Update files from the datasets library (from 1.5.0)

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

.gitattributes

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README.md

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+---
+annotations_creators:
+- expert-generated 
+language_creators:
+- expert-generated 
+languages:
+- en 
+licenses:
+- cc-by-nc-4-0
+multilinguality:
+- monolingual
+size_categories:
+- n<1K
+source_datasets:
+- original
+task_categories:
+- question-answering
+task_ids:
+- open-domain-qa
+---
+
+# Dataset Card for Cryptonite
+
+## Table of Contents
+- [Dataset Card for Cryptonite](#dataset-card-for-cryptonite)
+  - [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)
+    - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:** [Github](https://github.com/aviaefrat/cryptonite)
+- **Repository:** [Github](https://github.com/aviaefrat/cryptonite)
+- **Paper:** [Arxiv](https://arxiv.org/pdf/2103.01242.pdf)
+- **Leaderboard:**
+- **Point of Contact:** [Twitter](https://twitter.com/AviaEfrat)
+
+### Dataset Summary
+
+Current NLP datasets targeting ambiguity can be solved by a native speaker with relative ease. We present Cryptonite, a large-scale dataset based on cryptic crosswords, which is both linguistically complex and naturally sourced. Each example in Cryptonite is a cryptic clue, a short phrase or sentence with a misleading surface reading, whose solving requires disambiguating semantic, syntactic, and phonetic wordplays, as well as world knowledge. Cryptic clues pose a challenge even for experienced solvers, though top-tier experts can solve them with almost 100% accuracy. Cryptonite is a challenging task for current models; fine-tuning T5-Large on 470k cryptic clues achieves only 7.6% accuracy, on par with the accuracy of a rule-based clue solver (8.6%).
+
+### Languages
+
+English
+
+## Dataset Structure
+
+### Data Instances
+
+This is one example from the train set.
+
+```python
+{
+  'clue': 'make progress socially in stated region (5)',
+  'answer': 'climb',
+  'date': 971654400000,
+  'enumeration': '(5)',
+  'id': 'Times-31523-6across',
+  'publisher': 'Times',
+  'quick': False
+}
+```
+
+### Data Fields
+
+- `clue`: a string representing the clue provided for the crossword
+- `answer`: a string representing the answer to the clue
+- `enumeration`: a string representing the 
+- `publisher`: a string representing the publisher of the crossword
+- `date`: a int64 representing the UNIX timestamp of the date of publication of the crossword
+- `quick`: a bool representing whether the crossword is quick (a crossword aimed at beginners, easier to solve)
+- `id`: a string to uniquely identify a given example in the dataset
+
+### Data Splits
+
+Train (470,804 examples), validation (26,156 examples), test (26,157 examples).
+
+## Dataset Creation
+
+### Curation Rationale
+
+Crosswords from the Times and the Telegraph.
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed]
+
+#### 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
+
+Avia Efrat, Uri Shaham, Dan Kilman, Omer Levy
+
+### Licensing Information
+
+`cc-by-nc-4.0`
+
+### Citation Information
+
+```
+@misc{efrat2021cryptonite,
+      title={Cryptonite: A Cryptic Crossword Benchmark for Extreme Ambiguity in Language}, 
+      author={Avia Efrat and Uri Shaham and Dan Kilman and Omer Levy},
+      year={2021},
+      eprint={2103.01242},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```
+
+
+### Contributions
+
+Thanks to [@theo-m](https://github.com/theo-m) for adding this dataset.

