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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+---
+annotations_creators:
+- crowdsourced
+language_creators:
+- crowdsourced
+languages:
+- en
+licenses:
+- cc-by-4-0
+multilinguality:
+- monolingual
+size_categories:
+- 100K<n<1M
+source_datasets:
+- original
+task_categories:
+- conditional-text-generation
+task_ids:
+- other-stuctured-to-text
+---
+
+# Dataset Card for An Atlas of Machine Commonsense for If-Then Reasoning - Atomic Common Sense Dataset
+
+## 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://homes.cs.washington.edu/~msap/atomic/
+- **Repository:**
+https://homes.cs.washington.edu/~msap/atomic/
+- **Paper:**
+Maarten Sap, Ronan LeBras, Emily Allaway, Chandra Bhagavatula, Nicholas Lourie, Hannah Rashkin, Brendan Roof, Noah A. Smith & Yejin Choi (2019). ATOMIC: An Atlas of Machine Commonsense for If-Then Reasoning. AAAI
+
+### Dataset Summary
+
+This dataset provides the template sentences and
+relationships defined in the ATOMIC common sense dataset. There are
+three splits - train, test, and dev.
+
+From the authors.
+
+Disclaimer/Content warning: the events in atomic have been
+automatically extracted from blogs, stories and books written at
+various times. The events might depict violent or problematic actions,
+which we left in the corpus for the sake of learning the (probably
+negative but still important) commonsense implications associated with
+the events. We removed a small set of truly out-dated events, but
+might have missed some so please email us (msap@cs.washington.edu) if
+you have any concerns.
+
+
+For more information, see: https://homes.cs.washington.edu/~msap/atomic/
+
+### Languages
+en
+
+## Dataset Structure
+
+### Data Instances
+
+Here is one example from the atomic dataset:
+
+
+`` 
+{'event': "PersonX uses PersonX's ___ to obtain", 'oEffect': [], 'oReact': ['annoyed', 'angry', 'worried'], 'oWant': [], 'prefix': ['uses', 'obtain'], 'split': 'trn', 'xAttr': [], 'xEffect': [], 'xIntent': ['to have an advantage', 'to fulfill a desire', 'to get out of trouble'], 'xNeed': [], 'xReact': ['pleased', 'smug', 'excited'], 'xWant': []}
+``
+
+
+### Data Fields
+
+Notes from the authors:
+
+* event: just a string representation of the event.
+* oEffect,oReact,oWant,xAttr,xEffect,xIntent,xNeed,xReact,xWant: annotations for each of the dimensions, stored in a json-dumped string.
+  Note: "none" means the worker explicitly responded with the empty response, whereas [] means the worker did not annotate this dimension.
+* prefix: json-dumped string that represents the prefix of content words (used to make a better trn/dev/tst split).
+* split: string rep of which split the event belongs to.
+
+### Data Splits
+
+The atomic dataset has three splits: test, train and dev of the form:
+
+## Dataset Creation
+
+### Curation Rationale
+
+This dataset was gathered and created over to assist in common sense reasoning.
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+See the reaserch paper and website for more detail. The dataset was
+created by the University of Washington using crowd sourced data
+
+
+#### Who are the source language producers?
+
+The Atomic authors and crowd source.
+
+### Annotations
+
+#### Annotation process
+
+Human annotations directed by forms.
+
+#### Who are the annotators?
+
+Human annotations.
+
+### Personal and Sensitive Information
+
+Unkown, but likely none.
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+The goal for the work is to help machines understand common sense.
+
+### Discussion of Biases
+
+Since the data is human annotators, there is likely to be baised. From the authors:
+
+
+Disclaimer/Content warning: the events in atomic have been automatically extracted from blogs, stories and books written at various times. The events might depict violent or problematic actions, which we left in the corpus for the sake of learning the (probably negative but still important) commonsense implications associated with the events. We removed a small set of truly out-dated events, but might have missed some so please email us (msap@cs.washington.edu) if you have any concerns.
+
+
+### Other Known Limitations
+
+While there are many relationships, the data is quite sparse. Also, each item of the dataset could be expanded into multiple sentences along the vsrious dimensions, oEffect, oRect, etc.
+
+For example, given event: "PersonX uses PersonX's ___ to obtain" and dimension oReact: "annoyed", this could be transformed into an entry:
+
+"PersonX uses PersonX's ___ to obtain => PersonY is annoyed"
+
+## Additional Information
+
+### Dataset Curators
+
+The authors of Aotmic at The University of Washington
+
+### Licensing Information
+
+The Creative Commons Attribution 4.0 International License. https://creativecommons.org/licenses/by/4.0/
+
+### Citation Information
+
+@article{Sap2019ATOMICAA,
+  title={ATOMIC: An Atlas of Machine Commonsense for If-Then Reasoning},
+  author={Maarten Sap and Ronan Le Bras and Emily Allaway and Chandra Bhagavatula and Nicholas Lourie and Hannah Rashkin and Brendan Roof and Noah A. Smith and Yejin Choi},
+  journal={ArXiv},
+  year={2019},
+  volume={abs/1811.00146}
+}

