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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+*.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
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+*.lfs.* filter=lfs diff=lfs merge=lfs -text
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+*.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
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+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+annotations_creators:
+- expert-generated
+language_creators:
+- expert-generated
+languages:
+- en
+licenses:
+- mit
+multilinguality:
+- monolingual
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- structure-prediction
+task_ids:
+- coreference-resolution
+- named-entity-recognition
+- parsing
+---
+
+# Dataset Card for Wino_Bias dataset
+
+## 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:** [WinoBias](https://uclanlp.github.io/corefBias/overview)
+- **Repository:**
+- **Paper:** [Arxiv](https://arxiv.org/abs/1804.06876)
+- **Leaderboard:**
+- **Point of Contact:**
+
+### Dataset Summary
+
+WinoBias, a Winograd-schema dataset for coreference resolution focused on gender bias.
+The corpus contains Winograd-schema style sentences with entities corresponding to people
+referred by their occupation (e.g. the nurse, the doctor, the carpenter).
+
+### Supported Tasks and Leaderboards
+
+The underlying task is coreference resolution. But it also support NER and POS tasks.
+
+### Languages
+
+English
+
+## Dataset Structure
+
+### Data Instances
+
+[More Information Needed]
+
+### Data Fields
+
+    - document_id = This is a variation on the document filename
+    - part_number = Some files are divided into multiple parts numbered as 000, 001, 002, ... etc.
+    - word_num = This is the word index of the word in that sentence.
+    - tokens = This is the token as segmented/tokenized in the Treebank.
+    - pos_tags = This is the Penn Treebank style part of speech. When parse information is missing, all part of speeches except the one for which there is some sense or proposition annotation   are marked with a XX tag. The verb is marked with just a VERB tag.
+    - parse_bit = This is the bracketed structure broken before the first open parenthesis in the parse, and the word/part-of-speech leaf replaced with a *. The full parse can be created by substituting the asterix with the "([pos] [word])" string (or leaf) and concatenating the items in the rows of that column. When the parse information is missing, the first word of a sentence is tagged as "(TOP*" and the last word is tagged as "*)" and all intermediate words are tagged with a "*".
+    - predicate_lemma = The predicate lemma is mentioned for the rows for which we have semantic role information or word sense information. All other rows are marked with a "-".
+    - predicate_framenet_id = This is the PropBank frameset ID of the predicate in predicate_lemma.
+    - word_sense = This is the word sense of the word in Column tokens.
+    - speaker = This is the speaker or author name where available.
+    - ner_tags = These columns identifies the spans representing various named entities. For documents which do not have named entity annotation, each line is represented with an "*".
+    - verbal_predicates = There is one column each of predicate argument structure information for the predicate mentioned in predicate_lemma. If there are no predicates tagged in a sentence this is a single column with all rows marked with an "*".
+
+### Data Splits
+
+Single Split available
+
+## 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
+
+Gender Bias is discussed with the help of this dataset.
