Datasets:
md_gender_bias

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README.md ADDED Viewed

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+---
+annotations_creators:
+  convai2_inferred:
+  - machine-generated
+  funpedia:
+  - found
+  gendered_words:
+  - found
+  image_chat:
+  - found
+  light_inferred:
+  - machine-generated
+  name_genders:
+  - found
+  new_data:
+  - crowdsourced
+  - found
+  opensubtitles_inferred:
+  - machine-generated
+  wizard:
+  - found
+  yelp_inferred:
+  - machine-generated
+language_creators:
+  convai2_inferred:
+  - found
+  funpedia:
+  - found
+  gendered_words:
+  - found
+  image_chat:
+  - found
+  light_inferred:
+  - found
+  name_genders:
+  - found
+  new_data:
+  - crowdsourced
+  - found
+  opensubtitles_inferred:
+  - found
+  wizard:
+  - found
+  yelp_inferred:
+  - found
+languages:
+- en
+licenses:
+- mit
+multilinguality:
+- monolingual
+size_categories:
+  convai2_inferred:
+  - 100K<n<1M
+  funpedia:
+  - 10K<n<100K
+  gendered_words:
+  - n<1K
+  image_chat:
+  - 100K<n<1M
+  light_inferred:
+  - 100K<n<1M
+  name_genders:
+  - n>1M
+  new_data:
+  - 1K<n<10K
+  opensubtitles_inferred:
+  - 100K<n<1M
+  wizard:
+  - 10K<n<100K
+  yelp_inferred:
+  - n>1M
+source_datasets:
+  convai2_inferred:
+  - extended|other-convai2
+  - original
+  funpedia:
+  - original
+  gendered_words:
+  - original
+  image_chat:
+  - original
+  light_inferred:
+  - extended|other-light
+  - original
+  name_genders:
+  - original
+  new_data:
+  - original
+  opensubtitles_inferred:
+  - extended|other-opensubtitles
+  - original
+  wizard:
+  - original
+  yelp_inferred:
+  - extended|other-yelp
+  - original
+task_categories:
+- text-classification
+task_ids:
+- text-classification-other-gender-bias
+---
+# Dataset Card for Multi-Dimensional Gender Bias Classification
+## 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://parl.ai/projects/md_gender/
+- **Repository:** [Needs More Information]
+- **Paper:** https://arxiv.org/abs/2005.00614
+- **Leaderboard:** [Needs More Information]
+- **Point of Contact:** edinan@fb.com
+### Dataset Summary
+Machine learning models are trained to find patterns in data.
+NLP models can inadvertently learn socially undesirable patterns when training on gender biased text.
+In this work, we propose a general framework that decomposes gender bias in text along several pragmatic and semantic dimensions:
+bias from the gender of the person being spoken about, bias from the gender of the person being spoken to, and bias from the gender of the speaker.
+Using this fine-grained framework, we automatically annotate eight large scale datasets with gender information.
+In addition, we collect a novel, crowdsourced evaluation benchmark of utterance-level gender rewrites.
+Distinguishing between gender bias along multiple dimensions is important, as it enables us to train finer-grained gender bias classifiers.
+We show our classifiers prove valuable for a variety of important applications, such as controlling for gender bias in generative models,
+detecting gender bias in arbitrary text, and shed light on offensive language in terms of genderedness.
+### Supported Tasks and Leaderboards
+[Needs More Information]
+### Languages
+The data is in English (`en`)
+## Dataset Structure
+### Data Instances
+[Needs More Information]
+### Data Fields
+The data has the following features.
+For the `new_data` config:
+- `text`: the text to be classified
+- `original`: the text before reformulation
+- `labels`: a `list` of classification labels, with possible values including `ABOUT:female`, `ABOUT:male`, `PARTNER:female`, `PARTNER:male`, `SELF:female`.
+- `class_type`: a classification label, with possible values including `about`, `partner`, `self`.
+- `turker_gender`: a classification label, with possible values including `man`, `woman`, `nonbinary`, `prefer not to say`, `no answer`.
+For the other annotated datasets:
+- `text`: the text to be classified.
+- `gender`: a classification label, with possible values including `gender-neutral`, `female`, `male`.
+For the `_inferred` configurations:
+- `text`: the text to be classified.
+- `binary_label`: a classification label, with possible values including `ABOUT:female`, `ABOUT:male`.
