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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+*.arrow filter=lfs diff=lfs merge=lfs -text
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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:
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- question-answering
+- structure-prediction
+task_ids:
+- closed-domain-qa
+- extractive-qa
+- question-answering-other-yes-no-qa
+- structure-prediction-other-output-structure
+---
+
+# Dataset Card for "ZEST: ZEroShot learning from Task descriptions"
+
+## 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://allenai.org/data/zest
+- **Repository:** https://github.com/allenai/zest
+- **Paper:** https://arxiv.org/abs/2011.08115
+- **Leaderboard:** https://leaderboard.allenai.org/zest/submissions/public
+- **Point of Contact:**
+
+### Dataset Summary
+
+ZEST tests whether NLP systems can perform unseen tasks in a zero-shot way, given a natural language description of
+the task. It is an instantiation of our proposed framework "learning from task descriptions". The tasks include
+classification, typed entity extraction and relationship extraction, and each task is paired with 20 different
+annotated (input, output) examples. ZEST's structure allows us to systematically test whether models can generalize
+in five different ways.
+
+### Supported Tasks and Leaderboards
+
+A [leaderboard](https://leaderboard.allenai.org/zest/submissions/public) is included with accepatbility metrics for
+each of the four generalization types outlined in the paper. The metrics are novel acceptability metrics also
+proposed by the authors.
+
+### Languages
+
+The dataset is in English.
+
+## Dataset Structure
+
+### Data Instances
+
+[More Information Needed]
+
+### Data Fields
+
+[More Information Needed]
+
+### Data Splits
+
+[More Information Needed]
+
+## Dataset Creation
+
+### Curation Rationale
+
+To evaluate the ability of a model to generalize to unseen tasks based only on a task description in a zero-shot
+manner.
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+Mechanical Turk crowdsource workers.
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+Mechanical Turk crowdsource workers.
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+The dataset emphasizes a model's ability to generalize to unseen tasks with only a natural language description of
+the task. The long-term vision of this type of evaluation is to facilitate the creation of models which can perform
+arbitrary tasks with only a prompt from a non-technical user. This could broaden the frontier of what a user can
+ask something like a chatbot to do for them, but it is unclear how restrictions would be put in place to prevent
+users from prompting a system to perform unethical tasks.
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+This dataset is licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/).
+
+### Citation Information
+
+```
+@inproceedings{weller-etal-2020-learning,
+    title = "Learning from Task Descriptions",
+    author = "Weller, Orion  and
+      Lourie, Nicholas  and
+      Gardner, Matt  and
+      Peters, Matthew",
+    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
+    month = nov,
+    year = "2020",
+    address = "Online",
+    publisher = "Association for Computational Linguistics",
+    url = "https://www.aclweb.org/anthology/2020.emnlp-main.105",
+    pages = "1361--1375",
+    abstract = "Typically, machine learning systems solve new tasks by training on thousands of examples. In contrast, humans can solve new tasks by reading some instructions, with perhaps an example or two. To take a step toward closing this gap, we introduce a framework for developing NLP systems that solve new tasks after reading their descriptions, synthesizing prior work in this area. We instantiate this frame- work with a new English language dataset, ZEST, structured for task-oriented evaluation on unseen tasks. Formulating task descriptions as questions, we ensure each is general enough to apply to many possible inputs, thus comprehensively evaluating a model{'}s ability to solve each task. Moreover, the dataset{'}s structure tests specific types of systematic generalization. We find that the state-of-the-art T5 model achieves a score of 12% on ZEST, leaving a significant challenge for NLP researchers.",
+}
+```
+

dataset_infos.json

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+{"default": {"description": "ZEST tests whether NLP systems can perform unseen tasks in a zero-shot way, given a natural language description of\nthe task. It is an instantiation of our proposed framework \"learning from task descriptions\". The tasks include\nclassification, typed entity extraction and relationship extraction, and each task is paired with 20 different\nannotated (input, output) examples. ZEST's structure allows us to systematically test whether models can generalize\nin five different ways.\n", "citation": "@inproceedings{weller-etal-2020-learning,\n    title = \"Learning from Task Descriptions\",\n    author = \"Weller, Orion  and\n      Lourie, Nicholas  and\n      Gardner, Matt  and\n      Peters, Matthew\",\n    booktitle = \"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)\",\n    month = nov,\n    year = \"2020\",\n    address = \"Online\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/2020.emnlp-main.105\",\n    pages = \"1361--1375\",\n    abstract = \"Typically, machine learning systems solve new tasks by training on thousands of examples. In contrast, humans can solve new tasks by reading some instructions, with perhaps an example or two. To take a step toward closing this gap, we introduce a framework for developing NLP systems that solve new tasks after reading their descriptions, synthesizing prior work in this area. We instantiate this frame- work with a new English language dataset, ZEST, structured for task-oriented evaluation on unseen tasks. Formulating task descriptions as questions, we ensure each is general enough to apply to many possible inputs, thus comprehensively evaluating a model{'}s ability to solve each task. Moreover, the dataset{'}s structure tests specific types of systematic generalization. We find that the state-of-the-art T5 model achieves a score of 12{\\%} on ZEST, leaving a significant challenge for NLP researchers.\",\n}\n", "homepage": "https://allenai.org/data/zest", "license": "", "features": {"task_id": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "generalization_type": {"dtype": "string", "id": null, "_type": "Value"}, "derives_from": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "domain": {"dtype": "string", "id": null, "_type": "Value"}, "context": {"dtype": "string", "id": null, "_type": "Value"}, "answer": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "all_answers": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "zest", "config_name": "default", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 9588987, "num_examples": 10766, "dataset_name": "zest"}, "validation": {"name": "validation", "num_bytes": 2056804, "num_examples": 2280, "dataset_name": "zest"}, "test": {"name": "test", "num_bytes": 9280845, "num_examples": 11980, "dataset_name": "zest"}}, "download_checksums": {"https://ai2-datasets.s3-us-west-2.amazonaws.com/zest/zest.zip": {"num_bytes": 5796188, "checksum": "91b8e41470281e774034b2f2a42a5cb36a8ff4f7d17517123d51208aa9af795f"}}, "download_size": 5796188, "post_processing_size": null, "dataset_size": 20926636, "size_in_bytes": 26722824}}

