Upload nedzhi.py

nedzhi.py

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+import json
+import textwrap
+
+import datasets
+
+
+_CITATION = """\
+    @inproceedings{larson-etal-2019-evaluation,
+    title = "An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction",
+    author = "Larson, Stefan  and
+      Mahendran, Anish  and
+      Peper, Joseph J.  and
+      Clarke, Christopher  and
+      Lee, Andrew  and
+      Hill, Parker  and
+      Kummerfeld, Jonathan K.  and
+      Leach, Kevin  and
+      Laurenzano, Michael A.  and
+      Tang, Lingjia  and
+      Mars, Jason",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
+    year = "2019",
+    url = "https://www.aclweb.org/anthology/D19-1131"
+}
+"""
+
+_DESCRIPTION = """\
+    This dataset is for evaluating the performance of intent classification systems in the
+    presence of "out-of-scope" queries. By "out-of-scope", we mean queries that do not fall
+    into any of the system-supported intent classes. Most datasets include only data that is
+    "in-scope". Our dataset includes both in-scope and out-of-scope data. You might also know
+    the term "out-of-scope" by other terms, including "out-of-domain" or "out-of-distribution".
+"""
+
+_DESCRIPTIONS = {
+    "small": textwrap.dedent(
+        """\
+        Small, in which there are only 50 training queries per each in-scope intent
+        """
+    ),
+    "imbalanced": textwrap.dedent(
+        """\
+        Imbalanced, in which intents have either 25, 50, 75, or 100 training queries.
+        """
+    ),
+    "plus": textwrap.dedent(
+        """\
+        OOS+, in which there are 250 out-of-scope training examples, rather than 100.
+        """
+    ),
+}
+
+_URL = "https://amazon-massive-nlu-dataset.s3.amazonaws.com/amazon-massive-dataset-1.0.tar.gz"
+
+_DATA_URLS = {
+    "small": "https://amazon-massive-nlu-dataset.s3.amazonaws.com/amazon-massive-dataset-1.0.tar.gz",
+    "imbalanced": "https://amazon-massive-nlu-dataset.s3.amazonaws.com/amazon-massive-dataset-1.0.tar.gz",
+    "plus": "https://amazon-massive-nlu-dataset.s3.amazonaws.com/amazon-massive-dataset-1.0.tar.gz",
+}
+
+
+class ClincConfig(datasets.BuilderConfig):
+
+    """BuilderConfig for CLINC150"""
+
+    def __init__(self, description, data_url, citation, url, **kwrags):
+        """
+        Args:
+            description: `string`, brief description of the dataset
+            data_url: `dictionary`, dict with url for each split of data.
+            citation: `string`, citation for the dataset.
+            url: `string`, url for information about the dataset.
+            **kwrags: keyword arguments frowarded to super
+        """
+        super(ClincConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwrags)
+        self.description = description
+        self.data_url = data_url
+        self.citation = citation
+        self.url = url
+
+
+class ClincOos(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [
+        ClincConfig(
+            name=name, description=_DESCRIPTIONS[name], data_url=_DATA_URLS[name], citation=_CITATION, url=_URL
+        )
+        for name in ["small", "imbalanced", "plus"]
+    ]
+
+    def _info(self):
+        features = {}
+        features["text"] = datasets.Value("string")
+        labels_list = [
+            'audio_volume_other',
+            'play_music', 
+            'iot_hue_lighton', 
+            'general_greet', 
+            'calendar_set', 
+            'audio_volume_down', 
+            'social_query', 'audio_volume_mute', 'iot_wemo_on', 'iot_hue_lightup', 'audio_volume_up', 'iot_coffee', 'takeaway_query', 'qa_maths', 'play_game', 'cooking_query', 'iot_hue_lightdim', 'iot_wemo_off', 'music_settings', 'weather_query', 'news_query', 'alarm_remove', 'social_post', 'recommendation_events', 'transport_taxi', 'takeaway_order', 'music_query', 'calendar_query', 'lists_query', 'qa_currency', 'recommendation_movies', 'general_joke', 'recommendation_locations', 'email_querycontact', 'lists_remove', 'play_audiobook', 'email_addcontact', 'lists_createoradd', 'play_radio', 'qa_stock', 'alarm_query', 'email_sendemail', 'general_quirky', 'music_likeness', 'cooking_recipe', 'email_query', 'datetime_query', 'transport_traffic', 'play_podcasts', 'iot_hue_lightchange', 'calendar_remove', 'transport_query', 'transport_ticket', 'qa_factoid', 'iot_cleaning', 'alarm_set', 'datetime_convert', 'iot_hue_lightoff', 'qa_definition', 'music_dislikeness',
+        ]
+        features["intent"] = datasets.ClassLabel(names=labels_list)
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION + "\n" + self.config.description,
+            features=datasets.Features(features),
+            homepage=self.config.url,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        file_ = dl_manager.download_and_extract(self.config.data_url)
+
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": file_, "split": "train"}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": file_, "split": "val"}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": file_, "split": "test"}),
+        ]
+
+    def _generate_examples(self, filepath, split):
+        with open(filepath, encoding="utf-8") as f:
+            j = json.load(f)
+            for id_, row in enumerate(j[split] + j["oos_" + split]):
+                yield id_, {"text": row[0], "intent": row[1]}