+- `binary_score`: a score between 0 and 1.
+- `ternary_label`: a classification label, with possible values including `ABOUT:female`, `ABOUT:male`, `ABOUT:gender-neutral`.
+- `ternary_score`: a score between 0 and 1.
+### Data Splits
+The different parts of the data can be accessed through the different configurations:
+- `gendered_words`: A list of common nouns with a masculine and feminine variant.
+- `new_data`: Sentences reformulated and annotated along all three axes.
+- `funpedia`, `wizard`: Sentences from Funpedia and Wizards of Wikipedia annotated with ABOUT gender with entity gender information.
+- `image_chat`: sentences about images annotated  with ABOUT gender based on gender information from the entities in the image
+- `convai2_inferred`, `light_inferred`, `opensubtitles_inferred`, `yelp_inferred`:  Data from several source datasets with ABOUT annotations inferred by a trined classifier.
+## Dataset Creation
+### Curation Rationale
+[Needs More Information]
+### Source Data
+#### Initial Data Collection and Normalization
+[Needs More Information]
+#### Who are the source language producers?
+[Needs More Information]
+### Annotations
+#### Annotation process
+[Needs More Information]
+#### Who are the annotators?
+[Needs More Information]
+### Personal and Sensitive Information
+[Needs More Information]
+## Considerations for Using the Data
+### Social Impact of Dataset
+[Needs More Information]
+### Discussion of Biases
+[Needs More Information]
+### Other Known Limitations
+[Needs More Information]
+## Additional Information
+### Dataset Curators
+[Needs More Information]
+### Licensing Information
+[Needs More Information]
+### Citation Information
+[Needs More Information]

dataset_infos.json ADDED Viewed

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bias in arbitrary text, and shed light on offensive language in terms of genderedness.\n", "citation": "@inproceedings{md_gender_bias,\n author = {Emily Dinan and\n Angela Fan and\n Ledell Wu and\n Jason Weston and\n Douwe Kiela and\n Adina Williams},\n editor = {Bonnie Webber and\n Trevor Cohn and\n Yulan He and\n Yang Liu},\n title = {Multi-Dimensional Gender Bias Classification},\n booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural\n Language Processing, {EMNLP} 2020, Online, November 16-20, 2020},\n pages = {314--331},\n publisher = {Association for Computational Linguistics},\n year = {2020},\n url = {https://www.aclweb.org/anthology/2020.emnlp-main.23/}\n}\n", "homepage": "https://parl.ai/projects/md_gender/", "license": "MIT License", "features": {"word_masculine": {"dtype": "string", "id": null, "_type": "Value"}, "word_feminine": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": 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"@inproceedings{md_gender_bias,\n author = {Emily Dinan and\n Angela Fan and\n Ledell Wu and\n Jason Weston and\n Douwe Kiela and\n Adina Williams},\n editor = {Bonnie Webber and\n Trevor Cohn and\n Yulan He and\n Yang Liu},\n title = {Multi-Dimensional Gender Bias Classification},\n booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural\n Language Processing, {EMNLP} 2020, Online, November 16-20, 2020},\n pages = {314--331},\n publisher = {Association for Computational Linguistics},\n year = {2020},\n url = {https://www.aclweb.org/anthology/2020.emnlp-main.23/}\n}\n", "homepage": "https://parl.ai/projects/md_gender/", "license": "MIT License", "features": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "binary_label": {"num_classes": 2, "names": ["ABOUT:female", "ABOUT:male"], "names_file": null, "id": null, "_type": "ClassLabel"}, "binary_score": {"dtype": "float32", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, 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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.
+"""Multi-Dimensional Gender Bias classification"""
+from __future__ import absolute_import, division, print_function
+import json
+import os
+import datasets
+# TODO: Add BibTeX citation
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@inproceedings{md_gender_bias,
+  author    = {Emily Dinan and
+               Angela Fan and
+               Ledell Wu and
+               Jason Weston and
+               Douwe Kiela and
+               Adina Williams},
+  editor    = {Bonnie Webber and
+               Trevor Cohn and
+               Yulan He and
+               Yang Liu},
+  title     = {Multi-Dimensional Gender Bias Classification},
+  booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural
+               Language Processing, {EMNLP} 2020, Online, November 16-20, 2020},
+  pages     = {314--331},
+  publisher = {Association for Computational Linguistics},
+  year      = {2020},
+  url       = {https://www.aclweb.org/anthology/2020.emnlp-main.23/}
+}
+"""
+# TODO: Add description of the dataset here
+# You can copy an official description
+_DESCRIPTION = """\
+Machine learning models are trained to find patterns in data.
