Update files from the datasets library (from 1.5.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.5.0

dataset_infos.json

@@ -1 +1 @@
-{"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}}
+{"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-public-datasets.s3.amazonaws.com/zest/zest.zip": {"num_bytes": 5796188, "checksum": "91b8e41470281e774034b2f2a42a5cb36a8ff4f7d17517123d51208aa9af795f"}}, "download_size": 5796188, "post_processing_size": null, "dataset_size": 20926636, "size_in_bytes": 26722824}}

zest.py

@@ -50,7 +50,7 @@ _CITATION = """\
 }
 """
 
-_DOWNLOAD_URL = "https://ai2-datasets.s3-us-west-2.amazonaws.com/zest/zest.zip"
+_DOWNLOAD_URL = "https://ai2-public-datasets.s3.amazonaws.com/zest/zest.zip"
 _WEBPAGE = "https://allenai.org/data/zest"