/train.jsonl",
-                 "dev" : "./precision/dev.jsonl",
-                 "test": "./precision/test.jsonl"},
-    "07": {"train": "./07/train.jsonl",
-                 "dev" : "./07/dev.jsonl",
-                 "test": "./07/test.jsonl"},
-    "wiki_pages": "./wiki_pages/wiki_pages.jsonl",
-    "original_nli": {"train": "./original_nli/train.jsonl",
-                 "dev" : "./original_nli/dev.jsonl",
-                 "test": "./original_nli/test.jsonl"},
-    "f1_nli": {"train": "./f1_nli/train.jsonl",
-                 "dev" : "./f1_nli/dev.jsonl",
-                 "test": "./f1_nli/test.jsonl"},
-    "07_nli": {"train": "./07_nli/train.jsonl",
-                 "dev" : "./07_nli/dev.jsonl",
-                 "test": "./07_nli/test.jsonl"},
-    "precision_nli": {"train": "./precision_nli/train.jsonl",
-                 "dev" : "./precision_nli/dev.jsonl",
-                 "test": "./precision_nli/test.jsonl"},
-}
-
-_ORIGINAL_DESCRIPTION = ""
-
-_NLI_NAMES = ["original_nli", "07_nli", "precision_nli", "f1_nli"]
-
-
-#Name of the dataset usually matches the script name with CamelCase instead of snake_case
-class CsFEVERv2(datasets.GeneratorBasedBuilder):
-    """CsFEVERv2"""
-
-    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 = [
-        datasets.BuilderConfig(
-            name="original",
-            version=VERSION,
-            description=_ORIGINAL_DESCRIPTION,
-        ),
-        datasets.BuilderConfig(
-            name="f1",
-            version=VERSION,
-            description=_ORIGINAL_DESCRIPTION,
-        ),
-        datasets.BuilderConfig(
-            name="precision",
-            version=VERSION,
-            description=_ORIGINAL_DESCRIPTION
-        ),
-        datasets.BuilderConfig(
-            name="07",
-            version=VERSION,
-            description=_ORIGINAL_DESCRIPTION
-        ),
-        datasets.BuilderConfig(
-            name="wiki_pages",
-            version=VERSION,
-            description=_ORIGINAL_DESCRIPTION
-        ),
-        datasets.BuilderConfig(
-            name="original_nli",
-            version=VERSION,
-            description=_ORIGINAL_DESCRIPTION
-        ),
-        datasets.BuilderConfig(
-            name="07_nli",
-            version=VERSION,
-            description=_ORIGINAL_DESCRIPTION
-        ),
-        datasets.BuilderConfig(
-            name="f1_nli",
-            version=VERSION,
-            description=_ORIGINAL_DESCRIPTION
-        ),
-        datasets.BuilderConfig(
-            name="precision_nli",
-            version=VERSION,
-            description=_ORIGINAL_DESCRIPTION
-        ),
-    ]
-
-    DEFAULT_CONFIG_NAME = "original"  # It's not mandatory to have a default configuration. Just use one if it make sense.
-
-    def _info(self):
-        #This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
-        if self.config.name == "original":  # This is the name of the configuration selected in BUILDER_CONFIGS above
-            features = datasets.Features(
-                {
-                    "id": datasets.Value("int32"),
-                    "label": datasets.Value("string"),
-                    "predicted_label": datasets.Value("string"),
-                    "predicted_score": datasets.Value("float"),
-                    "claim": datasets.Value("string"),
-                    "evidence": datasets.Sequence(datasets.Sequence(datasets.Value("string"))),
-                    # These are the features of your dataset like images, labels ...
-                }
-            )
-        elif self.config.name in _NLI_NAMES:  # This is the name of the configuration selected in BUILDER_CONFIGS above
-            features = datasets.Features(
-                {
-                    "id": datasets.Value("int32"),
-                    "label": datasets.ClassLabel(num_classes=3, names=["SUPPORTS", "REFUTES", "NOT ENOUGH INFO"]),
-                    "claim": datasets.Value("string"),
-                    "evidence": datasets.Value("string"),
-                    # These are the features of your dataset like images, labels ...
-                }
-            )
-        elif self.config.name == "wiki_pages":
-            features = datasets.Features(
-                {
-                    "id": datasets.Value("int32"),
-                    "revid": datasets.Value("int32"),
-                    "url": datasets.Value("string"),
-                    "title": datasets.Value("string"),
-                    "text": datasets.Value("string"),
-                    # These are the features of your dataset like images, labels ...
-                }
-            )
-        else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
-            features = datasets.Features(
-                {
-                    "id": datasets.Value("int32"),
-                    "label": datasets.Value("string"),
-                    "claim": datasets.Value("string"),
-                    "evidence": datasets.Sequence(datasets.Sequence(datasets.Value("string"))),
-                    # These are the features of your dataset like images, labels ...
-                }
-            )
-        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=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):
-        # 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
-
-        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
-        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
-        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
-        urls = _URLS[self.config.name]
-        data_dir = dl_manager.download_and_extract(urls)
-        if self.config.name == "wiki_pages":
-            return [datasets.SplitGenerator(
-                name="wiki_pages",
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": data_dir,
-                    "split": "wiki_pages",
-                },
-            )]
-        else:
-            return [
-                datasets.SplitGenerator(
-                    name=datasets.Split.TRAIN,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={
-                        "filepath": data_dir["train"],
-                        "split": "train",
-                    },
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.VALIDATION,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={
-                        "filepath": data_dir["dev"],
-                        "split": "dev",
-                    },
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.TEST,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={
-                        "filepath": data_dir["test"],
-                        "split": "test"
-                    },
-                ),
-            ]
-
-    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
-    def _generate_examples(self, filepath, split):
-        # 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.
-        with open(filepath, encoding="utf-8") as f:
-            for key, row in enumerate(f):
-                data_point = json.loads(row)
-                if self.config.name == "original":
-                    # Yields examples as (key, example) tuples
-                    yield key, {
-                        "id": data_point["id"],
-                        "label": data_point["label"],
-                        "predicted_label": data_point["predicted_label"],
-                        "predicted_score": data_point["predicted_score"],
-                        "claim": data_point["claim"],
-                        "evidence": data_point["evidence"],
-                    }
-                elif self.config.name in _NLI_NAMES:
-                     yield key, {
-                        "id": data_point["id"],
-                        "label": data_point["label"],
-                        "claim": data_point["claim"],
-                        "evidence": data_point["evidence"],
-                    }
-                elif self.config.name == "wiki_pages":
-                    yield key, {
-                        "id": data_point["id"],
-                        "revid": data_point["revid"],
-                        "url": data_point["url"],
-                        "title": data_point["title"],
-                        "text": data_point["text"],
-                    }
-                else:
-                    yield key, {
-                        "id": data_point["id"],
-                        "label": data_point["label"],
-                        "claim": data_point["claim"],
-                        "evidence": data_point["evidence"],
-                    }