Update files from the datasets library (from 1.2.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.2.0

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bin.* filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+annotations_creators:
+  raw_ca:
+  - no-annotation
+  raw_en:
+  - no-annotation
+  raw_es:
+  - no-annotation
+  tagged_ca:
+  - machine-generated
+  tagged_en:
+  - machine-generated
+  tagged_es:
+  - machine-generated
+language_creators:
+- found
+languages:
+  raw_ca:
+  - ca
+  raw_en:
+  - en
+  raw_es:
+  - es
+  tagged_ca:
+  - ca
+  tagged_en:
+  - en
+  tagged_es:
+  - es
+licenses:
+- gfdl-1-1
+multilinguality:
+- monolingual
+size_categories:
+  raw_ca:
+  - 100K<n<1M
+  raw_en:
+  - n>1M
+  raw_es:
+  - 100K<n<1M
+  tagged_ca:
+  - n>1M
+  tagged_en:
+  - n>1M
+  tagged_es:
+  - n>1M
+source_datasets:
+- original
+task_categories:
+  raw_ca:
+  - sequence-modeling
+  raw_en:
+  - sequence-modeling
+  raw_es:
+  - sequence-modeling
+  tagged_ca:
+  - structure-prediction
+  - text-classification
+  tagged_en:
+  - structure-prediction
+  - text-classification
+  tagged_es:
+  - structure-prediction
+  - text-classification
+task_ids:
+  raw_ca:
+  - language-modeling
+  raw_en:
+  - language-modeling
+  raw_es:
+  - language-modeling
+  tagged_ca:
+  - structure-prediction-other-lemmatization
+  - structure-prediction-other-pos-tagging
+  - text-classification-other-word-sense-disambiguation
+  tagged_en:
+  - structure-prediction-other-lemmatization
+  - structure-prediction-other-pos-tagging
+  - text-classification-other-word-sense-disambiguation
+  tagged_es:
+  - structure-prediction-other-lemmatization
+  - structure-prediction-other-pos-tagging
+  - text-classification-other-word-sense-disambiguation
+---
+
+# Dataset Card for Wikicorpus
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-instances)
+  - [Data Splits](#data-instances)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+
+## Dataset Description
+
+- **Homepage:** https://www.cs.upc.edu/~nlp/wikicorpus/
+- **Repository:**
+- **Paper:** https://www.cs.upc.edu/~nlp/papers/reese10.pdf
+- **Leaderboard:**
+- **Point of Contact:**
+
+### Dataset Summary
+
+The Wikicorpus is a trilingual corpus (Catalan, Spanish, English) that contains large portions of the Wikipedia (based on a 2006 dump) and has been automatically enriched with linguistic information. In its present version, it contains over 750 million words.
+
+The corpora have been annotated with lemma and part of speech information using the open source library FreeLing. Also, they have been sense annotated with the state of the art Word Sense Disambiguation algorithm UKB. As UKB assigns WordNet senses, and WordNet has been aligned across languages via the InterLingual Index, this sort of annotation opens the way to massive explorations in lexical semantics that were not possible before.