+NLP models can inadvertently learn socially undesirable patterns when training on gender biased text.
+In this work, we propose a general framework that decomposes gender bias in text along several pragmatic and semantic dimensions:
+bias from the gender of the person being spoken about, bias from the gender of the person being spoken to, and bias from the gender of the speaker.
+Using this fine-grained framework, we automatically annotate eight large scale datasets with gender information.
+In addition, we collect a novel, crowdsourced evaluation benchmark of utterance-level gender rewrites.
+Distinguishing between gender bias along multiple dimensions is important, as it enables us to train finer-grained gender bias classifiers.
+We show our classifiers prove valuable for a variety of important applications, such as controlling for gender bias in generative models,
+detecting gender bias in arbitrary text, and shed light on offensive language in terms of genderedness.
+"""
+_HOMEPAGE = "https://parl.ai/projects/md_gender/"
+_LICENSE = "MIT License"
+_URL = "http://parl.ai/downloads/md_gender/gend_multiclass_10072020.tgz"
+_CONF_FILES = {
+    "funpedia": {
+        "train": "funpedia/train.jsonl",
+        "validation": "funpedia/valid.jsonl",
+        "test": "funpedia/test.jsonl",
+    },
+    "image_chat": {
+        "train": "image_chat/engaging_imagechat_gender_captions_hashed.test.jsonl",
+        "validation": "image_chat/engaging_imagechat_gender_captions_hashed.train.jsonl",
+        "test": "image_chat/engaging_imagechat_gender_captions_hashed.valid.jsonl",
+    },
+    "wizard": {
+        "train": "wizard/train.jsonl",
+        "validation": "wizard/valid.jsonl",
+        "test": "wizard/test.jsonl",
+    },
+    "convai2_inferred": {
+        "train": (
+            "inferred_about/convai2_train_binary.txt",
+            "inferred_about/convai2_train.txt",
+        ),
+        "validation": (
+            "inferred_about/convai2_valid_binary.txt",
+            "inferred_about/convai2_valid.txt",
+        ),
+        "test": (
+            "inferred_about/convai2_test_binary.txt",
+            "inferred_about/convai2_test.txt",
+        ),
+    },
+    "light_inferred": {
+        "train": (
+            "inferred_about/light_train_binary.txt",
+            "inferred_about/light_train.txt",
+        ),
+        "validation": (
+            "inferred_about/light_valid_binary.txt",
+            "inferred_about/light_valid.txt",
+        ),
+        "test": (
+            "inferred_about/light_test_binary.txt",
+            "inferred_about/light_test.txt",
+        ),
+    },
+    "opensubtitles_inferred": {
+        "train": (
+            "inferred_about/opensubtitles_train_binary.txt",
+            "inferred_about/opensubtitles_train.txt",
+        ),
+        "validation": (
+            "inferred_about/opensubtitles_valid_binary.txt",
+            "inferred_about/opensubtitles_valid.txt",
+        ),
+        "test": (
+            "inferred_about/opensubtitles_test_binary.txt",
+            "inferred_about/opensubtitles_test.txt",
+        ),
+    },
+    "yelp_inferred": {
+        "train": (
+            "inferred_about/yelp_train_binary.txt",
+            "",
+        ),
+        "validation": (
+            "inferred_about/yelp_valid_binary.txt",
+            "",
+        ),
+        "test": (
+            "inferred_about/yelp_test_binary.txt",
+            "",
+        ),
+    },
+}
+class MdGenderBias(datasets.GeneratorBasedBuilder):
+    """Multi-Dimensional Gender Bias classification"""
+    VERSION = datasets.Version("1.0.0")
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="gendered_words",
+            version=VERSION,
+            description="A list of common nouns with a masculine and feminine variant.",
+        ),
+        datasets.BuilderConfig(
+            name="name_genders",
+            version=VERSION,
+            description="A list of first names with their gender attribution by year in the US.",
+        ),
+        datasets.BuilderConfig(
+            name="new_data", version=VERSION, description="Some data reformulated and annotated along all three axes."
