xnli_cm.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
"""XNLI Code-Mixed Corpus in English, German, French for Binary Text Classification"""


import csv
import json
import os

import datasets


# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
# @InProceedings{huggingface:dataset,
# title = {A great new dataset},
# author={huggingface, Inc.
# },
# year={2020}
# }
"""

# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
This dataset was generated from XNLI using the CodeMixed Text Generator for a binary text classification task.
"""

# # 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 = ""

# TODO: Add link to the official dataset URLs here
# 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 = {
    "fr_ec": 
        {"train" : "data/fr_ec/train.tsv",
         "test" : "data/fr_ec/test.tsv"
         },
    "fr_ml": 
        {"train" : "data/fr_ml/train.tsv",
         "test" : "data/fr_ml/test.tsv"
         },
    "de_ec": 
        {"train" : "data/de_ec/train.tsv",
         "test" : "data/de_ec/test.tsv"
         },
    "de_ml": 
        {"train" : "data/de_ml/train.tsv",
         "test" : "data/de_ml/test.tsv"
         },
}



# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
class XnliCodeMixed(datasets.GeneratorBasedBuilder):
    """XNLI Corpus in English, German, and French"""

    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("fr_ec", version=VERSION, description="French-English code mixed dataset using Equivalence Constraint Theory"),
        datasets.BuilderConfig("fr_ml", version=VERSION, description="French-English code mixed dataset using Matrix Language Theory"),
        datasets.BuilderConfig("de_ec", version=VERSION, description="German-English code mixed dataset using Equivalence Constraint Theory"),
        datasets.BuilderConfig("de_ml", version=VERSION, description="German-English code mixed dataset using Matrix Language Theory"),
    ]

    DEFAULT_CONFIG_NAME = "fr_ec"  # It's not mandatory to have a default configuration. Just use one if it make sense.

    def _info(self):
        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
        # if self.config.name == "en":  # This is the name of the configuration selected in BUILDER_CONFIGS above
        features = datasets.Features(
            {
                "text": datasets.Value("string"),
                "label": datasets.Value("int64"),
                # 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(
        #         {
        #             "sentence": datasets.Value("string"),
        #             "option2": datasets.Value("string"),
        #             "second_domain_answer": 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):
        # TODO: 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)
        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": os.path.join(data_dir, "dev.jsonl"),
            #         "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):
        print(filepath)
        # TODO: 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:
            reader = csv.reader(f, delimiter='\t')
            next(reader)
            for key, data in enumerate(reader):
                # if self.config.name == "en":
                    # Yields examples as (key, example) tuples
                yield key, {
                    "text": data[0],
                    "label": data[1],
                }