Update files from the datasets library (from 1.2.0)

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

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README.md

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+---
+annotations_creators:
+- expert-generated
+language_creators:
+- found
+languages:
+  ca:
+  - ca
+  eu:
+  - eu
+licenses:
+- cc-by-3-0
+multilinguality:
+- monolingual
+size_categories:
+- n<1K
+source_datasets:
+- original
+task_categories:
+- text-classification
+task_ids:
+- sentiment-classification
+---
+
+# Dataset Card for MultiBooked
+
+## 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:** http://hdl.handle.net/10230/33928
+- **Repository:** https://github.com/jerbarnes/multibooked
+- **Paper:** https://arxiv.org/abs/1803.08614
+- **Leaderboard:**
+- **Point of Contact:**
+
+### Dataset Summary
+
+MultiBooked is a corpus of Basque and Catalan Hotel Reviews Annotated for Aspect-level Sentiment Classification.
+
+The corpora are compiled from hotel reviews taken mainly from booking.com. The corpora are in Kaf/Naf format, which is
+an xml-style stand-off format that allows for multiple layers of annotation. Each review was sentence- and
+word-tokenized and lemmatized using Freeling for Catalan and ixa-pipes for Basque. Finally, for each language two
+annotators annotated opinion holders, opinion targets, and opinion expressions for each review, following the
+guidelines set out in the OpeNER project.
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+Each sub-dataset is monolingual in the languages:
+- ca: Catalan
+- eu: Basque
+
+## Dataset Structure
+
+### Data Instances
+
+[More Information Needed]
+
+### Data Fields
+
+- `text`: layer of the original text.
+  - `wid`: list of word IDs for each word within the example.
+  - `sent`: list of sentence IDs for each sentence within the example.
+  - `para`: list of paragraph IDs for each paragraph within the example.
+  - `word`: list of words.
+- `terms`: layer of the terms resulting from the analysis of the original text (lemmatization, morphological,
+  PoS tagging)
+  - `tid`: list of term IDs for each term within the example.
+  - `lemma`: list of lemmas.
+  - `morphofeat`: list of morphological features.
+  - `pos`: list of PoS tags.
+  - `target`: list of sublists of the corresponding word IDs (normally, the sublists contain only one element,
+    in a one-to-one correspondence between words and terms).
+- `opinions`: layer of the opinions in the text.
+  - `oid`: list of opinion IDs
+  - `opinion_holder_target`: list of sublists of the corresponding term IDs that span the opinion holder.
+  - `opinion_target_target`: list of sublists of the corresponding term IDs that span the opinion target.
+  - `opinion_expression_polarity`: list of the opinion expression polarities. The polarity can take one of the values:
+    `StrongNegative`, `Negative`, `Positive`, or `StrongPositive`.
+  - `opinion_expression_target`: list of sublists of the corresponding term IDs that span the opinion expression.
+
+### 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
+
+Dataset is under the [CC-BY 3.0](https://creativecommons.org/licenses/by/3.0/) license.
+
+### Citation Information
+
+```
+@inproceedings{Barnes2018multibooked,
+    author={Barnes, Jeremy and Lambert, Patrik and Badia, Toni},
+    title={MultiBooked: A corpus of Basque and Catalan Hotel Reviews Annotated for Aspect-level Sentiment Classification},
+    booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC'18)},
+    year = {2018},
+    month = {May},
+    date = {7-12},
+    address = {Miyazaki, Japan},
+    publisher = {European Language Resources Association (ELRA)},
+    language = {english}
+}
+```

