Update tupy.py

tupy.py

@@ -1,5 +1,5 @@
 # coding=utf-8
-# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -12,100 +12,167 @@
 # 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.
-"""Portuguese dataset for hate speech detection composed of 5,668 tweets with binary annotations (i.e. 'hate' vs. 'no-hate')."""
 
+# Lint as: python3
+"""Toxic/Abusive Tweets Multilabel Classification Dataset for Brazilian Portuguese."""
 
-import csv
+
+import os
+
+import pandas as pd
 
 import datasets
 
 
+# TODO: Add BibTeX citation
+# Find for instance the citation on arxiv or on the dataset repo/website
 _CITATION = """\
-@inproceedings{fortuna-etal-2019-hierarchically,
-title = "A Hierarchically-Labeled {P}ortuguese Hate Speech Dataset",
-author = "Fortuna, Paula  and
-    Rocha da Silva, Jo{\\~a}o  and
-    Soler-Company, Juan  and
-    Wanner, Leo  and
-    Nunes, S{\'e}rgio",
-booktitle = "Proceedings of the Third Workshop on Abusive Language Online",
-month = aug,
-year = "2019",
-address = "Florence, Italy",
-publisher = "Association for Computational Linguistics",
-url = "https://www.aclweb.org/anthology/W19-3510",
-doi = "10.18653/v1/W19-3510",
-pages = "94--104",
-abstract = "Over the past years, the amount of online offensive speech has been growing steadily. To successfully cope with it, machine learning are applied. However, ML-based techniques require sufficiently large annotated datasets. In the last years, different datasets were published, mainly for English. In this paper, we present a new dataset for Portuguese, which has not been in focus so far. The dataset is composed of 5,668 tweets. For its annotation, we defined two different schemes used by annotators with different levels of expertise. Firstly, non-experts annotated the tweets with binary labels ({`}hate{'} vs. {`}no-hate{'}). Secondly, expert annotators classified the tweets following a fine-grained hierarchical multiple label scheme with 81 hate speech categories in total. The inter-annotator agreement varied from category to category, which reflects the insight that some types of hate speech are more subtle than others and that their detection depends on personal perception. This hierarchical annotation scheme is the main contribution of the presented work, as it facilitates the identification of different types of hate speech and their intersections. To demonstrate the usefulness of our dataset, we carried a baseline classification experiment with pre-trained word embeddings and LSTM on the binary classified data, with a state-of-the-art outcome.",
+@article{DBLP:journals/corr/abs-2010-04543,
+  author    = {Joao Augusto Leite and
+               Diego F. Silva and
+               Kalina Bontcheva and
+               Carolina Scarton},
+  title     = {Toxic Language Detection in Social Media for Brazilian Portuguese:
+               New Dataset and Multilingual Analysis},
+  journal   = {CoRR},
+  volume    = {abs/2010.04543},
+  year      = {2020},
+  url       = {https://arxiv.org/abs/2010.04543},
+  eprinttype = {arXiv},
+  eprint    = {2010.04543},
+  timestamp = {Tue, 15 Dec 2020 16:10:16 +0100},
+  biburl    = {https://dblp.org/rec/journals/corr/abs-2010-04543.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
 }
 """
 
 _DESCRIPTION = """\
-Portuguese dataset for hate speech detection composed of 5,668 tweets with binary annotations (i.e. 'hate' vs. 'no-hate').
+ToLD-Br is the biggest dataset for toxic tweets in Brazilian Portuguese, crowdsourced
+by 42 annotators selected from a pool of 129 volunteers. Annotators were selected aiming
+to create a plural group in terms of demographics (ethnicity, sexual orientation, age, gender).
+Each tweet was labeled by three annotators in 6 possible categories:
+LGBTQ+phobia,Xenophobia, Obscene, Insult, Misogyny and Racism.
 """
 
-_HOMEPAGE = "https://github.com/paulafortuna/Portuguese-Hate-Speech-Dataset"
+# TODO: Add a link to an official homepage for the dataset here
+_HOMEPAGE = "https://github.com/JAugusto97/ToLD-Br"
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = "https://github.com/JAugusto97/ToLD-Br/blob/main/LICENSE_ToLD-Br.txt "
 
-_LICENSE = "Unknown"
+# TODO: Add link to the official dataset URLs here
+# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
+# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
 
-_URL = "https://github.com/paulafortuna/Portuguese-Hate-Speech-Dataset/raw/master/2019-05-28_portuguese_hate_speech_binary_classification.csv"
+_URLS = {
+    "multilabel": "https://raw.githubusercontent.com/JAugusto97/ToLD-Br/main/ToLD-BR.csv",
+    "binary": "https://github.com/JAugusto97/ToLD-Br/raw/main/experiments/data/1annotator.zip",
+}
 
 
-class HateSpeechPortuguese(datasets.GeneratorBasedBuilder):
-    """Portuguese dataset for hate speech detection composed of 5,668 tweets with binary annotations (i.e. 'hate' vs. 'no-hate')."""
+class ToldBr(datasets.GeneratorBasedBuilder):
+    """Toxic/Abusive Tweets Classification Dataset for Brazilian Portuguese."""
 
