| # 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 | |
| } | |