dataset_infos.json

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+{"ca": {"description": "MultiBooked is a corpus of Basque and Catalan Hotel Reviews Annotated for Aspect-level Sentiment Classification.\n\nThe corpora are compiled from hotel reviews taken mainly from booking.com. The corpora are in Kaf/Naf format, which is\nan xml-style stand-off format that allows for multiple layers of annotation. Each review was sentence- and\nword-tokenized and lemmatized using Freeling for Catalan and ixa-pipes for Basque. Finally, for each language two\nannotators annotated opinion holders, opinion targets, and opinion expressions for each review, following the\nguidelines set out in the OpeNER project.\n", "citation": "@inproceedings{Barnes2018multibooked,\n    author={Barnes, Jeremy and Lambert, Patrik and Badia, Toni},\n    title={MultiBooked: A corpus of Basque and Catalan Hotel Reviews Annotated for Aspect-level Sentiment Classification},\n    booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC'18)},\n    year = {2018},\n    month = {May},\n    date = {7-12},\n    address = {Miyazaki, Japan},\n    publisher = {European Language Resources Association (ELRA)},\n    language = {english}\n}\n", "homepage": "http://hdl.handle.net/10230/33928", "license": "CC-BY 3.0", "features": {"text": {"feature": {"wid": {"dtype": "string", "id": null, "_type": "Value"}, "sent": {"dtype": "string", "id": null, "_type": "Value"}, "para": {"dtype": "string", "id": null, "_type": "Value"}, "word": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}, "terms": {"feature": {"tid": {"dtype": "string", "id": null, "_type": "Value"}, "lemma": {"dtype": "string", "id": null, "_type": "Value"}, "morphofeat": {"dtype": "string", "id": null, "_type": "Value"}, "pos": {"dtype": "string", "id": null, "_type": "Value"}, "target": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}, "opinions": {"feature": {"oid": {"dtype": "string", "id": null, "_type": "Value"}, "opinion_holder_target": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "opinion_target_target": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "opinion_expression_polarity": {"num_classes": 4, "names": ["StrongNegative", "Negative", "Positive", "StrongPositive"], "names_file": null, "id": null, "_type": "ClassLabel"}, "opinion_expression_target": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "multi_booked", "config_name": "ca", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 1952731, "num_examples": 567, "dataset_name": "multi_booked"}}, "download_checksums": {"https://github.com/jerbarnes/multibooked/archive/master.zip": {"num_bytes": 4429415, "checksum": "c9512b7fe0e5afc690aaf0205d61bc37e68836e008db05f17643a0de57007c80"}}, "download_size": 4429415, "post_processing_size": null, "dataset_size": 1952731, "size_in_bytes": 6382146}, "eu": {"description": "MultiBooked is a corpus of Basque and Catalan Hotel Reviews Annotated for Aspect-level Sentiment Classification.\n\nThe corpora are compiled from hotel reviews taken mainly from booking.com. The corpora are in Kaf/Naf format, which is\nan xml-style stand-off format that allows for multiple layers of annotation. Each review was sentence- and\nword-tokenized and lemmatized using Freeling for Catalan and ixa-pipes for Basque. Finally, for each language two\nannotators annotated opinion holders, opinion targets, and opinion expressions for each review, following the\nguidelines set out in the OpeNER project.\n", "citation": "@inproceedings{Barnes2018multibooked,\n    author={Barnes, Jeremy and Lambert, Patrik and Badia, Toni},\n    title={MultiBooked: A corpus of Basque and Catalan Hotel Reviews Annotated for Aspect-level Sentiment Classification},\n    booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC'18)},\n    year = {2018},\n    month = {May},\n    date = {7-12},\n    address = {Miyazaki, Japan},\n    publisher = {European Language Resources Association (ELRA)},\n    language = {english}\n}\n", "homepage": "http://hdl.handle.net/10230/33928", "license": "CC-BY 3.0", "features": {"text": {"feature": {"wid": {"dtype": "string", "id": null, "_type": "Value"}, "sent": {"dtype": "string", "id": null, "_type": "Value"}, "para": {"dtype": "string", "id": null, "_type": "Value"}, "word": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}, "terms": {"feature": {"tid": {"dtype": "string", "id": null, "_type": "Value"}, "lemma": {"dtype": "string", "id": null, "_type": "Value"}, "morphofeat": {"dtype": "string", "id": null, "_type": "Value"}, "pos": {"dtype": "string", "id": null, "_type": "Value"}, "target": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}, "opinions": {"feature": {"oid": {"dtype": "string", "id": null, "_type": "Value"}, "opinion_holder_target": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "opinion_target_target": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "opinion_expression_polarity": {"num_classes": 4, "names": ["StrongNegative", "Negative", "Positive", "StrongPositive"], "names_file": null, "id": null, "_type": "ClassLabel"}, "opinion_expression_target": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "multi_booked", "config_name": "eu", "version": "0.0.0", "splits": {"train": {"name": "train", "num_bytes": 1175816, "num_examples": 343, "dataset_name": "multi_booked"}}, "download_checksums": {"https://github.com/jerbarnes/multibooked/archive/master.zip": {"num_bytes": 4429415, "checksum": "c9512b7fe0e5afc690aaf0205d61bc37e68836e008db05f17643a0de57007c80"}}, "download_size": 4429415, "post_processing_size": null, "dataset_size": 1175816, "size_in_bytes": 5605231}}