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+Each sub-dataset is monolingual in the languages:
+- ca: Catalan
+- en: English
+- es: Spanish
+
+## Dataset Structure
+
+### Data Instances
+
+[More Information Needed]
+
+### Data Fields
+
+[More Information Needed]
+
+### Data Splits
+
+[More Information Needed]
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+The WikiCorpus is licensed under the same license as Wikipedia, that is, the [GNU Free Documentation License](http://www.fsf.org/licensing/licenses/fdl.html)
+
+### Citation Information
+
+```
+@inproceedings{reese-etal-2010-wikicorpus,
+    title = "{W}ikicorpus: A Word-Sense Disambiguated Multilingual {W}ikipedia Corpus",
+    author = "Reese, Samuel  and
+      Boleda, Gemma  and
+      Cuadros, Montse  and
+      Padr{\'o}, Llu{\'i}s  and
+      Rigau, German",
+    booktitle = "Proceedings of the Seventh International Conference on Language Resources and Evaluation ({LREC}'10)",
+    month = may,
+    year = "2010",
+    address = "Valletta, Malta",
+    publisher = "European Language Resources Association (ELRA)",
+    url = "http://www.lrec-conf.org/proceedings/lrec2010/pdf/222_Paper.pdf",
+    abstract = "This article presents a new freely available trilingual corpus (Catalan, Spanish, English) that contains large portions of the Wikipedia and has been automatically enriched with linguistic information. To our knowledge, this is the largest such corpus that is freely available to the community: In its present version, it contains over 750 million words. The corpora have been annotated with lemma and part of speech information using the open source library FreeLing. Also, they have been sense annotated with the state of the art Word Sense Disambiguation algorithm UKB. As UKB assigns WordNet senses, and WordNet has been aligned across languages via the InterLingual Index, this sort of annotation opens the way to massive explorations in lexical semantics that were not possible before. We present a first attempt at creating a trilingual lexical resource from the sense-tagged Wikipedia corpora, namely, WikiNet. Moreover, we present two by-products of the project that are of use for the NLP community: An open source Java-based parser for Wikipedia pages developed for the construction of the corpus, and the integration of the WSD algorithm UKB in FreeLing.",
+}
+```

dataset_infos.json

@@ -0,0 +1 @@
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wikicorpus.py

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+# 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.
+"""Wikicorpus dataset."""
+
+import re
+from pathlib import Path
+
+import datasets
+
+
+_CITATION = """\
+@inproceedings{reese-etal-2010-wikicorpus,
+    title = "{W}ikicorpus: A Word-Sense Disambiguated Multilingual {W}ikipedia Corpus",
+    author = "Reese, Samuel  and
+      Boleda, Gemma  and
+      Cuadros, Montse  and
+      Padr{\'o}, Llu{\'i}s  and
+      Rigau, German",
+    booktitle = "Proceedings of the Seventh International Conference on Language Resources and Evaluation ({LREC}'10)",
+    month = may,
+    year = "2010",
+    address = "Valletta, Malta",
+    publisher = "European Language Resources Association (ELRA)",
+    url = "http://www.lrec-conf.org/proceedings/lrec2010/pdf/222_Paper.pdf",
+    abstract = "This article presents a new freely available trilingual corpus (Catalan, Spanish, English) that contains large portions of the Wikipedia and has been automatically enriched with linguistic information. To our knowledge, this is the largest such corpus that is freely available to the community: In its present version, it contains over 750 million words. The corpora have been annotated with lemma and part of speech information using the open source library FreeLing. Also, they have been sense annotated with the state of the art Word Sense Disambiguation algorithm UKB. As UKB assigns WordNet senses, and WordNet has been aligned across languages via the InterLingual Index, this sort of annotation opens the way to massive explorations in lexical semantics that were not possible before. We present a first attempt at creating a trilingual lexical resource from the sense-tagged Wikipedia corpora, namely, WikiNet. Moreover, we present two by-products of the project that are of use for the NLP community: An open source Java-based parser for Wikipedia pages developed for the construction of the corpus, and the integration of the WSD algorithm UKB in FreeLing.",
+}
+"""
+
+_DESCRIPTION = """\
+The Wikicorpus is a trilingual corpus (Catalan, Spanish, English) that contains large portions of the Wikipedia (based on a 2006 dump) and has been automatically enriched with linguistic information. In its present version, it contains over 750 million words.
+"""
+
+_HOMEPAGE = "https://www.cs.upc.edu/~nlp/wikicorpus/"
+
+_LICENSE = "GNU Free Documentation License"
+
+_URLs = "https://www.cs.upc.edu/~nlp/wikicorpus/{form}.{language}.tgz"
+
+_LANGUAGES = ["ca", "es", "en"]
+_FORMS = ["raw", "tagged"]
+
+METADATA_PATTERN = re.compile(r'.+id="(?P<id>[^"]+)".+title="(?P<title>[^"]+)".+')
+
+
+class WikicorpusConfig(datasets.BuilderConfig):
+    """ BuilderConfig for Wikicorpus."""