atomic.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.
+"""The Atommic Dataset"""
+
+from __future__ import absolute_import, division, print_function
+
+import json
+import os
+
+import datasets
+
+
+_CITATION = """@article{Sap2019ATOMICAA,
+  title={ATOMIC: An Atlas of Machine Commonsense for If-Then Reasoning},
+  author={Maarten Sap and Ronan Le Bras and Emily Allaway and Chandra Bhagavatula and Nicholas Lourie and Hannah Rashkin and Brendan Roof and Noah A. Smith and Yejin Choi},
+  journal={ArXiv},
+  year={2019},
+  volume={abs/1811.00146}
+}
+"""
+
+
+_DESCRIPTION = """This dataset provides the template sentences and
+relationships defined in the ATOMIC common sense dataset. There are
+three splits - train, test, and dev.
+
+From the authors.
+
+Disclaimer/Content warning: the events in atomic have been
+automatically extracted from blogs, stories and books written at
+various times. The events might depict violent or problematic actions,
+which we left in the corpus for the sake of learning the (probably
+negative but still important) commonsense implications associated with
+the events. We removed a small set of truly out-dated events, but
+might have missed some so please email us (msap@cs.washington.edu) if
+you have any concerns.
+
+"""
+
+_HOMEPAGE = "https://homes.cs.washington.edu/~msap/atomic/"
+
+_LICENSE = "The Creative Commons Attribution 4.0 International License. https://creativecommons.org/licenses/by/4.0/"
+
+_URLs = {"atomic": "https://homes.cs.washington.edu/~msap/atomic/data/atomic_data.tgz"}
+
+
+class Atomic(datasets.GeneratorBasedBuilder):
+    """Atomic Common Sense Dataset"""
+
+    VERSION = datasets.Version("1.1.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="atomic", version=VERSION, description="The Atomic dataset"),
+    ]
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "event": datasets.Value("string"),
+                "oEffect": datasets.Sequence(datasets.Value("string")),
+                "oReact": datasets.Sequence(datasets.Value("string")),
+                "oWant": datasets.Sequence(datasets.Value("string")),
+                "xAttr": datasets.Sequence(datasets.Value("string")),
+                "xEffect": datasets.Sequence(datasets.Value("string")),
+                "xIntent": datasets.Sequence(datasets.Value("string")),
+                "xNeed": datasets.Sequence(datasets.Value("string")),
+                "xReact": datasets.Sequence(datasets.Value("string")),
+                "xWant": datasets.Sequence(datasets.Value("string")),
+                "prefix": datasets.Sequence(datasets.Value("string")),
+                "split": datasets.Value("string"),
+            }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        my_urls = _URLs[self.config.name]
+        data_dir = dl_manager.download_and_extract(my_urls)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "v4_atomic_trn.csv"),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"filepath": os.path.join(data_dir, "v4_atomic_tst.csv"), "split": "test"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "v4_atomic_dev.csv"),
+                    "split": "dev",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath, split):
+        """ Yields examples from the Atomic dataset. """
+
+        with open(filepath, encoding="utf-8") as f:
+            for id_, row in enumerate(f):
+                if row.startswith("event"):
+                    continue
+                row = row.replace('"[', "[").replace(']"', "]")
+                sent, rest = row.split("[", 1)
+                sent = sent.strip(', "')
+                rest = "[" + rest
+                rest = rest.replace('""', '"').replace('\\\\"]', '"]')
+                rest = rest.split(",")
+                rest[-1] = '"' + rest[-1].strip() + '"'
+                rest = ",".join(rest)
+                row = '["' + sent + '",' + rest + "]"
+                row = json.loads(row)
+                yield id_, {
+                    "event": str(row[0]),
+                    "oEffect": row[1],
+                    "oReact": row[2],
+                    "oWant": row[3],
+                    "xAttr": row[4],
+                    "xEffect": row[5],
+                    "xIntent": row[6],
+                    "xNeed": row[7],
+                    "xReact": row[8],
+                    "xWant": row[9],
+                    "prefix": row[10],
+                    "split": str(row[11]),
+                }

dataset_infos.json

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+{"atomic": {"description": "This dataset provides the template sentences and\nrelationships defined in the ATOMIC common sense dataset. There are\nthree splits - train, test, and dev.\n\nFrom the authors.\n\nDisclaimer/Content warning: the events in atomic have been\nautomatically extracted from blogs, stories and books written at\nvarious times. The events might depict violent or problematic actions,\nwhich we left in the corpus for the sake of learning the (probably\nnegative but still important) commonsense implications associated with\nthe events. We removed a small set of truly out-dated events, but\nmight have missed some so please email us (msap@cs.washington.edu) if\nyou have any concerns.\n\n", "citation": "@article{Sap2019ATOMICAA,\n  title={ATOMIC: An Atlas of Machine Commonsense for If-Then Reasoning},\n  author={Maarten Sap and Ronan Le Bras and Emily Allaway and Chandra Bhagavatula and Nicholas Lourie and Hannah Rashkin and Brendan Roof and Noah A. Smith and Yejin Choi},\n  journal={ArXiv},\n  year={2019},\n  volume={abs/1811.00146}\n}\n", "homepage": "https://homes.cs.washington.edu/~msap/atomic/", "license": "The Creative Commons Attribution 4.0 International License. https://creativecommons.org/licenses/by/4.0/", "features": {"event": {"dtype": "string", "id": null, "_type": "Value"}, "oEffect": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "oReact": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "oWant": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "xAttr": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "xEffect": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "xIntent": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "xNeed": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "xReact": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "xWant": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "prefix": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "split": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "atomic", "config_name": "atomic", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 32441878, "num_examples": 202271, "dataset_name": "atomic"}, "test": {"name": "test", "num_bytes": 3995624, "num_examples": 24856, "dataset_name": "atomic"}, "validation": {"name": "validation", "num_bytes": 3629768, "num_examples": 22620, "dataset_name": "atomic"}}, "download_checksums": {"https://homes.cs.washington.edu/~msap/atomic/data/atomic_data.tgz": {"num_bytes": 19083782, "checksum": "f22779944e6613044d530875dd9592a333be2e3cb83d25493b36bba7c59b2ba9"}}, "download_size": 19083782, "post_processing_size": null, "dataset_size": 40067270, "size_in_bytes": 59151052}}

dummy/atomic/1.1.0/dummy_data.zip

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