+        ),
+        datasets.BuilderConfig(
+            name="funpedia",
+            version=VERSION,
+            description="Data from Funpedia with ABOUT annotations based on Funpedia information on an entity's gender.",
+        ),
+        datasets.BuilderConfig(
+            name="image_chat",
+            version=VERSION,
+            description="Data from ImageChat with ABOUT annotations based on image recognition.",
+        ),
+        datasets.BuilderConfig(
+            name="wizard",
+            version=VERSION,
+            description="Data from WizardsOfWikipedia with ABOUT annotations based on Wikipedia information on an entity's gender.",
+        ),
+        datasets.BuilderConfig(
+            name="convai2_inferred",
+            version=VERSION,
+            description="Data from the ConvAI2 challenge with ABOUT annotations inferred by a trined classifier.",
+        ),
+        datasets.BuilderConfig(
+            name="light_inferred",
+            version=VERSION,
+            description="Data from LIGHT with ABOUT annotations inferred by a trined classifier.",
+        ),
+        datasets.BuilderConfig(
+            name="opensubtitles_inferred",
+            version=VERSION,
+            description="Data from OpenSubtitles with ABOUT annotations inferred by a trined classifier.",
+        ),
+        datasets.BuilderConfig(
+            name="yelp_inferred",
+            version=VERSION,
+            description="Data from Yelp reviews with ABOUT annotations inferred by a trined classifier.",
+        ),
+    ]
+    DEFAULT_CONFIG_NAME = (
+        "new_data"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+    )
+    def _info(self):
+        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
+        if (
+            self.config.name == "gendered_words"
+        ):  # This is the name of the configuration selected in BUILDER_CONFIGS above
+            features = datasets.Features(
+                {
+                    "word_masculine": datasets.Value("string"),
+                    "word_feminine": datasets.Value("string"),
+                }
+            )
+        elif self.config.name == "name_genders":
+            features = datasets.Features(
+                {
+                    "name": datasets.Value("string"),
+                    "assigned_gender": datasets.ClassLabel(names=["M", "F"]),
+                    "count": datasets.Value("int32"),
+                }
+            )
+        elif self.config.name == "new_data":
+            features = datasets.Features(
+                {
+                    "text": datasets.Value("string"),
+                    "original": datasets.Value("string"),
+                    "labels": [
+                        datasets.ClassLabel(
+                            names=[
+                                "ABOUT:female",
+                                "ABOUT:male",
+                                "PARTNER:female",
+                                "PARTNER:male",
+                                "SELF:female",
+                                "SELF:male",
+                            ]
+                        )
+                    ],
+                    "class_type": datasets.ClassLabel(names=["about", "partner", "self"]),
+                    "turker_gender": datasets.ClassLabel(
+                        names=["man", "woman", "nonbinary", "prefer not to say", "no answer"]
+                    ),
+                    "episode_done": datasets.Value("bool_"),
+                    "confidence": datasets.Value("string"),
+                }
+            )
+        elif self.config.name == "funpedia":
+            features = datasets.Features(
+                {
+                    "text": datasets.Value("string"),
+                    "title": datasets.Value("string"),
+                    "persona": datasets.Value("string"),
+                    "gender": datasets.ClassLabel(names=["gender-neutral", "female", "male"]),
+                }
+            )
+        elif self.config.name == "image_chat":
+            features = datasets.Features(
+                {
+                    "caption": datasets.Value("string"),
+                    "id": datasets.Value("string"),
+                    "male": datasets.Value("bool_"),
+                    "female": datasets.Value("bool_"),
+                }
+            )
+        elif self.config.name == "wizard":
+            features = datasets.Features(
+                {
+                    "text": datasets.Value("string"),
+                    "chosen_topic": datasets.Value("string"),
+                    "gender": datasets.ClassLabel(names=["gender-neutral", "female", "male"]),
+                }
+            )
+        elif self.config.name == "yelp_inferred":
+            features = datasets.Features(
+                {
+                    "text": datasets.Value("string"),
+                    "binary_label": datasets.ClassLabel(names=["ABOUT:female", "ABOUT:male"]),
+                    "binary_score": datasets.Value("float"),
+                }
+            )
+        else:  # data with inferred labels
+            features = datasets.Features(
+                {
+                    "text": datasets.Value("string"),
