Update files from the datasets library (from 1.2.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.2.0

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README.md

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+---
+annotations_creators:
+- expert-generated
+language_creators:
+- expert-generated
+languages:
+- en
+licenses:
+- cc0-1-0
+multilinguality:
+- monolingual
+size_categories:
+- n<1K
+source_datasets:
+- original
+task_categories:
+- text-classification
+task_ids:
+- intent-classification
+---
+
+# Dataset Card for Snips Built In Intents
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-instances)
+  - [Data Splits](#data-instances)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+
+## Dataset Description
+
+- **Homepage:** https://github.com/sonos/nlu-benchmark/tree/master/2016-12-built-in-intents
+- **Repository:** https://github.com/sonos/nlu-benchmark/tree/master/2016-12-built-in-intents
+- **Paper:** https://arxiv.org/abs/1805.10190
+- **Point of Contact:** The Snips team has joined Sonos in November 2019. These open datasets remain available and their access is now managed by the Sonos Voice Experience Team. Please email sve-research@sonos.com with any question.
+
+### Dataset Summary
+
+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 in folder 2016-12-built-in-intents. The dataset contains 328 utterances over 10 intent classes. 
+A related Medium post is https://medium.com/snips-ai/benchmarking-natural-language-understanding-systems-d35be6ce568d.
+
+### Supported Tasks and Leaderboards
+
+There are no related shared tasks that we are aware of.
+
+### Languages
+
+English
+
+## Dataset Structure
+
+### Data Instances
+
+The dataset contains 328 utterances over 10 intent classes. Each sample looks like:
+`{'label': 8, 'text': 'Transit directions to Barcelona Pizza.'}`
+
+### Data Fields
+
+- `text`: The text utterance expressing some user intent.
+- `label`: The intent label of the piece of text utterance.
+
+### Data Splits
+
+The source data is not split.
+
+## Dataset Creation
+
+### Curation Rationale
+
+The dataset was originally created to compare the performance of a number of voice assistants. However, the labelled utterances are useful 
+for developing and benchmarking text chatbots as well.
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+It is not clear how the data was collected. From the Medium post: `The benchmark relies on a set of 328 queries built by the business team 
+at Snips, and kept secret from data scientists and engineers throughout the development of the solution.`
+
+#### Who are the source language producers?
+
+Originally prepared by snips.ai. The Snips team has since joined Sonos in November 2019. These open datasets remain available and their 
+access is now managed by the Sonos Voice Experience Team. Please email sve-research@sonos.com with any question.
+
+### Annotations
+
+#### Annotation process
+
+It is not clear how the data was collected. From the Medium post: `The benchmark relies on a set of 328 queries built by the business team 
+at Snips, and kept secret from data scientists and engineers throughout the development of the solution.`
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+Originally prepared by snips.ai. The Snips team has since joined Sonos in November 2019. These open datasets remain available and their 
+access is now managed by the Sonos Voice Experience Team. Please email sve-research@sonos.com with any question.
+
+### Licensing Information
+
+The source data is licensed under Creative Commons Zero v1.0 Universal.
+
+### Citation Information
+
+Any publication based on these datasets must include a full citation to the following paper in which the results were published by the Snips Team:
+
+Coucke A. et al., "Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces." CoRR 2018, 
+https://arxiv.org/abs/1805.10190 

dataset_infos.json

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+{"default": {"description": "Snips' built in intents dataset was initially used to compare different voice assistants and released as a public dataset hosted at\nhttps://github.com/sonos/nlu-benchmark 2016-12-built-in-intents. The dataset contains 328 utterances over 10 intent classes. The\nrelated paper mentioned on the github page is https://arxiv.org/abs/1805.10190 and a related Medium post is\nhttps://medium.com/snips-ai/benchmarking-natural-language-understanding-systems-d35be6ce568d .\n", "citation": "@article{DBLP:journals/corr/abs-1805-10190,\n  author    = {Alice Coucke and\n               Alaa Saade and\n               Adrien Ball and\n               Th{'{e}}odore Bluche and\n               Alexandre Caulier and\n               David Leroy and\n               Cl{'{e}}ment Doumouro and\n               Thibault Gisselbrecht and\n               Francesco Caltagirone and\n               Thibaut Lavril and\n               Ma{\"{e}}l Primet and\n               Joseph Dureau},\n  title     = {Snips Voice Platform: an embedded Spoken Language Understanding system\n               for private-by-design voice interfaces},\n  journal   = {CoRR},\n  volume    = {abs/1805.10190},\n  year      = {2018},\n  url       = {http://arxiv.org/abs/1805.10190},\n  archivePrefix = {arXiv},\n  eprint    = {1805.10190},\n  timestamp = {Mon, 13 Aug 2018 16:46:59 +0200},\n  biburl    = {https://dblp.org/rec/journals/corr/abs-1805-10190.bib},\n  bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n", "homepage": "https://github.com/sonos/nlu-benchmark/tree/master/2016-12-built-in-intents", "license": "", "features": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 10, "names": ["ComparePlaces", "RequestRide", "GetWeather", "SearchPlace", "GetPlaceDetails", "ShareCurrentLocation", "GetTrafficInformation", "BookRestaurant", "GetDirections", "ShareETA"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "builder_name": "snips_built_in_intents", "config_name": "default", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 19431, "num_examples": 328, "dataset_name": "snips_built_in_intents"}}, "download_checksums": {"https://raw.githubusercontent.com/sonos/nlu-benchmark/master/2016-12-built-in-intents/benchmark_data.json": {"num_bytes": 9130264, "checksum": "e3f6ba7b7ab0e8d1a5959a8c8ecb4fc566a281f4ebd34fdf1160929c630d299f"}}, "download_size": 9130264, "post_processing_size": null, "dataset_size": 19431, "size_in_bytes": 9149695}}

dummy/0.0.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4e0e1fc5f5396f649d77fa2a866b14ffd86bbaf70f7060150e0c8410a35d1200
+size 3038

snips_built_in_intents.py

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+# 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."""
+
+from __future__ import absolute_import, division, print_function
+
+import json
+
+import datasets
+
+
+_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,
+        )
+
+    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.