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+MIT Licence
+
+### Citation Information
+
+@article{DBLP:journals/corr/abs-1804-06876,
+  author    = {Jieyu Zhao and
+               Tianlu Wang and
+               Mark Yatskar and
+               Vicente Ordonez and
+               Kai{-}Wei Chang},
+  title     = {Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods},
+  journal   = {CoRR},
+  volume    = {abs/1804.06876},
+  year      = {2018},
+  url       = {http://arxiv.org/abs/1804.06876},
+  archivePrefix = {arXiv},
+  eprint    = {1804.06876},
+  timestamp = {Mon, 13 Aug 2018 16:47:01 +0200},
+  biburl    = {https://dblp.org/rec/journals/corr/abs-1804-06876.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}

dataset_infos.json

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+{"wino_bias": {"description": "WinoBias, a Winograd-schema dataset for coreference resolution focused on gender bias.\nThe corpus contains Winograd-schema style sentences with entities corresponding to people\nreferred by their occupation (e.g. the nurse, the doctor, the carpenter).\n", "citation": "@article{DBLP:journals/corr/abs-1804-06876,\n  author    = {Jieyu Zhao and\n               Tianlu Wang and\n               Mark Yatskar and\n               Vicente Ordonez and\n               Kai{-}Wei Chang},\n  title     = {Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods},\n  journal   = {CoRR},\n  volume    = {abs/1804.06876},\n  year      = {2018},\n  url       = {http://arxiv.org/abs/1804.06876},\n  archivePrefix = {arXiv},\n  eprint    = {1804.06876},\n  timestamp = {Mon, 13 Aug 2018 16:47:01 +0200},\n  biburl    = {https://dblp.org/rec/journals/corr/abs-1804-06876.bib},\n  bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n", "homepage": "https://uclanlp.github.io/corefBias/overview", "license": "MIT License (https://github.com/uclanlp/corefBias/blob/master/LICENSE)", "features": {"document_id": {"dtype": "string", "id": null, "_type": "Value"}, "part_number": {"dtype": "string", "id": null, "_type": "Value"}, "word_number": {"feature": {"dtype": "int32", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "tokens": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "pos_tags": {"feature": {"num_classes": 54, "names": ["\"", "''", "#", "$", "(", ")", ",", ".", ":", "``", "CC", "CD", "DT", "EX", "FW", "IN", "JJ", "JJR", "JJS", "LS", "MD", "NN", "NNP", "NNPS", "NNS", "NN|SYM", "PDT", "POS", "PRP", "PRP$", "RB", "RBR", "RBS", "RP", "SYM", "TO", "UH", "VB", "VBD", "VBG", "VBN", "VBP", "VBZ", "WDT", "WP", "WP$", "WRB", "HYPH", "XX", "NFP", "AFX", "ADD", "-LRB-", "-RRB-"], "names_file": null, "id": null, "_type": "ClassLabel"}, "length": -1, "id": null, "_type": "Sequence"}, "parse_bit": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "predicate_lemma": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "predicate_framenet_id": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "word_sense": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "speaker": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "ner_tags": {"feature": {"num_classes": 38, "names": ["B-PERSON", "I-PERSON", "B-NORP", "I-NORP", "B-FAC", "I-FAC", "B-ORG", "I-ORG", "B-GPE", "I-GPE", "B-LOC", "I-LOC", "B-PRODUCT", "I-PRODUCT", "B-EVENT", "I-EVENT", "B-WORK_OF_ART", "I-WORK_OF_ART", "B-LAW", "I-LAW", "B-LANGUAGE", "I-LANGUAGE", "B-DATE", "I-DATE", "B-TIME", "I-TIME", "B-PERCENT", "I-PERCENT", "B-MONEY", "I-MONEY", "B-QUANTITY", "I-QUANTITY", "B-ORDINAL", "I-ORDINAL", "B-CARDINAL", "I-CARDINAL", "*", "0"], "names_file": null, "id": null, "_type": "ClassLabel"}, "length": -1, "id": null, "_type": "Sequence"}, "verbal_predicates": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "wino_bias", "config_name": "wino_bias", "version": {"version_str": "4.0.0", "description": null, "major": 4, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 173899234, "num_examples": 150335, "dataset_name": "wino_bias"}}, "download_checksums": {"https://drive.google.com/uc?export=download&id=14Im3BnNl-d2fYETYmiH5yq6eFGLVC3g0": {"num_bytes": 268725744, "checksum": "139a6511bcb9761f6306bba7d151bde6ec7ec82cc9c593127b848df8af5f68a1"}}, "download_size": 268725744, "post_processing_size": null, "dataset_size": 173899234, "size_in_bytes": 442624978}}

dummy/wino_bias/4.0.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a5c20b4ec3916367f3f8b88384e3c6bde4aefdedfa670cbfb241f05e0156411c
+size 764

wino_bias.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.
+"""WinoBias: Winograd-schema dataset for detecting gender bias"""
+
+from __future__ import absolute_import, division, print_function
+
+import datasets
+
+
+_CITATION = """\
+@article{DBLP:journals/corr/abs-1804-06876,
+  author    = {Jieyu Zhao and
+               Tianlu Wang and
+               Mark Yatskar and
+               Vicente Ordonez and
+               Kai{-}Wei Chang},
+  title     = {Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods},
+  journal   = {CoRR},
+  volume    = {abs/1804.06876},
+  year      = {2018},
+  url       = {http://arxiv.org/abs/1804.06876},
+  archivePrefix = {arXiv},
+  eprint    = {1804.06876},
+  timestamp = {Mon, 13 Aug 2018 16:47:01 +0200},
+  biburl    = {https://dblp.org/rec/journals/corr/abs-1804-06876.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+"""
+
+_DESCRIPTION = """\
+WinoBias, a Winograd-schema dataset for coreference resolution focused on gender bias.
