First version of the Wikipedia BERT 128 dataset

wikipedia_bert_128.py

@@ -0,0 +1,167 @@
+# 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
+        data_dir = os.path.abspath(os.path.expanduser(dl_manager.manual_dir))
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": data_dir,
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath):
+        """ 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
+        )
+        # tfrecords = Path(filepath).glob("*.tfrecord")
+        tfrecords = [Path(filepath)/"wiki_000.tfrecord"]
+
+        highest_id_ = -1
+        for rec in tfrecords:
+            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"]
+                }