Update files from the datasets library (from 1.4.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.4.0

.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+annotations_creators:
+- crowdsourced
+- expert-generated
+- machine-generated
+language_creators:
+- crowdsourced
+- expert-generated
+- machine-generated
+languages:
+- af
+- ar
+- az
+- be
+- bg
+- bn
+- ca
+- ceb
+- cs
+- cy
+- da
+- de
+- el
+- en
+- es
+- et
+- eu
+- fa
+- fi
+- fr
+- ga
+- gl
+- he
+- hi
+- hr
+- hu
+- hy
+- id
+- it
+- ja
+- ka
+- ko
+- la
+- lt
+- lv
+- ms
+- nl
+- pl
+- pt
+- ro
+- ru
+- sk
+- sl
+- sq
+- sr
+- sv
+- ta
+- th
+- tr
+- uk
+- ur
+- vi
+- zh
+licenses:
+- cc-by-nc-sa-4-0
+multilinguality:
+- translation
+size_categories:
+- 1M>n>100K
+source_datasets:
+- extended|lama
+task_categories:
+- question-answering
+- text-scoring
+task_ids:
+- open-domain-qa
+- text-scoring-other-probing
+---
+
+# Dataset Card for [Dataset Name]
+
+## Table of Contents
+- [Dataset Card for [Dataset Name]](#dataset-card-for-dataset-name)
+  - [Table of Contents](#table-of-contents)
+  - [Dataset Description](#dataset-description)
+    - [Dataset Summary](#dataset-summary)
+    - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+    - [Languages](#languages)
+  - [Dataset Structure](#dataset-structure)
+    - [Data Instances](#data-instances)
+    - [Data Fields](#data-fields)
+    - [Data Splits](#data-splits)
+  - [Dataset Creation](#dataset-creation)
+    - [Curation Rationale](#curation-rationale)
+    - [Source Data](#source-data)
+      - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
+      - [Who are the source language producers?](#who-are-the-source-language-producers)
+    - [Annotations](#annotations)
+      - [Annotation process](#annotation-process)
+      - [Who are the annotators?](#who-are-the-annotators)
+    - [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)
+    - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:** [Multilingual LAMA](http://cistern.cis.lmu.de/mlama/)
+- **Repository:** [Github](https://github.com/norakassner/mlama)
+- **Paper:** [Arxiv](https://arxiv.org/abs/2102.00894)
+- **Point of Contact:** [Contact section](http://cistern.cis.lmu.de/mlama/)
+
+
+
+### Dataset Summary
+
+This dataset provides the data for mLAMA, a multilingual version of LAMA. 
+Regarding LAMA see https://github.com/facebookresearch/LAMA. For mLAMA
+the TREx and GoogleRE part of LAMA was considered and machine translated using 
+Google Translate, and the Wikidata and Google Knowledge Graph API. The machine
+translated templates were checked for validity, i.e., whether they contain 
+exactly one '[X]' and one '[Y]'.
+
+This data can be used for creating fill-in-the-blank queries like 
+"Paris is the capital of [MASK]" across 53 languages. For more details see
+the website http://cistern.cis.lmu.de/mlama/ or the github repo https://github.com/norakassner/mlama.
+
+### Supported Tasks and Leaderboards
+
+Language model knowledge probing.
+
+### Languages
+
+This dataset contains data in 53 languages: 
+af,ar,az,be,bg,bn,ca,ceb,cs,cy,da,de,el,en,es,et,eu,fa,fi,fr,ga,gl,he,hi,hr,hu,hy,id,it,ja,ka,ko,la,lt,lv,ms,nl,pl,pt,ro,ru,sk,sl,sq,sr,sv,ta,th,tr,uk,ur,vi,zh
+
+## Dataset Structure
+For each of the 53 languages and each of the 43 relations/predicates there is a set of triples.
