Upload suomi24_toxicity_pred.py

suomi24_toxicity_pred.py

@@ -0,0 +1,96 @@
+import json
+import pandas as pd
+import datasets
+
+
+_DESCRIPTION = """\
+This dataset consists of Suomi24 comments which have been labeled by human raters for toxic behavior.
+"""
+
+_HOMEPAGE = "https://turkunlp.org/"
+
+_URLS = {
+    "test": "https://huggingface.co/datasets/TurkuNLP/Suomi24-toxicity-annotated/resolve/main/all_annotations.tsv"
+}
+
+
+class Suomi24ToxicityPred(datasets.GeneratorBasedBuilder):
+    """This is a dataset of comments sampled from Suomi24 and annotated using jigsaw toxicity labels."""
+
+    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": datasets.Value("string") # we only have one label for each text
+                }
+            ),
+            # 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
+        )
+
+    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.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 tsv file
+        with open(filepath, "rt", encoding="utf-8") as f:
+            data = f.readlines()
+        data = data[1:]
+        for i in range(len(data)):
+            data[i] = data[i].replace("\n", "")
+            data[i] = data[i].split("\t")
+            assert len(data[i]) == 3
+        
+        from collections import Counter
+        from itertools import count
+
+        ids = [one[0] for one in data]
+        c = Counter(ids)
+
+        iters = {k: count(1) for k, v in c.items() if v > 1}
+        output_list = [x+str(next(iters[x])) if x in iters else x for x in ids]
+
+        count = 0
+        # here make the data into a proper thing
+        for one in data:
+            example = {}
+            id = output_list[count] # change this somehow
+            count = count + 1
+            example["text"] = one[2]
+            example["label"] = one[1]
+
+            yield (id, example)
+
+