+The corpus contains Winograd-schema style sentences with entities corresponding to people
+referred by their occupation (e.g. the nurse, the doctor, the carpenter).
+"""
+
+_HOMEPAGE = "https://uclanlp.github.io/corefBias/overview"
+
+_LICENSE = "MIT License (https://github.com/uclanlp/corefBias/blob/master/LICENSE)"
+
+_URL = "https://drive.google.com/uc?export=download&id=14Im3BnNl-d2fYETYmiH5yq6eFGLVC3g0"
+
+
+class WinoBias(datasets.GeneratorBasedBuilder):
+    """WinoBias: Winograd-schema dataset for detecting gender bias"""
+
+    VERSION = datasets.Version("4.0.0")
+
+    # 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 = [
+        datasets.BuilderConfig(
+            name="wino_bias",
+            version=VERSION,
+            description="WinoBias: Winograd-schema dataset for detecting gender bias",
+        ),
+    ]
+
+    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
+            # Info about features for this: http://cemantix.org/data/ontonotes.html
+            features=datasets.Features(
+                {
+                    "document_id": datasets.Value("string"),
+                    "part_number": datasets.Value("string"),
+                    "word_number": datasets.Sequence(datasets.Value("int32")),
+                    "tokens": datasets.Sequence(datasets.Value("string")),
+                    "pos_tags": datasets.Sequence(
+                        datasets.features.ClassLabel(
+                            names=[
+                                '"',
+                                "''",
+                                "#",
+                                "$",
+                                "(",
+                                ")",
+                                ",",
+                                ".",
+                                ":",
+                                "``",
+                                "CC",
+                                "CD",
+                                "DT",
+                                "EX",
+                                "FW",
+                                "IN",
+                                "JJ",
+                                "JJR",
+                                "JJS",
+                                "LS",
+                                "MD",
+                                "NN",
+                                "NNP",
+                                "NNPS",
+                                "NNS",
+                                "NN|SYM",
+                                "PDT",
+                                "POS",
+                                "PRP",
+                                "PRP$",
+                                "RB",
+                                "RBR",
+                                "RBS",
+                                "RP",
+                                "SYM",
+                                "TO",
+                                "UH",
+                                "VB",
+                                "VBD",
+                                "VBG",
+                                "VBN",
+                                "VBP",
+                                "VBZ",
+                                "WDT",
+                                "WP",
+                                "WP$",
+                                "WRB",
+                                "HYPH",
+                                "XX",
+                                "NFP",
+                                "AFX",
+                                "ADD",
+                                "-LRB-",
+                                "-RRB-",
+                            ]
+                        )
+                    ),
+                    "parse_bit": datasets.Sequence(datasets.Value("string")),
+                    "predicate_lemma": datasets.Sequence(datasets.Value("string")),
+                    "predicate_framenet_id": datasets.Sequence(datasets.Value("string")),
+                    "word_sense": datasets.Sequence(datasets.Value("string")),
+                    "speaker": datasets.Sequence(datasets.Value("string")),
+                    "ner_tags": datasets.Sequence(
+                        datasets.features.ClassLabel(
+                            names=[
+                                "B-PERSON",
+                                "I-PERSON",
+                                "B-NORP",
+                                "I-NORP",
+                                "B-FAC",
+                                "I-FAC",
+                                "B-ORG",
+                                "I-ORG",
+                                "B-GPE",
+                                "I-GPE",
+                                "B-LOC",
+                                "I-LOC",
+                                "B-PRODUCT",
+                                "I-PRODUCT",
+                                "B-EVENT",
+                                "I-EVENT",
+                                "B-WORK_OF_ART",
+                                "I-WORK_OF_ART",
+                                "B-LAW",
+                                "I-LAW",
+                                "B-LANGUAGE",
+                                "I-LANGUAGE",
+                                "B-DATE",
+                                "I-DATE",
+                                "B-TIME",
+                                "I-TIME",
+                                "B-PERCENT",
+                                "I-PERCENT",
+                                "B-MONEY",
+                                "I-MONEY",
+                                "B-QUANTITY",
+                                "I-QUANTITY",
+                                "B-ORDINAL",
+                                "I-ORDINAL",
+                                "B-CARDINAL",
+                                "I-CARDINAL",
+                                "*",
+                                "0",
+                            ]
+                        )
+                    ),
+                    "verbal_predicates": datasets.Sequence(datasets.Value("string")),
+                }
+            ),
+            supervised_keys=None,
+            # 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):
+        """Returns SplitGenerators."""
