Datasets:

UKPLab
/

TexPrax

@@ -1,260 +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.
-"""TexPrax: Data collected during the project https://texprax.de/ """
-import csv
-import json
-import os
-import ast
-import datasets
-_CITATION = """\
-@article{stangier2022texprax,
-  title={TexPrax: A Messaging Application for Ethical, Real-time Data Collection and Annotation},
-  author={Stangier, Lorenz and Lee, Ji-Ung and Wang, Yuxi and M{\"u}ller, Marvin and Frick, Nicholas and Metternich, Joachim and Gurevych, Iryna},
-  journal={arXiv preprint arXiv:2208.07846},
-  year={2022}
-}
-"""
-_DESCRIPTION = """\
-This dataset was collected in the [TexPrax](https://texprax.de/) project and contains named entities annotated by three researchers as well as annotated sentences (problem/P, cause/C, solution/S, and other/O).
-"""
-_HOMEPAGE = "https://texprax.de/"
-_LICENSE = "Creative Commons Attribution-NonCommercial 4.0"
-# 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)
-_SENTENCE_URL = "https://tudatalib.ulb.tu-darmstadt.de/bitstream/handle/tudatalib/3534/texprax-sentences.zip?sequence=8&isAllowed=y"
-_ENTITY_URL = "https://tudatalib.ulb.tu-darmstadt.de/bitstream/handle/tudatalib/3534/texprax-ner.zip?sequence=9&isAllowed=y"
-class TexPraxConfig(datasets.BuilderConfig):
-    """BuilderConfig for SuperGLUE."""
-    def __init__(self, features, data_url, citation, url, label_classes=("False", "True"), **kwargs):
-        """BuilderConfig for TexPrax.
-        Args:
-        features: *list[string]*, list of the features that will appear in the
-            feature dict. Should not include "label".
-        data_url: *string*, url to download the zip file from.
-        citation: *string*, citation for the data set.
-        url: *string*, url for information about the data set.
-        label_classes: *list[string]*, the list of classes for the label if the
-            label is present as a string. Non-string labels will be cast to either
-            'False' or 'True'.
-        **kwargs: keyword arguments forwarded to super.
-        """
-        # Version history:
-        # 1.0.2: Fixed non-nondeterminism in ReCoRD.
-        # 1.0.1: Change from the pre-release trial version of SuperGLUE (v1.9) to
-        #        the full release (v2.0).
-        # 1.0.0: S3 (new shuffling, sharding and slicing mechanism).
-        # 0.0.2: Initial version.
-        super(TexPraxConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
-        self.features = features
-        self.label_classes = label_classes
-        self.data_url = data_url
-        self.citation = _CITATION
-        self.url = url
-class TexPraxDataset(datasets.GeneratorBasedBuilder):
-    """German dialgues that ocurred between workers in a factory. This dataset contains token level entity annotation as well as sentence level problem, cause, solution annotations."""
-    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 = [
-        TexPraxConfig(
-            name="sentence_classification",
-            description="Sentence level annotations of the TexPrax dataset.",
-            features=["sentence"],
-            data_url=_SENTENCE_URLS,
-        ),
-        TexPraxConfig(
-            name="named_entity_recognition",
-            description="Sentence level annotations of the TexPrax dataset.",
-            features=["tokens"],
-            data_url=_ENTITY_URL,
-        ),
-        #datasets.BuilderConfig(name="sentence_classification", version=VERSION, description="Sentence level annotations of the TexPrax dataset."),
-        #datasets.BuilderConfig(name="named_entity_recognition", version=VERSION, description="BIO-tagged named entites of the TexPrax dataset."),
-    ]
-    DEFAULT_CONFIG_NAME = "sentence_classification"  # It's not mandatory to have a default configuration. Just use one if it make sense.
-    def _info(self):
-        if self.config.name == "sentence_classification":  # This is the name of the configuration selected in BUILDER_CONFIGS above
-            features = datasets.Features(
-                {
-                    # Note: ID consists of <dialog-id_sentence-id_turn-id>
-                    "id": datasets.Value("string"),
-                    "sentence": datasets.Value("string"),
-                    "class": 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(
-                {
-                    # Note: ID consists of <dialog-id_turn-id>
-                    "id": datasets.Value("string"),
-                    "tokens": datasets.Value("list(string)"),
-                    "labels": datasets.Value("list(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
-        self.config.name == "sentence_classification":
-            urls = _SENTENCE_URL
-            data_dir = dl_manager.download_and_extract(urls)
-            return [
-                datasets.SplitGenerator(
-                    name=datasets.Split.TRAIN1,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={
-                        "filepath": os.path.join(data_dir, "industrie_sents_batch_1.csv"),
-                        "split": "batch-1-industrie",
-                    },
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.TRAIN2,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={
-                        "filepath": os.path.join(data_dir, "zerspanung_sents_batch_1.csv"),
-                        "split": "batch-1-zerspanung",
-                    },
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.VALIDATION,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={
-                        "filepath": os.path.join(data_dir, "sents_batch_2.csv"),
-                        "split": "batch-2",
-                    },
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.TEST,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={
-                        "filepath": os.path.join(data_dir, "sents_batch_3.csv"),
-                        "split": "batch-3"
-                    },
-                ),
-            ]
-        else:
-            urls = _ENTITY_URL
-            data_dir = dl_manager.download_and_extract(urls)
-            return [
-                datasets.SplitGenerator(
-                    name=datasets.Split.TRAIN1,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={
-                        "filepath": os.path.join(data_dir, "industrie_entities_batch_1.csv"),
-                        "split": "batch-1-industrie",
-                    },
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.TRAIN2,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={
-                        "filepath": os.path.join(data_dir, "zerspanung_entities_batch_1.csv"),
-                        "split": "batch-1-zerspanung",
-                    },
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.VALIDATION,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={
-                        "filepath": os.path.join(data_dir, "entities_batch_2.csv"),
-                        "split": "batch-2",
-                    },
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.TEST,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={
-                        "filepath": os.path.join(data_dir, "entities_batch_3.csv"),
-                        "split": "batch-3"
-                    },
-                ),
-            ]
-    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
-    def _generate_examples(self, filepath, split):
-        # 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:
-            creader = csv.reader(f, delimiter=';', quotechar='"')
-            for key, row in enumerate(creader):
-                if self.config.name == "sentence_classification":
-                    idx, sentence, label = row
-                    # Yields examples as (key, example) tuples
-                    yield key, {
-                        "idx": idx,
-                        "sentence": sentence,
-                        "label": label,
-                    }
-                else:
-                    idx, sentence, labels = row
-                    # Yields examples as (key, example) tuples
-                    yield key, {
-                        "idx": idx,
-                        "tokens": [t.strip() for t in ast.literal_eval(sentence)],
-                        "labels": [l.strip() for l in ast.literal_eval(labels)],
-                    }