cryptonite.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.
+
+from __future__ import absolute_import, division, print_function
+
+import json
+import os
+
+import datasets
+
+
+_CITATION = """\
+@misc{efrat2021cryptonite,
+      title={Cryptonite: A Cryptic Crossword Benchmark for Extreme Ambiguity in Language},
+      author={Avia Efrat and Uri Shaham and Dan Kilman and Omer Levy},
+      year={2021},
+      eprint={2103.01242},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+"""
+
+_DESCRIPTION = """\
+Cryptonite: A Cryptic Crossword Benchmark for Extreme Ambiguity in Language
+Current NLP datasets targeting ambiguity can be solved by a native speaker with relative ease. We present Cryptonite,
+a large-scale dataset based on cryptic crosswords, which is both linguistically complex and naturally sourced. Each
+example in Cryptonite is a cryptic clue, a short phrase or sentence with a misleading surface reading, whose solving
+requires disambiguating semantic, syntactic, and phonetic wordplays, as well as world knowledge. Cryptic clues pose a
+challenge even for experienced solvers, though top-tier experts can solve them with almost 100% accuracy. Cryptonite
+is a challenging task for current models; fine-tuning T5-Large on 470k cryptic clues achieves only 7.6% accuracy, on
+par with the accuracy of a rule-based clue solver (8.6%).
+"""
+
+_HOMEPAGE = "https://github.com/aviaefrat/cryptonite"
+
+_LICENSE = "cc-by-nc-4.0"
+
+_URL = "https://github.com/aviaefrat/cryptonite/blob/main/data/cryptonite-official-split.zip?raw=true"
+
+
+class Cryptonite(datasets.GeneratorBasedBuilder):
+
+    VERSION = datasets.Version("1.1.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="cryptonite", version=VERSION),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # This defines the different columns of the dataset and their types
+            features=datasets.Features(
+                {
+                    "clue": datasets.Value("string"),
+                    "answer": datasets.Value("string"),
+                    "enumeration": datasets.Value("string"),
+                    "publisher": datasets.Value("string"),
+                    "date": datasets.Value("int64"),
+                    "quick": datasets.Value("bool"),
+                    "id": datasets.Value("string"),
+                }
+            ),
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        data_dir = dl_manager.download_and_extract(_URL)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "cryptonite-official-split/cryptonite-train.jsonl"),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "cryptonite-official-split/cryptonite-val.jsonl"),
+                    "split": "val",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "cryptonite-official-split/cryptonite-test.jsonl"),
+                    "split": "test",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath, split):
+        """ Yields examples. """
+
+        with open(filepath, encoding="utf-8") as f:
+            for id_, row in enumerate(f):
+                data = json.loads(row)
+
+                publisher = data["publisher"]
+                crossword_id = data["crossword_id"]
+                number = data["number"]
+                orientation = data["orientation"]
+                d_id = f"{publisher}-{crossword_id}-{number}{orientation}"
+
+                yield id_, {
+                    "clue": data["clue"],
+                    "answer": data["answer"],
+                    "enumeration": data["enumeration"],
+                    "publisher": publisher,
+                    "date": data["date"],
+                    "quick": data["quick"],
+                    "id": d_id,
+                }

