Upload FRENK-hate-en.py

FRENK-hate-en.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.
+"""An annotated dataset for classifying offensive or acceptable speech."""
+
+import os
+import csv
+
+import datasets
+
+
+
+_CITATION = """\
+@misc{ljubešić2019frenk,
+      title={The FRENK Datasets of Socially Unacceptable Discourse in Slovene and English}, 
+      author={Nikola Ljubešić and Darja Fišer and Tomaž Erjavec},
+      year={2019},
+      eprint={1906.02045},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/1906.02045}
+}
+"""
+
+_DESCRIPTION = """\
+The FRENK Datasets of Socially Unacceptable Discourse in English.
+"""
+
+_HOMEPAGE = "https://www.clarin.si/repository/xmlui/handle/11356/1433"
+
+_LICENSE = "CLARIN.SI Licence ACA ID-BY-NC-INF-NORED 1.0"
+
+_URL = "https://huggingface.co/datasets/5roop/FRENK-hate-en/resolve/main/data.zip"
+
+_CLASS_MAP_MULTICLASS = {
+    'Acceptable speech': 0, 
+    'Inappropriate': 1, 
+    'Background offensive': 2,
+    'Other offensive': 3, 
+    'Background violence': 4,
+    'Other violence': 5, 
+}
+
+_CLASS_MAP_BINARY = {
+    'Acceptable': 0, 
+    'Offensive': 1, 
+}
+
+
+class FRENKHateSpeechEN(datasets.GeneratorBasedBuilder):
+    """The FRENK Datasets of Socially Unacceptable Discourse in Slovene and English."""
+
+    VERSION = datasets.Version("0.0.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="binary", version=VERSION, 
+                                description="Labels are either 'Offensive' or 'Acceptable'."),
+        datasets.BuilderConfig(name="multiclass", version=VERSION, 
+                                description="Labels are 'Acceptable speech', 'Other offensive', 'Background offensive', 'Inappropriate', 'Other violence', 'Background violence'"),
+    ]
+
+    DEFAULT_CONFIG_NAME = "binary"  
+
+    def _info(self):
+        feature_dict = {
+            "text": datasets.Value("string"),
+            "target": datasets.Value("string"),
+            "topic": datasets.Value("string"),
+            }
+        if self.config.name == "binary":
+            features = datasets.Features(
+                {
+                    **feature_dict,
+                    "label": datasets.ClassLabel(names=["Acceptable", "Offensive"]),
+                }
+            )
+        else:
+            features = datasets.Features(
+                {
+                    **feature_dict,
+                    "label": datasets.ClassLabel(names=['Acceptable speech', 'Other offensive', 'Background offensive', 'Inappropriate', 'Other violence', 'Background violence']),
+                }
+            )
+        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_file = dl_manager.download_and_extract(_URL)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN, gen_kwargs={
+                    'filepath': os.path.join(data_file, "train.tsv"),
+                    }
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION, gen_kwargs={
+                    'filepath': os.path.join(data_file, "dev.tsv"),
+                    }
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST, gen_kwargs={
+                    'filepath': os.path.join(data_file, "test.tsv"),
+                    }
+            ),
+        ]
+
+    def _generate_examples(self, filepath):
+        """Yields examples."""
+
+        with open(filepath, encoding="utf-8") as f:
+            reader = csv.reader(f, delimiter="\t")
+            for id_, row in enumerate(reader):
+                if id_ == 0:
+                    continue
+                to_return_dict = {
+                    "text": row[1],
+                    "target": row[4] ,
+                    "topic":  row[5]
+                    }
+                yield id_, {
+                    **to_return_dict,
+                    **{"label": _CLASS_MAP_BINARY[row[3]] if self.config.name == "binary" else _CLASS_MAP_MULTICLASS[row[2]]}
+                }