Delete wikipedia-bert-512.py

Don't need dataset conversion script anymore

wikipedia-bert-512.py

@@ -1,164 +0,0 @@
-# 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"]
-                }
-