     VERSION = datasets.Version("1.0.0")
 
+    # 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="multilabel",
+            version=VERSION,
+            description="""
+            Full multilabel dataset with target values ranging
+            from 0 to 3 representing the votes from each annotator.
+            """,
+        ),
+        datasets.BuilderConfig(
+            name="binary",
+            version=VERSION,
+            description="""
+            Binary classification dataset version separated in train, dev and test test.
+            A text is considered toxic if at least one of the multilabel classes were labeled
+            by at least one annotator.
+            """,
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "binary"
+
     def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
+        if self.config.name == "binary":
+            features = datasets.Features(
                 {
                     "text": datasets.Value("string"),
-                    "label": datasets.ClassLabel(names=["no-hate", "hate"]),
-                    "hatespeech_G1": datasets.Value("string"),
-                    "annotator_G1": datasets.Value("string"),
-                    "hatespeech_G2": datasets.Value("string"),
-                    "annotator_G2": datasets.Value("string"),
-                    "hatespeech_G3": datasets.Value("string"),
-                    "annotator_G3": datasets.Value("string"),
+                    "label": datasets.ClassLabel(names=["not-toxic", "toxic"]),
                 }
-            ),
-            supervised_keys=("text", "label"),
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
+            )
+        else:
+            features = datasets.Features(
+                {
+                    "text": datasets.Value("string"),
+                    "homophobia": datasets.ClassLabel(names=["zero_votes", "one_vote", "two_votes", "three_votes"]),
+                    "obscene": datasets.ClassLabel(names=["zero_votes", "one_vote", "two_votes", "three_votes"]),
+                    "insult": datasets.ClassLabel(names=["zero_votes", "one_vote", "two_votes", "three_votes"]),
+                    "racism": datasets.ClassLabel(names=["zero_votes", "one_vote", "two_votes", "three_votes"]),
+                    "misogyny": datasets.ClassLabel(names=["zero_votes", "one_vote", "two_votes", "three_votes"]),
+                    "xenophobia": datasets.ClassLabel(names=["zero_votes", "one_vote", "two_votes", "three_votes"]),
+                }
+            )
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION
         )
 
     def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-
-        data_file = dl_manager.download_and_extract(_URL)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "filepath": data_file,
-                },
-            ),
-        ]
+        urls = _URLS[self.config.name]
+        data_dir = dl_manager.download_and_extract(urls)
+        if self.config.name == "binary":
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    gen_kwargs={"filepath": os.path.join(data_dir, "1annotator/ptbr_train_1annotator.csv")},
+                ),
+                datasets.SplitGenerator(
+                    name=datasets.Split.TEST,
+                    gen_kwargs={"filepath": os.path.join(data_dir, "1annotator/ptbr_test_1annotator.csv")},
+                ),
+                datasets.SplitGenerator(
+                    name=datasets.Split.VALIDATION,
+                    gen_kwargs={"filepath": os.path.join(data_dir, "1annotator/ptbr_validation_1annotator.csv")},
+                ),
+            ]
+        else:
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    gen_kwargs={
+                        "filepath": os.path.join(data_dir),
+                    },
+                )
+            ]
 
     def _generate_examples(self, filepath):
-        """Yields examples."""
-
-        with open(filepath, encoding="utf-8") as f:
-            reader = csv.reader(f)
-            for id_, row in enumerate(reader):
-                if id_ == 0:
-                    continue
-
-                yield id_, {
-                    "text": row[0],
-                    "label": "hate" if row[1] == "1" else "no-hate",
-                    "hatespeech_G1": row[2],
-                    "annotator_G1": row[3],
-                    "hatespeech_G2": row[4],
-                    "annotator_G2": row[5],
-                    "hatespeech_G3": row[6],
-                    "annotator_G3": row[7],
+        df = pd.read_csv(filepath, engine="python")
+        for key, row in enumerate(df.itertuples()):
+            if self.config.name == "multilabel":
+                yield key, {
+                    "text": row.text,
+                    "homophobia": int(float(row.homophobia)),
+                    "obscene": int(float(row.obscene)),
+                    "insult": int(float(row.insult)),
+                    "racism": int(float(row.racism)),
+                    "misogyny": int(float(row.misogyny)),
+                    "xenophobia": int(float(row.xenophobia)),
                 }
+            else:
+                yield key, {"text": row.text, "label": int(row.toxic)}