Adding dataset loading script

README.md

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+POS tagging on the Universal Dependencies dataset

udpos.py

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+# 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
+"""Script to load the Universal Dependencies dataset for POS tagging in prompting format"""
+
+
+import csv
+import json
+import os
+
+import datasets
+
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@inproceedings{nivre-etal-2020-universal,
+    title = "{U}niversal {D}ependencies v2: An Evergrowing Multilingual Treebank Collection",
+    author = "Nivre, Joakim  and
+      de Marneffe, Marie-Catherine  and
+      Ginter, Filip  and
+      Haji{\v{c}}, Jan  and
+      Manning, Christopher D.  and
+      Pyysalo, Sampo  and
+      Schuster, Sebastian  and
+      Tyers, Francis  and
+      Zeman, Daniel",
+    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
+    month = may,
+    year = "2020",
+    address = "Marseille, France",
+    publisher = "European Language Resources Association",
+    url = "https://aclanthology.org/2020.lrec-1.497",
+    pages = "4034--4043",
+    abstract = "Universal Dependencies is an open community effort to create cross-linguistically consistent treebank annotation for many languages within a dependency-based lexicalist framework. The annotation consists in a linguistically motivated word segmentation; a morphological layer comprising lemmas, universal part-of-speech tags, and standardized morphological features; and a syntactic layer focusing on syntactic relations between predicates, arguments and modifiers. In this paper, we describe version 2 of the universal guidelines (UD v2), discuss the major changes from UD v1 to UD v2, and give an overview of the currently available treebanks for 90 languages.",
+    language = "English",
+    ISBN = "979-10-95546-34-4",
+}
+"""
+
+# You can copy an official description
+_DESCRIPTION = """\
+Universal Dependencies is an open community effort to create cross-linguistically consistent treebank annotation for many languages within a dependency-based lexicalist framework. The annotation consists in a linguistically motivated word segmentation; a morphological layer comprising lemmas, universal part-of-speech tags, and standardized morphological features; and a syntactic layer focusing on syntactic relations between predicates, arguments and modifiers. 
+"""
+
+_HOMEPAGE = "https://universaldependencies.org"
+
+_LICENSE = ""
+
+# 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)
+_LANGS = ['pt-pud', 'de-gsd', 'zh-gsdsimp', 'hu-szeged', 'ko-gsd', 'af-afribooms', 'tr-imst', 'it-partut', 'it-vit', 'pl-pud', 'ar-pud', 'kk-ktb', 'fr-pud', 'it-postwita', 'ro-simonero', 'pl-pdb', 'en-partut', 'th-pud', 'ro-nonstandard', 'hi-hdtb', 'eu-bdt', 'te-mtg', 'en-pud', 'hi-pud', 'ja-pud', 'wo-wtb', 'fi-pud', 'nl-alpino', 'ru-gsd', 'fr-sequoia', 'fr-partut', 'fi-tdt', 'ro-rrt', 'ur-udtb', 'ko-kaist', 'pt-bosque', 'it-twittiro', 'ru-syntagrus', 'fa-seraji', 'it-isdt', 'he-htb', 'lt-alksnis', 'nl-lassysmall', 'de-pud', 'uk-iu', 'zh-pud', 'es-pud', 'fr-gsd', 'ja-gsd', 'tr-gb', 'bg-btb', 'ta-ttb', 'pl-lfg', 'en-lines', 'yo-ytb', 'lt-hse', 'tl-trg', 'id-gsd', 'en-gum', 'vi-vtb', 'ko-pud', 'de-lit', 'ar-padt', 'en-ewt', 'ru-pud', 'pt-gsd', 'es-ancora', 'mr-ufal', 'zh-hk', 'ru-taiga', 'de-hdt', 'id-pud', 'en-pronouns', 'it-pud', 'es-gsd', 'fi-ftb', 'tr-pud', 'fr-fqb', 'zh-gsd', 'el-gdt', 'et-edt', 'fr-spoken', 'zh-cfl', 'et-ewt', 'ja-modern']
+_URL = "https://huggingface.co/udpos/"
+_URLS = {}
+for lang in _LANGS:
+    _URLS[lang] = {
+        'train': _URL + lang + '-train.tsv'
+        'valid': _URL + lang + '-valid.tsv'
+        'test': _URL + lang + '-test.tsv'
+    }
+
+class Udpos(datasets.GeneratorBasedBuilder):
+    """POS Tagging on the Universal Dependencies 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 = []
+    for lang in _LANGS:
+        datasets.BuilderConfig(name=lang, version=VERSION, description="Split corresponding to language "+lang)
+
+    # DEFAULT_CONFIG_NAME = "first_domain"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "sentence": datasets.Value("string"),
+                "tags": 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, uncomment supervised_keys line below and
+            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
+            supervised_keys=("sentence", "tags"),
+            # 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):
+        # 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
+        urls = _URLS
+        data_dir = dl_manager.download_and_extract(urls)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, self.config.name+"-train.tsv"),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, self.config.name+"-test.tsv"),
+                    "split": "test"
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, self.config.name+"-dev.tsv"),
+                    "split": "dev",
+                },
+            ),
+        ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, filepath, 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.
+        with csv.reader(open(filepath, encoding="utf-8"), delimiter='\t') as f:
+            for key, row in enumerate(f):
+                yield key, {
+                    "sentence": row[0],
+                    "tags": row[1],
+                }