# 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: Add description """TexPrax: Data collected during the project https://texprax.de/ """ import csv import os import ast #import json import datasets # TODO: Add citation _CITATION = """\ @inproceedings{stangier-etal-2022-texprax, title = "{T}ex{P}rax: 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}, booktitle = "Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing: System Demonstrations", month = nov, year = "2022", address = "Taipei, Taiwan", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.aacl-demo.2", pages = "9--16", } """ # TODO: Add description _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). """ # TODO: Add link _HOMEPAGE = "https://texprax.de/" # TODO: Add license _LICENSE = "Creative Commons Attribution-NonCommercial 4.0" # TODO: Add tudatalib urls here! _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 TexPrax.""" def __init__(self, features, data_url, citation, url, label_classes=("False", "True"), **kwargs): super(TexPraxConfig, self).__init__(**kwargs) 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") BUILDER_CONFIGS = [ datasets.BuilderConfig(name="sentence_cl", version=VERSION, description="Sentence level annotations of the TexPrax dataset."), datasets.BuilderConfig(name="ner", version=VERSION, description="BIO-tagged named entites of the TexPrax dataset."), ] DEFAULT_CONFIG_NAME = "sentence_cl" # It's not mandatory to have a default configuration. Just use one if it make sense. def _info(self): if self.config.name == "sentence_cl": # This is the name of the configuration selected in BUILDER_CONFIGS above features = datasets.Features( { # Note: ID consists of "id": datasets.Value("string"), "sentence": datasets.Value("string"), "label": datasets.features.ClassLabel( names=[ "P", "C", "S", "O", ]), "subsplit": 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 "id": datasets.Value("string"), "tokens": datasets.Sequence(datasets.Value("string")), "entities": datasets.Sequence( datasets.features.ClassLabel( names=[ "B-LOC", "I-LOC", "B-ED", "B-ACT", "I-ACT", "B-PRE", "I-PRE", "B-AKT", "I-AKT", "B-PER", "I-PER", "B-A", "B-G", "B-I", "I-I", "B-OT", "I-OT", "B-M", "I-M", "B-P", "I-P", "B-PR", "I-PR", "B-PE", "I-PE", "O", ] ) ), "subsplit": datasets.Value("string"), } ) 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): # 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 if self.config.name == "sentence_cl": urls = _SENTENCE_URL 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": os.path.join(data_dir, "sents_train.csv"), "split": "train", }, ), datasets.SplitGenerator( name=datasets.Split.TEST, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": os.path.join(data_dir, "sents_test.csv"), "split": "test" }, ), ] else: urls = _ENTITY_URL 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": os.path.join(data_dir, "entities_train.csv"), "split": "train", }, ), datasets.SplitGenerator( name=datasets.Split.TEST, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": os.path.join(data_dir, "entities_test.csv"), "split": "test" }, ) ] # 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='"') next(creader) # skip header for key, row in enumerate(creader): if self.config.name == "sentence_cl": dialog_id, turn_id, sentence_id, sentence, label, domain, batch = row idx = f"{dialog_id}_{turn_id}_{sentence_id}" yield key, { "id": idx, "sentence": sentence, "label": label, "subsplit": batch, #"domain": domain, } else: idx, sentence, labels, split = row # Yields examples as (key, example) tuples yield key, { "id": idx, "tokens": [t.strip() for t in ast.literal_eval(sentence)], "entities": [l.strip() for l in ast.literal_eval(labels)], "subsplit": split, }