+
+### Data Instances
+For each language and relation there are triples, that consists of an object, a predicate and a subject. For each predicate there is a template available. An example for `dataset["test"][0]` is given here:
+```python
+{
+'language': 'af',
+'lineid': 0, 
+'obj_label': 'Frankryk', 
+'obj_uri': 'Q142', 
+'predicate_id': 'P1001', 
+'sub_label': 'President van Frankryk', 
+'sub_uri': 'Q191954', 
+'template': "[X] is 'n wettige term in [Y].", 
+'uuid': '3fe3d4da-9df9-45ba-8109-784ce5fba38a'
+}
+```
+
+
+### Data Fields
+
+Each instance has the following fields
+* "uuid": a unique identifier
+* "lineid": a identifier unique to mlama
+* "obj_id": knowledge graph id of the object
+* "obj_label": surface form of the object
+* "sub_id": knowledge graph id of the subject
+* "sub_label": surface form of the subject
+* "template": template
+* "language": language code
+* "predicate_id": relation id
+
+
+### Data Splits
+
+There is only one partition that is labelled as 'test data'.
+
+## Dataset Creation
+
+### Curation Rationale
+
+The dataset was translated into 53 languages to investigate knowledge in pretrained language models
+multilingually.
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+The data has several sources: 
+
+LAMA (https://github.com/facebookresearch/LAMA) licensed under Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
+T-REx (https://hadyelsahar.github.io/t-rex/) licensed under Creative Commons Attribution-ShareAlike 4.0 International License
+Google-RE (https://github.com/google-research-datasets/relation-extraction-corpus)
+Wikidata (https://www.wikidata.org/) licensed under Creative Commons CC0 License and Creative Commons Attribution-ShareAlike License
+
+#### Who are the source language producers?
+
+See links above. 
+
+### Annotations
+
+#### Annotation process
+
+Crowdsourced (wikidata) and machine translated.
+
+#### Who are the annotators?
+
+Unknown. 
+
+### Personal and Sensitive Information
+
+Names of (most likely) famous people who have entries in Google Knowledge Graph or Wikidata.
+
+## Considerations for Using the Data
+
+Data was created through machine translation and automatic processes.
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+Not all triples are available in all languages.
+
+
+## Additional Information
+
+### Dataset Curators
+
+The authors of the mLAMA paper and the authors of the original datasets.
+
+### Licensing Information
+
+The Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0). https://creativecommons.org/licenses/by-nc-sa/4.0/
+
+### Citation Information
+
+```
+@article{kassner2021multilingual,
+  author    = {Nora Kassner and
+               Philipp Dufter and
+               Hinrich Sch{\"{u}}tze},
+  title     = {Multilingual {LAMA:} Investigating Knowledge in Multilingual Pretrained
+               Language Models},
+  journal   = {CoRR},
+  volume    = {abs/2102.00894},
+  year      = {2021},
+  url       = {https://arxiv.org/abs/2102.00894},
+  archivePrefix = {arXiv},
+  eprint    = {2102.00894},
+  timestamp = {Tue, 09 Feb 2021 13:35:56 +0100},
+  biburl    = {https://dblp.org/rec/journals/corr/abs-2102-00894.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org},
+  note      = {to appear in EACL2021}
+}
+```
+
+### Contributions
+
+Thanks to [@pdufter](https://github.com/pdufter) for adding this dataset.

