# coding=utf-8 # 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. """ Exercise 2: Part 2 - Extractive QA 30 points , storing a dataset with HuggingFace """ import csv import json import os import datasets # Find for instance the citation on arxiv or on the dataset repo/website _CITATION = """\ @InProceedings{air21_grp13:tokenized_results, title = {Adv. Information Retrieval - Exercise , Part 2: Storing Results with HuggingFace}, author={Alexander Genser, Lena Jiricka, Samuel Keller }, year={2021} } """ # You can copy an official description _DESCRIPTION = """\ This new dataset are results from extractive QA, using our top-1 re-ranking results from our implementation of part 1 and the fira gold label """ _HOMEPAGE = "https://github.com/tuwien-information-retrieval/air-2021-group_13" _LICENSE = "MIT" # The HuggingFace dataset library don't host the datasets but only point to the original files # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method) _URLs = { 'first_domain': "https://huggingface.co/datasets/huggingFaceUser02/air21_grp13_tokenized_results.zip", 'second_domain': "https://huggingface.co/datasets/huggingFaceUser02/air21_grp13_tokenized_results.zip", } class AIR21Grp13TokenizedResults(datasets.GeneratorBasedBuilder): """ results from the top-1 re-ranking results from implementation of part 1 of Exercise 2, AIR 21. These are compared with the FiRA gold label results, using the BERT Transformer 'bert-large-uncased-whole-word-masking-finetuned-squad' """ VERSION = datasets.Version("0.1.0") # 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="inference_results", version=VERSION, description="This part of my dataset covers the results of the inference") ] DEFAULT_CONFIG_NAME = "inference_results" # 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 features = datasets.Features( { "doc_id": datasets.Value("long"), "query_id": datasets.Value("long"), "relevance": datasets.Value("float"), "query_sentence": 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, # specify them here. They'll be used if as_supervised=True in # builder.as_dataset. supervised_keys=None, # 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): """Returns SplitGenerators.""" # 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 my_urls = _URLs[self.config.name] data_dir = dl_manager.download_and_extract(my_urls) return [ datasets.SplitGenerator( name=datasets.Split.VALIDATION, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": os.path.join(data_dir, "top1_bert_large_uncased_answers_01.tsv"), "split": "dev", }, ), ] def _generate_examples( self, filepath, split # method parameters are unpacked from `gen_kwargs` as given in `_split_generators` ): """ Yields examples as (key, example) tuples. """ # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset. # The `key` is here for legacy reason (tfds) and is not important in itself. with open(filepath, encoding="utf-8") as f: # logger.info("Reading instances from lines in file at: %s", file_path) for line_num, line in enumerate(f): line = line.strip("\n") if not line: continue line_parts = line.split('\t') query_id, doc_id, relevance, answer = line_parts yield { "query_id": query_id, "doc_id": doc_id, "relevance": relevance, "answer": answer }