| # 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. | |
| """TODO: Add a description here.""" | |
| import csv | |
| import json | |
| import os | |
| import numpy as np | |
| from pathlib import Path | |
| from tfrecord.reader import tfrecord_loader | |
| import datasets | |
| # TODO: Add BibTeX citation | |
| # Find for instance the citation on arxiv or on the dataset repo/website | |
| _CITATION = """\ | |
| @InProceedings{huggingface:dataset, | |
| title = {A great new dataset}, | |
| author={huggingface, Inc. | |
| }, | |
| year={2020} | |
| } | |
| """ | |
| # TODO: Add description of the dataset here | |
| # You can copy an official description | |
| _DESCRIPTION = """\ | |
| This new dataset is designed to solve this great NLP task and is crafted with a lot of care. | |
| """ | |
| # TODO: Add a link to an official homepage for the dataset here | |
| _HOMEPAGE = "" | |
| # TODO: Add the licence for the dataset here if you can find it | |
| _LICENSE = "" | |
| # TODO: Add link to the official dataset URLs here | |
| # 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 = { | |
| 'pretraining': "https://huggingface.co/great-new-dataset-first_domain.zip", | |
| # 'second_domain': "https://huggingface.co/great-new-dataset-second_domain.zip", | |
| } | |
| # TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case | |
| class WikipediaBERT128(datasets.GeneratorBasedBuilder): | |
| """TODO: Short description of my dataset.""" | |
| 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 = [ | |
| datasets.BuilderConfig(name="pretraining", version=VERSION, description="This part of my dataset covers a first domain"), | |
| ] | |
| def _info(self): | |
| # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset | |
| print(self.config.name) | |
| features = datasets.Features( | |
| { | |
| "input_ids": datasets.Sequence(datasets.Value("int64")), | |
| "attention_mask": datasets.Sequence(datasets.Value("int64")), | |
| "token_type_ids": datasets.Sequence(datasets.Value("int64")), | |
| "labels": datasets.Sequence(datasets.Value("int64")), | |
| "next_sentence_label": datasets.Value("int64"), | |
| # 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, | |
| # 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 | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| # These kwargs will be passed to _generate_examples | |
| gen_kwargs={ | |
| "data_files": self.config.data_files["train"], | |
| }, | |
| ), | |
| ] | |
| def _generate_examples(self, data_files): | |
| """ 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. | |
| # Convert from tfrecord files | |
| TFRECORD_KEYS = ( # Torch Model Keys | |
| 'input_ids', # input_ids : tokens after masking | |
| 'input_mask', # attention_mask : 1 if padding token, 0 otherwise | |
| 'segment_ids', # token_type_ids : sentence 0 or 1 | |
| 'masked_lm_positions', # masked_lm_positions : position of masked tokens in input_ids | |
| 'masked_lm_ids', # masked_lm_labels=None : label of masked tokens with padding as 0. | |
| 'next_sentence_labels' # next_sentence_label=None : 1 if next sentence, 0 otherwise | |
| ) | |
| highest_id_ = -1 | |
| for rec in data_files: | |
| reader = tfrecord_loader(rec, None, list(TFRECORD_KEYS)) | |
| for id_, d in enumerate(reader, start=highest_id_+1): | |
| highest_id_ = id_ | |
| input_ids = d["input_ids"] | |
| labels = np.ones_like(input_ids) * -100 | |
| masked_lm_positions = d["masked_lm_positions"] | |
| masked_lm_labels = d["masked_lm_ids"] | |
| masked_lm_positions_ = masked_lm_positions[masked_lm_positions != 0] | |
| masked_lm_labels_ = masked_lm_labels[:len(masked_lm_positions_)] | |
| labels[masked_lm_positions_] = masked_lm_labels_ | |
| yield id_, { | |
| "input_ids": input_ids, | |
| "attention_mask": d["input_mask"], | |
| "token_type_ids": d["segment_ids"], | |
| "labels": labels, | |
| "next_sentence_label": d["next_sentence_labels"] | |
| } | |