Update files from the datasets library (from 1.2.0)

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

.gitattributes

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README.md

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+---
+task_categories:
+- text-scoring
+multilinguality:
+- monolingual
+task_ids:
+- other
+languages:
+- en
+annotations_creators:
+- crowdsourced
+source_datasets:
+- extended
+size_categories:
+- 10K<n<100K
+licenses:
+- CC-BY-SA-4-0
+---
+
+# Dataset Card Creation Guide
+
+## Table of Contents
+- [Dataset Card Creation Guide](#dataset-card-creation-guide)
+  - [Table of Contents](#table-of-contents)
+  - [Dataset Description](#dataset-description)
+    - [Dataset Summary](#dataset-summary)
+    - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+    - [Languages](#languages)
+  - [Dataset Structure](#dataset-structure)
+    - [Data Instances](#data-instances)
+    - [Data Fields](#data-fields)
+    - [Data Splits](#data-splits)
+  - [Dataset Creation](#dataset-creation)
+    - [Curation Rationale](#curation-rationale)
+    - [Source Data](#source-data)
+      - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
+      - [Who are the source language producers?](#who-are-the-source-language-producers)
+    - [Annotations](#annotations)
+      - [Annotation process](#annotation-process)
+      - [Who are the annotators?](#who-are-the-annotators)
+    - [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:** [GitHub](https://github.com/google-research-datasets/query-wellformedness)
+- **Repository:** [GitHub](https://github.com/google-research-datasets/query-wellformedness)
+- **Paper:** [ARXIV](https://arxiv.org/abs/1808.09419)
+- **Leaderboard:**
+- **Point of Contact:**
+
+### Dataset Summary
+
+Google's query wellformedness dataset was created by crowdsourcing well-formedness annotations for 25,100 queries from the Paralex corpus. Every query was annotated by five raters each with 1/0 rating of whether or not the query is well-formed.
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+English
+
+## Dataset Structure
+
+### Data Instances
+
+[More Information Needed]
+
+### Data Fields
+
+- `rating`: a `float` between 0-1
+- `sentence`: query which you want to rate
+
+### Data Splits
+
+|                            | Train   | Valid | Test |
+| -----                      | ------ | ----- | ---- |
+| Input Sentences            |   17500     |   3750    |  3850    |
+
+## Dataset Creation
+
+### Curation Rationale
+
+Understanding search queries is a hard problem as it involves dealing with “word salad” text ubiquitously issued by users. However, if a query resembles a well-formed question, a natural language processing pipeline is able to perform more accurate interpretation, thus reducing downstream compounding errors. Hence, identifying whether or not a query is well formed can enhance query understanding. This dataset introduce a new task of identifying a well-formed natural language question. 
+
+### Source Data
+
+Used the Paralex corpus (Fader et al., 2013) that contains pairs of noisy paraphrase questions. These questions were issued by users in WikiAnswers (a Question-Answer forum) and consist of both web-search query like constructs (“5 parts of chloroplast?”) and well-formed questions (“What is the punishment for grand theft?”).
+
+#### Initial Data Collection and Normalization
+
+Selected 25,100 queries from the unique list of queries extracted from the corpus such that no two queries in the selected set are paraphrases.
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation process
+
+The queries are annotated into well-formed or non-wellformed questions if it satisfies the following:
+
+1. Query is grammatical.
+2. Query is an explicit question.
+3. Query does not contain spelling errors.
+
+#### Who are the annotators?
+
+Every query was labeled by five different crowdworkers with a binary label indicating whether a query is well-formed or not. And average of the ratings of the five annotators was reported, to get the probability of a query being well-formed.
+
+### 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
+
+[More Information Needed]
+
+### Licensing Information
+
+Query-wellformedness dataset is licensed under CC BY-SA 4.0. Any third party content or data is provided “As Is” without any warranty, express or implied.
+
+### Citation Information
+
+```
+@InProceedings{FaruquiDas2018,
+   title = {{Identifying Well-formed Natural Language Questions}},
+   author = {Faruqui, Manaal and Das, Dipanjan},
+   booktitle = {Proc. of EMNLP},
+   year = {2018}
+}
+```