+                    "binary_label": datasets.ClassLabel(names=["ABOUT:female", "ABOUT:male"]),
+                    "binary_score": datasets.Value("float"),
+                    "ternary_label": datasets.ClassLabel(names=["ABOUT:female", "ABOUT:male", "ABOUT:gender-neutral"]),
+                    "ternary_score": datasets.Value("float"),
+                }
+            )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,  # Here we define them above because they are different between the two configurations
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        data_dir = os.path.join(dl_manager.download_and_extract(_URL), "data_to_release")
+        if self.config.name == "gendered_words":
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    gen_kwargs={
+                        "filepath": None,
+                        "filepath_pair": (
+                            os.path.join(data_dir, "word_list/male_word_file.txt"),
+                            os.path.join(data_dir, "word_list/female_word_file.txt"),
+                        ),
+                    },
+                )
+            ]
+        elif self.config.name == "name_genders":
+            return [
+                datasets.SplitGenerator(
+                    name=f"yob{yob}",
+                    gen_kwargs={
+                        "filepath": os.path.join(data_dir, f"names/yob{yob}.txt"),
+                        "filepath_pair": None,
+                    },
+                )
+                for yob in range(1880, 2019)
+            ]
+        elif self.config.name == "new_data":
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    gen_kwargs={
+                        "filepath": os.path.join(data_dir, "new_data/data.jsonl"),
+                        "filepath_pair": None,
+                    },
+                )
+            ]
+        elif self.config.name in ["funpedia", "image_chat", "wizard"]:
+            return [
+                datasets.SplitGenerator(
+                    name=spl,
+                    gen_kwargs={
+                        "filepath": os.path.join(data_dir, fname),
+                        "filepath_pair": None,
+                    },
+                )
+                for spl, fname in _CONF_FILES[self.config.name].items()
+            ]
+        else:
+            return [
+                datasets.SplitGenerator(
+                    name=spl,
+                    gen_kwargs={
+                        "filepath": None,
+                        "filepath_pair": (
+                            os.path.join(data_dir, fname_1),
+                            os.path.join(data_dir, fname_2),
+                        ),
+                    },
+                )
+                for spl, (fname_1, fname_2) in _CONF_FILES[self.config.name].items()
+            ]
+    def _generate_examples(self, filepath, filepath_pair):
+        if self.config.name == "gendered_words":
+            with open(filepath_pair[0], encoding="utf-8") as f_m:
+                with open(filepath_pair[1], encoding="utf-8") as f_f:
+                    for id_, (l_m, l_f) in enumerate(zip(f_m, f_f)):
+                        yield id_, {
+                            "word_masculine": l_m.strip(),
+                            "word_feminine": l_f.strip(),
+                        }
+        elif self.config.name == "name_genders":
+            with open(filepath, encoding="utf-8") as f:
+                for id_, line in enumerate(f):
+                    name, g, ct = line.strip().split(",")
+                    yield id_, {
+                        "name": name,
+                        "assigned_gender": g,
+                        "count": int(ct),
+                    }
+        elif "_inferred" in self.config.name:
+            with open(filepath_pair[0], encoding="utf-8") as f_b:
+                if "yelp" in self.config.name:
+                    for id_, line_b in enumerate(f_b):
+                        text_b, label_b, score_b = line_b.split("\t")
+                        yield id_, {
+                            "text": text_b,
+                            "binary_label": label_b,
+                            "binary_score": float(score_b.strip()),
+                        }
+                else:
+                    with open(filepath_pair[1], encoding="utf-8") as f_t:
+                        for id_, (line_b, line_t) in enumerate(zip(f_b, f_t)):
+                            text_b, label_b, score_b = line_b.split("\t")
+                            text_t, label_t, score_t = line_t.split("\t")
+                            yield id_, {
+                                "text": text_b,
+                                "binary_label": label_b,
+                                "binary_score": float(score_b.strip()),
+                                "ternary_label": label_t,
+                                "ternary_score": float(score_t.strip()),
+                            }
+        else:
+            with open(filepath, encoding="utf-8") as f:
+                for id_, line in enumerate(f):
+                    example = json.loads(line.strip())
+                    if "turker_gender" in example and example["turker_gender"] is None:
+                        example["turker_gender"] = "no answer"
+                    yield id_, example