dataset_infos.json

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+{"all": {"description": "mLAMA: a multilingual version of the LAMA benchmark (T-REx and GoogleRE) covering 53 languages.", "citation": "\n@article{kassner2021multilingual,\n  author    = {Nora Kassner and\n               Philipp Dufter and\n               Hinrich Sch{\"{u}}tze},\n  title     = {Multilingual {LAMA:} Investigating Knowledge in Multilingual Pretrained\n               Language Models},\n  journal   = {CoRR},\n  volume    = {abs/2102.00894},\n  year      = {2021},\n  url       = {https://arxiv.org/abs/2102.00894},\n  archivePrefix = {arXiv},\n  eprint    = {2102.00894},\n  timestamp = {Tue, 09 Feb 2021 13:35:56 +0100},\n  biburl    = {https://dblp.org/rec/journals/corr/abs-2102-00894.bib},\n  bibsource = {dblp computer science bibliography, https://dblp.org},\n  note      = {to appear in EACL2021}\n}\n", "homepage": "http://cistern.cis.lmu.de/mlama/", "license": "The Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0). https://creativecommons.org/licenses/by-nc-sa/4.0/", "features": {"uuid": {"dtype": "string", "id": null, "_type": "Value"}, "lineid": {"dtype": "uint32", "id": null, "_type": "Value"}, "obj_uri": {"dtype": "string", "id": null, "_type": "Value"}, "obj_label": {"dtype": "string", "id": null, "_type": "Value"}, "sub_uri": {"dtype": "string", "id": null, "_type": "Value"}, "sub_label": {"dtype": "string", "id": null, "_type": "Value"}, "template": {"dtype": "string", "id": null, "_type": "Value"}, "language": {"dtype": "string", "id": null, "_type": "Value"}, "predicate_id": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "m_lama", "config_name": "all", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"test": {"name": "test", "num_bytes": 125919995, "num_examples": 843143, "dataset_name": "m_lama"}}, "download_checksums": {"http://cistern.cis.lmu.de/mlama/mlama1.1.zip": {"num_bytes": 40772287, "checksum": "043dc82b1b4b72de10ec98fb3a75341af13a1b439f6ee8e769398f42bd6d5883"}}, "download_size": 40772287, "post_processing_size": null, "dataset_size": 125919995, "size_in_bytes": 166692282}}

dummy/all/1.1.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:88f979f6051f015349ff51138ae78f590791c3f5a981129ed45b996b494aa4c0
+size 699202

m_lama.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.
+"""The mLAMA Dataset"""
+
+from __future__ import absolute_import, division, print_function
+
+import json
+import os
+
+import datasets
+
+
+_CITATION = """
+@article{kassner2021multilingual,
+  author    = {Nora Kassner and
+               Philipp Dufter and
+               Hinrich Sch{\"{u}}tze},
+  title     = {Multilingual {LAMA:} Investigating Knowledge in Multilingual Pretrained
+               Language Models},
+  journal   = {CoRR},
+  volume    = {abs/2102.00894},
+  year      = {2021},
+  url       = {https://arxiv.org/abs/2102.00894},
+  archivePrefix = {arXiv},
+  eprint    = {2102.00894},
+  timestamp = {Tue, 09 Feb 2021 13:35:56 +0100},
+  biburl    = {https://dblp.org/rec/journals/corr/abs-2102-00894.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org},
+  note      = {to appear in EACL2021}
+}
+"""
+
+
+_DESCRIPTION = """mLAMA: a multilingual version of the LAMA benchmark (T-REx and GoogleRE) covering 53 languages."""
+
+_HOMEPAGE = "http://cistern.cis.lmu.de/mlama/"
+
+_LICENSE = "The Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0). https://creativecommons.org/licenses/by-nc-sa/4.0/"
+
+_URL = "http://cistern.cis.lmu.de/mlama/mlama1.1.zip"
+
+_LANGUAGES = (
+    "af",
+    "ar",
+    "az",
+    "be",
+    "bg",
+    "bn",
+    "ca",
+    "ceb",
+    "cs",
+    "cy",
+    "da",
+    "de",
+    "el",
+    "en",
+    "es",
+    "et",
+    "eu",
+    "fa",
+    "fi",
+    "fr",
+    "ga",
+    "gl",
+    "he",
+    "hi",
+    "hr",
+    "hu",
+    "hy",
+    "id",
+    "it",
+    "ja",
+    "ka",
+    "ko",
+    "la",
+    "lt",
+    "lv",
+    "ms",
+    "nl",
+    "pl",
+    "pt",
+    "ro",
+    "ru",
+    "sk",
+    "sl",
+    "sq",
+    "sr",
+    "sv",
+    "ta",
+    "th",
+    "tr",
+    "uk",
+    "ur",
+    "vi",
+    "zh",
+)
+_RELATIONS = (
+    "place_of_birth",
+    "place_of_death",
+    "P1001",
+    "P101",
+    "P103",
+    "P106",
+    "P108",
+    "P127",
+    "P1303",
+    "P131",
+    "P136",
+    "P1376",
+    "P138",
+    "P140",
+    "P1412",
+    "P159",
+    "P17",
+    "P176",
+    "P178",
+    "P19",
+    "P190",
+    "P20",
+    "P264",
+    "P27",
+    "P276",
+    "P279",
+    "P30",
+    "P31",
+    "P36",
+    "P361",
+    "P364",
+    "P37",
+    "P39",
+    "P407",
+    "P413",
+    "P449",
+    "P463",
+    "P47",
+    "P495",
+    "P527",
+    "P530",
+    "P740",
+    "P937",
+)
+
+
+class MLamaConfig(datasets.BuilderConfig):
+    """BuilderConfig for mLAMA."""
