Upload TexPrax.py

TexPrax.py

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+# 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)],
+                    }
+
+
+