dummy/ca/0.0.0/dummy_data.zip

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+size 6433

dummy/eu/0.0.0/dummy_data.zip

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+size 2496

multi_booked.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.
+"""MultiBooked dataset."""
+
+import os
+import xml.etree.ElementTree as ET
+from collections import defaultdict
+from pathlib import Path
+
+import datasets
+
+
+_CITATION = """\
+@inproceedings{Barnes2018multibooked,
+    author={Barnes, Jeremy and Lambert, Patrik and Badia, Toni},
+    title={MultiBooked: A corpus of Basque and Catalan Hotel Reviews Annotated for Aspect-level Sentiment Classification},
+    booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC'18)},
+    year = {2018},
+    month = {May},
+    date = {7-12},
+    address = {Miyazaki, Japan},
+    publisher = {European Language Resources Association (ELRA)},
+    language = {english}
+}
+"""
+
+_DESCRIPTION = """\
+MultiBooked is a corpus of Basque and Catalan Hotel Reviews Annotated for Aspect-level Sentiment Classification.
+
+The corpora are compiled from hotel reviews taken mainly from booking.com. The corpora are in Kaf/Naf format, which is
+an xml-style stand-off format that allows for multiple layers of annotation. Each review was sentence- and
+word-tokenized and lemmatized using Freeling for Catalan and ixa-pipes for Basque. Finally, for each language two
+annotators annotated opinion holders, opinion targets, and opinion expressions for each review, following the
+guidelines set out in the OpeNER project.
+"""
+
+_HOMEPAGE = "http://hdl.handle.net/10230/33928"
+
+_LICENSE = "CC-BY 3.0"
+
+_URL = "https://github.com/jerbarnes/multibooked/archive/master.zip"
+
+
+class MultiBooked(datasets.GeneratorBasedBuilder):
+    """MultiBooked dataset."""
+
+    VERSION = datasets.Version("0.0.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="ca", description="MultiBooked dataset in Catalan language."),
+        datasets.BuilderConfig(name="eu", description="MultiBooked dataset in Basque language."),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "text": datasets.features.Sequence(
+                        {
+                            "wid": datasets.Value("string"),
+                            "sent": datasets.Value("string"),
+                            "para": datasets.Value("string"),
+                            "word": datasets.Value("string"),
+                        }
+                    ),
+                    "terms": datasets.features.Sequence(
+                        {
+                            "tid": datasets.Value("string"),
+                            "lemma": datasets.Value("string"),
+                            "morphofeat": datasets.Value("string"),
+                            "pos": datasets.Value("string"),
+                            "target": datasets.features.Sequence(datasets.Value("string")),
+                        }
+                    ),
+                    "opinions": datasets.features.Sequence(
+                        {
+                            "oid": datasets.Value("string"),
+                            "opinion_holder_target": datasets.features.Sequence(datasets.Value("string")),
+                            "opinion_target_target": datasets.features.Sequence(datasets.Value("string")),
+                            "opinion_expression_polarity": datasets.features.ClassLabel(
+                                names=["StrongNegative", "Negative", "Positive", "StrongPositive"]
+                            ),
+                            "opinion_expression_target": datasets.features.Sequence(datasets.Value("string")),
+                        }
+                    ),
+                }
+            ),
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        data_dir = dl_manager.download_and_extract(_URL)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "dirpath": os.path.join(data_dir, "multibooked-master", "corpora", self.config.name),
+                },
+            ),
+        ]
+
+    def _generate_examples(self, dirpath):
+        for id_, filepath in enumerate(sorted(Path(dirpath).iterdir())):
+            example = defaultdict(lambda: defaultdict(list))
+            with open(filepath, encoding="utf-8") as f:
+                for _, elem in ET.iterparse(f):
+                    if elem.tag == "text":
+                        for child in elem:
+                            # sometimes wid is missing in the eu configuration
+                            example["text"]["wid"].append(child.attrib.get("wid", ""))
+                            example["text"]["sent"].append(child.attrib["sent"])
+                            example["text"]["para"].append(child.attrib["para"])
+                            example["text"]["word"].append(child.text)
+                    elif elem.tag == "terms":
+                        for child in elem:
+                            # sometimes tid is missing in the eu configuration
+                            example["terms"]["tid"].append(child.attrib.get("tid", ""))
+                            example["terms"]["lemma"].append(child.attrib["lemma"])
+                            example["terms"]["morphofeat"].append(child.attrib["morphofeat"])
+                            example["terms"]["pos"].append(child.attrib["pos"])
+                            targets = []
+                            for target in child.findall("span/target"):
+                                targets.append(target.attrib["id"])
+                            example["terms"]["target"].append(targets)
+                    elif elem.tag == "opinions":
+                        for child in elem:
+                            example["opinions"]["oid"].append(child.attrib["oid"])
+                            # Opinion holder
+                            opinion_holder = child.find("opinion_holder")
+                            targets = []
+                            for target in opinion_holder.findall("span/target"):
+                                targets.append(target.attrib["id"])
+                            example["opinions"]["opinion_holder_target"].append(targets)
+                            # Opinion target
+                            opinion_target = child.find("opinion_target")
+                            targets = []
+                            for target in opinion_target.findall("span/target"):
+                                targets.append(target.attrib["id"])
+                            example["opinions"]["opinion_target_target"].append(targets)
+                            # Opinion expression
+                            opinion_expression = child.find("opinion_expression")
+                            example["opinions"]["opinion_expression_polarity"].append(
+                                opinion_expression.attrib["polarity"]
+                            )
+                            targets = []
+                            for target in opinion_expression.findall("span/target"):
+                                targets.append(target.attrib["id"])
+                            example["opinions"]["opinion_expression_target"].append(targets)
+            yield id_, example