+        data_dir = dl_manager.download_and_extract(_URL)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={"filepath": data_dir},
+            )
+        ]
+
+    def _generate_examples(self, filepath):
+        """ Yields examples. """
+        with open(filepath, encoding="utf-8") as f:
+            id_ = 0
+            document_id = None
+            part_number = 0
+            word_num = []
+            tokens = []
+            pos_tags = []
+            parse_bit = []
+            predicate_lemma = []
+            predicate_framenet_id = []
+            word_sense = []
+            speaker = []
+            ner_tags = []
+            ner_start = False
+            verbal_predicates = []
+            for line in f:
+                if line.startswith("#begin") or line.startswith("#end"):
+                    continue
+                elif not line.strip():
+                    id_ += 1
+                    yield str(id_), {
+                        "document_id": document_id,
+                        "part_number": part_number,
+                        "word_number": word_num,
+                        "tokens": tokens,
+                        "pos_tags": pos_tags,
+                        "parse_bit": parse_bit,
+                        "predicate_lemma": predicate_lemma,
+                        "predicate_framenet_id": predicate_framenet_id,
+                        "word_sense": word_sense,
+                        "speaker": speaker,
+                        "ner_tags": ner_tags,
+                        "verbal_predicates": verbal_predicates,
+                    }
+                    word_num = []
+                    tokens = []
+                    pos_tags = []
+                    parse_bit = []
+                    predicate_lemma = []
+                    predicate_framenet_id = []
+                    word_sense = []
+                    speaker = []
+                    ner_tags = []
+                    verbal_predicates = []
+                else:
+                    splits = [s for s in line.split(" ") if s]
+                    if len(splits) > 7:
+                        document_id = splits[0]
+                        part_number = splits[1]
+                        word_num.append(splits[2])
+                        tokens.append(splits[3])
+                        pos_tags.append(splits[4])
+                        parse_bit.append(splits[5])
+                        predicate_lemma.append(splits[6])
+                        predicate_framenet_id.append(splits[7])
+                        word_sense.append(splits[8])
+                        speaker.append(splits[9])
+                        ner_word = splits[10]
+                        if ")" in ner_word and ner_start:
+                            ner_start = False
+                            ner_word = "0"
+                        if "(" in ner_word:
+                            ner_start = True
+                            ner_word = ner_word.strip(" ").replace("(", "B-").replace("*", "").replace(")", "")
+                            start_word = ner_word.strip(" ").replace("B-", "")
+                        if ner_start:
+                            if ner_word.strip(" ") == "*":
+                                ner_word = "I-" + start_word
+                        ner_tags.append(ner_word)
+                        word_is_verbal_predicate = any(["(V" in x for x in splits[11:-1]])
+                        if word_is_verbal_predicate:
+                            verbal_predicates.append(splits[3])
+            if tokens:
+                # add the last one
+                id_ += 1
+                yield str(id_), {
+                    "document_id": document_id,
+                    "part_number": part_number,
+                    "word_number": word_num,
+                    "tokens": tokens,
+                    "pos_tags": pos_tags,
+                    "parse_bit": parse_bit,
+                    "predicate_lemma": predicate_lemma,
+                    "predicate_framenet_id": predicate_framenet_id,
+                    "word_sense": word_sense,
+                    "speaker": speaker,
+                    "ner_tags": ner_tags,
+                    "verbal_predicates": verbal_predicates,
+                }