+
+    def __init__(self, languages=None, relations=None, **kwargs):
+        """BuilderConfig for mLAMA.
+        Args:
+        languages: A subset of af,ar,az,be,bg,bn,ca,ceb,cs,cy,da,de,el,en,es,et,eu,fa,fi,fr,ga,gl,he,hi,hr,hu,hy,id,it,ja,ka,ko,la,lt,lv,ms,nl,pl,pt,ro,ru,sk,sl,sq,sr,sv,ta,th,tr,uk,ur,vi,zh
+        relations: A subset of place_of_birth,place_of_death,P1001,P101,P103,P106,P108,P127,P1303,P131,P136,P1376,P138,P140,P1412,P159,P17,P176,P178,P19,P190,P20,P264,P27,P276,P279,P30,P31,P36,P361,P364,P37,P39,P407,P413,P449,P463,P47,P495,P527,P530,P740,P937
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(MLamaConfig, self).__init__(**kwargs)
+        self.languages = languages if languages is not None else _LANGUAGES
+        self.relations = relations if relations is not None else _RELATIONS
+
+
+class MLama(datasets.GeneratorBasedBuilder):
+    """multilingual LAMA Dataset (mLAMA)"""
+
+    VERSION = datasets.Version("1.1.0")
+    BUILDER_CONFIG_CLASS = MLamaConfig
+    BUILDER_CONFIGS = [
+        MLamaConfig(
+            name="all",
+            languages=None,
+            relations=None,
+            version=datasets.Version("1.1.0"),
+            description="Import of mLAMA for all languages and all relations.",
+        )
+    ]
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "uuid": datasets.Value("string"),
+                "lineid": datasets.Value("uint32"),
+                "obj_uri": datasets.Value("string"),
+                "obj_label": datasets.Value("string"),
+                "sub_uri": datasets.Value("string"),
+                "sub_label": datasets.Value("string"),
+                "template": datasets.Value("string"),
+                "language": datasets.Value("string"),
+                "predicate_id": 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):
+        """Returns SplitGenerators."""
+        data_dir = dl_manager.download_and_extract(_URL)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "mlama1.1"),
+                    "split": "test",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath, split):
+        """ Yields examples from the mLAMA dataset. """
+        id_ = -1
+        for language in self.config.languages:
+            # load templates
+            templates = {}
+            with open(os.path.join(filepath, language, "templates.jsonl"), encoding="utf-8") as fp:
+                for line in fp:
+                    line = json.loads(line)
+                    templates[line["relation"]] = line["template"]
+            for relation in self.config.relations:
+                # load triples
+                with open(os.path.join(filepath, language, f"{relation}.jsonl"), encoding="utf-8") as fp:
+                    for line in fp:
+                        triple = json.loads(line)
+                        triple["language"] = language
+                        triple["predicate_id"] = relation
+                        triple["template"] = templates.get(relation, "")
+                        id_ += 1
+                        yield id_, triple