dummy/0.0.0/dummy_data.zip

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+oid sha256:c06e069a35cb78eebc7af2254cde3ddcdde1e16c8ff549705855b448e5caec27
+size 74157

zest.py

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+# coding=utf-8
+# Copyright 2020 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
+"""ZEST: ZEroShot learning from Task descriptions"""
+
+from __future__ import absolute_import, division, print_function
+
+import json
+import os
+
+import datasets
+
+
+_DESCRIPTION = """\
+ZEST tests whether NLP systems can perform unseen tasks in a zero-shot way, given a natural language description of
+the task. It is an instantiation of our proposed framework "learning from task descriptions". The tasks include
+classification, typed entity extraction and relationship extraction, and each task is paired with 20 different
+annotated (input, output) examples. ZEST's structure allows us to systematically test whether models can generalize
+in five different ways.
+"""
+
+_CITATION = """\
+@inproceedings{weller-etal-2020-learning,
+    title = "Learning from Task Descriptions",
+    author = "Weller, Orion  and
+      Lourie, Nicholas  and
+      Gardner, Matt  and
+      Peters, Matthew",
+    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
+    month = nov,
+    year = "2020",
+    address = "Online",
+    publisher = "Association for Computational Linguistics",
+    url = "https://www.aclweb.org/anthology/2020.emnlp-main.105",
+    pages = "1361--1375",
+    abstract = "Typically, machine learning systems solve new tasks by training on thousands of examples. In contrast, humans can solve new tasks by reading some instructions, with perhaps an example or two. To take a step toward closing this gap, we introduce a framework for developing NLP systems that solve new tasks after reading their descriptions, synthesizing prior work in this area. We instantiate this frame- work with a new English language dataset, ZEST, structured for task-oriented evaluation on unseen tasks. Formulating task descriptions as questions, we ensure each is general enough to apply to many possible inputs, thus comprehensively evaluating a model{'}s ability to solve each task. Moreover, the dataset{'}s structure tests specific types of systematic generalization. We find that the state-of-the-art T5 model achieves a score of 12% on ZEST, leaving a significant challenge for NLP researchers.",
+}
+"""
+
+_DOWNLOAD_URL = "https://ai2-datasets.s3-us-west-2.amazonaws.com/zest/zest.zip"
+_WEBPAGE = "https://allenai.org/data/zest"
+
+
+class Zest(datasets.GeneratorBasedBuilder):
+    """ZEST: ZEroShot learning from Task descriptions"""
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "task_id": datasets.Value("string"),
+                    "question": datasets.Value("string"),
+                    "generalization_type": datasets.Value("string"),
+                    "derives_from": datasets.Sequence(datasets.Value("string")),
+                    "domain": datasets.Value("string"),
+                    "context": datasets.Value("string"),
+                    "answer": datasets.Sequence(datasets.Value("string")),
+                    "all_answers": datasets.Sequence(datasets.Value("string")),
+                }
+            ),
+            homepage=_WEBPAGE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        path = dl_manager.download_and_extract(_DOWNLOAD_URL)
+        path = os.path.join(path, "zest")
+
+        train_path = os.path.join(path, "train.jsonl")
+        validation_path = os.path.join(path, "dev.jsonl")
+        test_path = os.path.join(path, "test_unanswered.jsonl")
+
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": validation_path}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_path, "is_labeled": False}),
+        ]
+
+    def _generate_examples(self, filepath, is_labeled=True):
+        """Generate AG News examples."""
+        counter = 0
+        with open(filepath, "r", encoding="utf-8") as f:
+            for line in f:
+                task = json.loads(line)
+                base_dict = {
+                    "task_id": task["id"],
+                    "question": task["question"],
+                    "generalization_type": task["type"]["generalization_type"] if is_labeled else None,
+                    "derives_from": task["type"]["derives_from"] if is_labeled else [],
+                    "domain": task["type"]["domain"] if is_labeled else None,
+                }
+
+                for example in task["examples"]:
+                    answer = example["answer"] if is_labeled else []
+                    if isinstance(answer, str):
+                        answer = [answer]
+                    yield counter, dict(
+                        context=example["context"],
+                        answer=answer,
+                        all_answers=example["all_answers"] if is_labeled else [],
+                        **base_dict,
+                    )
+                    counter += 1