csfever_v2.py

# 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"],
                    }