dataset_infos.json

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+{"default": {"description": "We study negotiation dialogues where two agents, a buyer and a seller,\nnegotiate over the price of an time for sale. We collected a dataset of more\nthan 6K negotiation dialogues over multiple categories of products scraped from Craigslist.\nOur goal is to develop an agent that negotiates with humans through such conversations.\nThe challenge is to handle both the negotiation strategy and the rich language for bargaining.\n", "citation": "@misc{he2018decoupling,\n    title={Decoupling Strategy and Generation in Negotiation Dialogues},\n    author={He He and Derek Chen and Anusha Balakrishnan and Percy Liang},\n    year={2018},\n    eprint={1808.09637},\n    archivePrefix={arXiv},\n    primaryClass={cs.CL}\n}\n", "homepage": "https://stanfordnlp.github.io/cocoa/", "license": "", "features": {"agent_info": {"feature": {"Bottomline": {"dtype": "string", "id": null, "_type": "Value"}, "Role": {"dtype": "string", "id": null, "_type": "Value"}, "Target": {"dtype": "float32", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}, "agent_turn": {"feature": {"dtype": "int32", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "dialogue_acts": {"feature": {"intent": {"dtype": "string", "id": null, "_type": "Value"}, "price": {"dtype": "float32", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}, "utterance": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "items": {"feature": {"Category": {"dtype": "string", "id": null, "_type": "Value"}, "Images": {"dtype": "string", "id": null, "_type": "Value"}, "Price": {"dtype": "float32", "id": null, "_type": "Value"}, "Description": {"dtype": "string", "id": null, "_type": "Value"}, "Title": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "craigslist_bargains", "config_name": "default", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 8538836, "num_examples": 5247, "dataset_name": "craigslist_bargains"}, "test": {"name": "test", "num_bytes": 1353933, "num_examples": 838, "dataset_name": "craigslist_bargains"}, "validation": {"name": "validation", "num_bytes": 966032, "num_examples": 597, "dataset_name": "craigslist_bargains"}}, "download_checksums": {"https://worksheets.codalab.org/rest/bundles/0xd34bbbc5fb3b4fccbd19e10756ca8dd7/contents/blob/parsed.json": {"num_bytes": 20148723, "checksum": "34033ff87565b9fc9eb0efe867e9d3e32456dbe1528cd1683f94a84b09f66ace"}, "https://worksheets.codalab.org/rest/bundles/0x15c4160b43d44ee3a8386cca98da138c/contents/blob/parsed.json": {"num_bytes": 2287054, "checksum": "03b35dc18bd90d87dac46893ac4db8ab3eed51786d192975be68d3bab38e306e"}, "https://worksheets.codalab.org/rest/bundles/0x54d325bbcfb2463583995725ed8ca42b/contents/blob/": {"num_bytes": 2937841, "checksum": "c802f15f80ea3066d429375393319d7234daacbd6a26a6ad5afd0ad78a2f7736"}}, "download_size": 25373618, "post_processing_size": null, "dataset_size": 10858801, "size_in_bytes": 36232419}, "cryptonite": {"description": "Cryptonite: A Cryptic Crossword Benchmark for Extreme Ambiguity in Language\nCurrent NLP datasets targeting ambiguity can be solved by a native speaker with relative ease. We present Cryptonite, \na large-scale dataset based on cryptic crosswords, which is both linguistically complex and naturally sourced. Each \nexample in Cryptonite is a cryptic clue, a short phrase or sentence with a misleading surface reading, whose solving \nrequires disambiguating semantic, syntactic, and phonetic wordplays, as well as world knowledge. Cryptic clues pose a \nchallenge even for experienced solvers, though top-tier experts can solve them with almost 100% accuracy. Cryptonite \nis a challenging task for current models; fine-tuning T5-Large on 470k cryptic clues achieves only 7.6% accuracy, on \npar with the accuracy of a rule-based clue solver (8.6%).\n", "citation": "@misc{efrat2021cryptonite,\n      title={Cryptonite: A Cryptic Crossword Benchmark for Extreme Ambiguity in Language}, \n      author={Avia Efrat and Uri Shaham and Dan Kilman and Omer Levy},\n      year={2021},\n      eprint={2103.01242},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL}\n}\n", "homepage": "https://github.com/aviaefrat/cryptonite", "license": "", "features": {"clue": {"dtype": "string", "id": null, "_type": "Value"}, "answer": {"dtype": "string", "id": null, "_type": "Value"}, "enumeration": {"dtype": "string", "id": null, "_type": "Value"}, "publisher": {"dtype": "string", "id": null, "_type": "Value"}, "date": {"dtype": "int64", "id": null, "_type": "Value"}, "quick": {"dtype": "bool", "id": null, "_type": "Value"}, "id": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "new_dataset", "config_name": "cryptonite", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 52228597, "num_examples": 470804, "dataset_name": "new_dataset"}, "validation": {"name": "validation", "num_bytes": 2901768, "num_examples": 26156, "dataset_name": "new_dataset"}, "test": {"name": "test", "num_bytes": 2908275, "num_examples": 26157, "dataset_name": "new_dataset"}}, "download_checksums": {"https://github.com/aviaefrat/cryptonite/blob/main/data/cryptonite-official-split.zip?raw=true": {"num_bytes": 21615952, "checksum": "c0022977effc68b3f0e72bfe639263d5aaaa36f11287f3ec018e8db42dadb410"}}, "download_size": 21615952, "post_processing_size": null, "dataset_size": 58038640, "size_in_bytes": 79654592}}

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