+
+    def __init__(self, form=None, language=None, **kwargs):
+        """
+        Args:
+            form: form of the dataset.
+            language: language of the dataset.
+            **kwargs: keyword arguments forwarded to super.
+        """
+        super().__init__(
+            name=f"{form}_{language}",
+            description=f"Wikicorpus dataset in {form} form and {language} language.",
+            **kwargs,
+        )
+        self.form = form
+        self.language = language
+
+
+class Wikicorpus(datasets.GeneratorBasedBuilder):
+    """Wikicorpus dataset."""
+
+    VERSION = datasets.Version("1.0.0")
+    BUILDER_CONFIG_CLASS = WikicorpusConfig
+    BUILDER_CONFIGS = [WikicorpusConfig(form=form, language=language) for form in _FORMS for language in _LANGUAGES]
+
+    def _info(self):
+        if self.config.form == "raw":
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "title": datasets.Value("string"),
+                    "text": datasets.Value("string"),
+                }
+            )
+        elif self.config.form == "tagged":
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "title": datasets.Value("string"),
+                    "sentence": datasets.Sequence(datasets.Value("string")),
+                    "lemmas": datasets.Sequence(datasets.Value("string")),
+                    "pos_tags": datasets.Sequence(datasets.Value("string")),
+                    "wordnet_senses": datasets.Sequence(datasets.Value("string")),
+                }
+            )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        url_to_download = _URLs.format(form=self.config.form, language=self.config.language)
+        downloaded_dir = dl_manager.download_and_extract(url_to_download)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "dirpath": downloaded_dir,
+                },
+            ),
+        ]
+
+    def _generate_examples(self, dirpath):
+        for filepath in sorted(Path(dirpath).iterdir()):
+            with open(filepath, encoding="latin-1") as f:
+                example = {}
+                # raw
+                text = []
+                # tagged
+                words = []
+                lemmas = []
+                pos_tags = []
+                wordnet_senses = []
+                for id_, row in enumerate(f):
+                    if self.config.form == "raw":
+                        if row.startswith("<doc id"):
+                            metadata_match = METADATA_PATTERN.match(row)
+                            example["id"] = metadata_match.group("id") if metadata_match else ""
+                            example["title"] = metadata_match.group("title") if metadata_match else ""
+                        elif row.startswith("</doc>"):
+                            pass
+                        elif row.startswith("ENDOFARTICLE"):
+                            yield id_, {
+                                "id": example["id"],
+                                "title": example["title"],
+                                "text": "\n".join(text).strip(),
+                            }
+                            example = {}
+                            text = []
+                        else:
+                            text.append(row)
+                    elif self.config.form == "tagged":
+                        if row.startswith("<doc id"):
+                            metadata_match = METADATA_PATTERN.match(row)
+                            example["id"] = metadata_match.group("id") if metadata_match else ""
+                            example["title"] = metadata_match.group("title") if metadata_match else ""
+                        elif row.startswith("</doc>"):
+                            pass
+                        elif row.startswith("ENDOFARTICLE") or row.startswith("\n"):
+                            if len(words) > 1:  # some content besides only (. . Fp 0)
+                                yield id_, {
+                                    "id": example["id"],
+                                    "title": example["title"],
+                                    "sentence": words,
+                                    "lemmas": lemmas,
+                                    "pos_tags": pos_tags,
+                                    "wordnet_senses": wordnet_senses,
+                                }
+                            words = []
+                            lemmas = []
+                            pos_tags = []
+                            wordnet_senses = []
+                            if row.startswith("ENDOFARTICLE"):
+                                example = {}
+                        else:
+                            splits = row.split()
+                            for tag, tags in zip(splits, [words, lemmas, pos_tags, wordnet_senses]):
+                                tags.append(tag)