Delete loading script

snips_built_in_intents.py

@@ -1,125 +0,0 @@
-# coding=utf-8
-# Copyright 2020 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.
-# 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.
-
-# Lint as: python3
-"""Snips built in intents (2016-12-built-in-intents) dataset."""
-
-
-import json
-
-import datasets
-from datasets.tasks import TextClassification
-
-
-_DESCRIPTION = """\
-Snips' built in intents dataset was initially used to compare different voice assistants and released as a public dataset hosted at
-https://github.com/sonos/nlu-benchmark 2016-12-built-in-intents. The dataset contains 328 utterances over 10 intent classes. The
-related paper mentioned on the github page is https://arxiv.org/abs/1805.10190 and a related Medium post is
-https://medium.com/snips-ai/benchmarking-natural-language-understanding-systems-d35be6ce568d .
-"""
-
-_CITATION = """\
-@article{DBLP:journals/corr/abs-1805-10190,
-  author    = {Alice Coucke and
-               Alaa Saade and
-               Adrien Ball and
-               Th{\'{e}}odore Bluche and
-               Alexandre Caulier and
-               David Leroy and
-               Cl{\'{e}}ment Doumouro and
-               Thibault Gisselbrecht and
-               Francesco Caltagirone and
-               Thibaut Lavril and
-               Ma{\"{e}}l Primet and
-               Joseph Dureau},
-  title     = {Snips Voice Platform: an embedded Spoken Language Understanding system
-               for private-by-design voice interfaces},
-  journal   = {CoRR},
-  volume    = {abs/1805.10190},
-  year      = {2018},
-  url       = {http://arxiv.org/abs/1805.10190},
-  archivePrefix = {arXiv},
-  eprint    = {1805.10190},
-  timestamp = {Mon, 13 Aug 2018 16:46:59 +0200},
-  biburl    = {https://dblp.org/rec/journals/corr/abs-1805-10190.bib},
-  bibsource = {dblp computer science bibliography, https://dblp.org}
-}
-"""
-
-_DOWNLOAD_URL = (
-    "https://raw.githubusercontent.com/sonos/nlu-benchmark/master/2016-12-built-in-intents/benchmark_data.json"
-)
-
-
-class SnipsBuiltInIntents(datasets.GeneratorBasedBuilder):
-    """Snips built in intents (2016-12-built-in-intents) dataset."""
-
-    def _info(self):
-        # ToDo: Consider adding an alternate configuration for the entity slots. The default is to only return the intent labels.
-
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "text": datasets.Value("string"),
-                    "label": datasets.features.ClassLabel(
-                        names=[
-                            "ComparePlaces",
-                            "RequestRide",
-                            "GetWeather",
-                            "SearchPlace",
-                            "GetPlaceDetails",
-                            "ShareCurrentLocation",
-                            "GetTrafficInformation",
-                            "BookRestaurant",
-                            "GetDirections",
-                            "ShareETA",
-                        ]
-                    ),
-                }
-            ),
-            homepage="https://github.com/sonos/nlu-benchmark/tree/master/2016-12-built-in-intents",
-            citation=_CITATION,
-            task_templates=[TextClassification(text_column="text", label_column="label")],
-        )
-
-    def _split_generators(self, dl_manager):
-        # Note: The source dataset doesn't have a train-test split.
-        # ToDo: Consider splitting the data into train-test sets and re-hosting.
-        samples_path = dl_manager.download_and_extract(_DOWNLOAD_URL)
-        return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": samples_path}),
-        ]
-
-    def _generate_examples(self, filepath):
-        """Snips built in intent examples."""
-        num_examples = 0
-
-        with open(filepath, encoding="utf-8") as file_obj:
-            snips_dict = json.load(file_obj)
-            domains = snips_dict["domains"]
-
-            for domain_dict in domains:
-                intents = domain_dict["intents"]
-
-                for intent_dict in intents:
-                    label = intent_dict["benchmark"]["Snips"]["original_intent_name"]
-                    queries = intent_dict["queries"]
-
-                    for query_dict in queries:
-                        query_text = query_dict["text"]
-
-                        yield num_examples, {"text": query_text, "label": label}
-                        num_examples += 1  # Explicitly keep track of the number of examples.