add loading script

bernice-pretrain-data.py

@@ -0,0 +1,121 @@
+# 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: Address all TODOs and remove all explanatory comments
+"""Bernice pretrain data"""
+
+
+import csv
+import json
+import os
+import gzip
+import datasets
+
+
+# TODO: Add BibTeX citation
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+Alexandra DeLucia, Shijie Wu, Aaron Mueller, Carlos Aguirre, Philip Resnik, and Mark Dredze. 2022. 
+Bernice: A Multilingual Pre-trained Encoder for Twitter. In Proceedings of the 2022 Conference on 
+Empirical Methods in Natural Language Processing, pages 6191–6205, Abu Dhabi, United Arab Emirates. 
+Association for Computational Linguistics.
+"""
+
+# You can copy an official description
+_DESCRIPTION = """\
+Tweet IDs for the 2.5 billion multilingual tweets used to train Bernice, a Twitter encoder.
+The tweets are from the public 1% Twitter API stream from January 2016 to December 2021. 
+Twitter-provided language metadata is provided with the tweet ID. The data contains 66 unique languages, 
+as identified by ISO 639 language codes, including `und` for undefined languages.
+Tweets need to be re-gathered via the Twitter API.
+"""
+
+_HOMEPAGE = "https://preview.aclanthology.org/emnlp-22-ingestion/2022.emnlp-main.415"
+
+# 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 Datasets library doesn't host the datasets but only points to the original files.
+# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
+# If the data files live in the same folder or repository of the dataset script,
+# you can just pass the relative paths to the files instead of URLs.
+# Only train data, validation split not provided
+_URLS = {
+    "train": "data"
+}
+
+
+# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
+class BernicePretrainData(datasets.GeneratorBasedBuilder):
+    """Tweet IDs for the 2.5 billion multilingual tweets used to train Bernice, a Twitter encoder."""
+    VERSION = datasets.Version("1.1.0")
+
+    def _info(self):
+        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
+            # Here we define them above because they are different between the two configurations
+            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
+            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
+            # supervised_keys=("sentence", "label"),
+            # Homepage of the dataset for documentation
+            features=datasets.Features(
+                {
+                    "tweet_id": datasets.Value("string"),
+                    "lang": datasets.Value("string"),
+                    "year": datasets.Value("string")
+                }
+            ),
+            homepage=_HOMEPAGE,
+            # License for the dataset if available
+            license=_LICENSE,
+            # Citation for the dataset
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        # 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
+        dir_url = self._URLS["train"]
+        urls_to_download = [f"{dir_url}/{f}" for f in os.listdir(dir_url)]
+        downloaded_files = dl_manager.download_and_extract(urls_to_download)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepaths": downloaded_files,
+                    "split": "train",
+                },
+            )
+        ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, filepaths, split):
+        # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
+        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
+        for filepath in filepaths:
+            with gzip.open(filepath, "rb") as f:
+                for line_number, instance in enumerate(f):
+                    tweet_id, lang, year = instance.strip().split("\t")
+                    yield tweet_id, {
+                        "tweet_id": tweet_id,
+                        "lang": lang,
+                        "year": year
+                    }