Upload jigsaw_toxicity_pred_fi.py

jigsaw_toxicity_pred_fi.py

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+"""Comments from Jigsaw Toxic Comment Classification Kaggle Competition """
+
+import json
+
+import pandas as pd
+
+import datasets
+
+
+_DESCRIPTION = """\
+This dataset consists of a large number of Wikipedia comments translated to Finnish which have been labeled by human raters for toxic behavior.
+"""
+
+_HOMEPAGE = "https://turkunlp.org/"
+
+_URLS = {
+    "train": "https://huggingface.co/datasets/TurkuNLP/wikipedia-toxicity-data-fi/resolve/main/train_fi_deepl.jsonl.bz2",
+    "test": "https://huggingface.co/datasets/TurkuNLP/wikipedia-toxicity-data-fi/resolve/main/test_fi_deepl.jsonl.bz2"
+}
+
+
+class JigsawToxicityPred(datasets.GeneratorBasedBuilder):
+    """This is a dataset of comments from Wikipedia’s talk page edits which have been labeled by human raters for toxic behavior."""
+
+    VERSION = datasets.Version("1.1.0")
+
+
+    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
+            features=datasets.Features(
+                {
+                    "text": datasets.Value("string"),
+                    "label_toxicity": datasets.ClassLabel(names=["false", "true"]),
+                    "label_severe_toxicity": datasets.ClassLabel(names=["false", "true"]),
+                    "label_obscene": datasets.ClassLabel(names=["false", "true"]),
+                    "label_threat": datasets.ClassLabel(names=["false", "true"]),
+                    "label_insult": datasets.ClassLabel(names=["false", "true"]),
+                    "label_identity_attack": datasets.ClassLabel(names=["false", "true"]),
+                }
+            ),
+            # If there's a common (input, target) tuple from the features,
+            # specify them here. They'll be used if as_supervised=True in
+            # builder.as_dataset.
+            supervised_keys=None,
+            # Homepage of the dataset for documentation
+            homepage=_HOMEPAGE,
+            # License for the dataset if available
+            license=_LICENSE,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        # This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
+        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
+
+        urls_to_download = _URLS
+        downloaded_files = dl_manager.download_and_extract(urls_to_download)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={"filepath": downloaded_files["train"]}
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": downloaded_files["test"],
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath):
+        """Yields examples."""
+        # This method will receive as arguments the `gen_kwargs` defined in the previous `_split_generators` method.
+        # It is in charge of opening the given file and yielding (key, example) tuples from the dataset
+        # The key is not important, it's more here for legacy reason (legacy from tfds)
+
+      # read the json into dictionaries
+        with open(filepath, 'r') as json_file:
+            json_list = list(json_file)
+            lines = [json.loads(jline) for jline in json_list]
+        
+        for data in lines:
+            example = {}
+            example["text"] = data["text"]
+            
+            for label in ["label_toxicity", "label_severe_toxicity", "label_obscene", "label_threat", "label_insult", "label_identity_attack"]:
+                example[label] = int(data[label])
+
+            yield (data["id"], example)
+
+