dataset_infos.json

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+{"default": {"description": "Google's query wellformedness dataset was created by crowdsourcing well-formedness annotations for 25,100 queries from the Paralex corpus. Every query was annotated by five raters each with 1/0 rating of whether or not the query is well-formed.\n", "citation": "@misc{faruqui2018identifying,\n      title={Identifying Well-formed Natural Language Questions},\n      author={Manaal Faruqui and Dipanjan Das},\n      year={2018},\n      eprint={1808.09419},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL}\n}\n", "homepage": "https://github.com/google-research-datasets/query-wellformedness", "license": "", "features": {"rating": {"dtype": "float32", "id": null, "_type": "Value"}, "content": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "google_wellformed_query", "config_name": "default", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 857391, "num_examples": 17500, "dataset_name": "google_wellformed_query"}, "test": {"name": "test", "num_bytes": 189503, "num_examples": 3850, "dataset_name": "google_wellformed_query"}, "validation": {"name": "validation", "num_bytes": 184110, "num_examples": 3750, "dataset_name": "google_wellformed_query"}}, "download_checksums": {"https://raw.githubusercontent.com/google-research-datasets/query-wellformedness/master/train.tsv": {"num_bytes": 805818, "checksum": "d857d11fed665bd6daeaf68bc5bbcf81c0cccfa21d485f4f8be9a169db526b6b"}, "https://raw.githubusercontent.com/google-research-datasets/query-wellformedness/master/test.tsv": {"num_bytes": 178070, "checksum": "978574b96a37587845fd25e1cabc992a2d6f1bcab57750d1d04b60e2757a0ba2"}, "https://raw.githubusercontent.com/google-research-datasets/query-wellformedness/master/dev.tsv": {"num_bytes": 173131, "checksum": "a9e869b9c66fa43887f9a6da41b5928ab0296439990930b0ff0becfd0842193d"}}, "download_size": 1157019, "post_processing_size": null, "dataset_size": 1231004, "size_in_bytes": 2388023}}

dummy/0.0.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:fb4799c784ada192e575c585a63c2af963bcc734af89f0b1c77ad69576283449
+size 950

google_wellformed_query.py

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+# coding=utf-8
+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# 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.
+"""Google Wellformed Query Dataset"""
+
+from __future__ import absolute_import, division, print_function
+
+import datasets
+
+
+_CITATION = """\
+@misc{faruqui2018identifying,
+      title={Identifying Well-formed Natural Language Questions},
+      author={Manaal Faruqui and Dipanjan Das},
+      year={2018},
+      eprint={1808.09419},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+"""
+
+_DESCRIPTION = """\
+Google's query wellformedness dataset was created by crowdsourcing well-formedness annotations for 25,100 queries from the Paralex corpus. Every query was annotated by five raters each with 1/0 rating of whether or not the query is well-formed.
+"""
+
+_URL = "https://raw.githubusercontent.com/google-research-datasets/query-wellformedness/master/{}.tsv"
+
+
+class GoogleWellformedQuery(datasets.GeneratorBasedBuilder):
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features({"rating": datasets.Value("float"), "content": datasets.Value("string")}),
+            supervised_keys=None,
+            homepage="https://github.com/google-research-datasets/query-wellformedness",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        tr_file = dl_manager.download_and_extract(_URL.format("train"))
+        tst_file = dl_manager.download_and_extract(_URL.format("test"))
+        dev_file = dl_manager.download_and_extract(_URL.format("dev"))
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": tr_file,
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": tst_file,
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": dev_file,
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath):
+        """ Yields examples. """
+        with open(filepath, "r", encoding="utf-8") as file:
+            reader = file.read().split("\n")
+            for idx, row in enumerate(reader):
+                row = row.split("\t")
+                if len(row) == 1:
+                    continue
+                yield idx, {"rating": row[1], "content": row[0]}