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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README.md

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+---
+annotations_creators:
+- expert-generated
+language_creators:
+- crowdsourced
+languages:
+- en
+licenses:
+- mit
+multilinguality:
+- monolingual
+size_categories:
+- 10K<n<100K
+source_datasets:
+- extended|other
+task_categories:
+- sequence-modeling
+task_ids:
+- slot-filling
+---
+
+# Dataset Card for [Dataset Name]
+
+## 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://inklab.usc.edu/NumerSense/
+- **Repository:** https://github.com/INK-USC/NumerSense
+- **Paper:** https://arxiv.org/abs/2005.00683
+- **Leaderboard:** https://inklab.usc.edu/NumerSense/#exp
+- **Point of Contact:** Author emails listed in [paper](https://arxiv.org/abs/2005.00683)
+
+### Dataset Summary
+
+NumerSense is a new numerical commonsense reasoning probing task, with a diagnostic dataset consisting of 3,145
+masked-word-prediction probes. The general idea is to mask numbers between 0-10 in sentences mined from a commonsense
+corpus and evaluate whether a language model can correctly predict the masked value.
+
+### Supported Tasks and Leaderboards
+
+The dataset supports the task of slot-filling, specifically as an evaluation of numerical common sense. A leaderboard
+is included on the [dataset webpage](https://inklab.usc.edu/NumerSense/#exp) with included benchmarks for GPT-2,
+RoBERTa, BERT, and human performance. Leaderboards are included for both the core set and the adversarial set
+discussed below.
+
+### Languages
+
+This dataset is in English.
+
+## Dataset Structure
+
+### Data Instances
+
+Each instance consists of a sentence with a masked numerical value between 0-10 and (in the train set) a target.
+Example from the training set:
+
+```
+sentence: Black bears are about <mask> metres tall.
+target: two
+```
+
+### Data Fields
+
+Each value of the training set consists of:
+- `sentence`: The sentence with a number masked out with the `<mask>` token.
+- `target`: The ground truth target value. Since the test sets do not include the ground truth, the `target` field
+values are empty strings in the `test_core` and `test_all` splits.
+
+### Data Splits
+
+The dataset includes the following pre-defined data splits:
+
+- A train set with >10K labeled examples (i.e. containing a ground truth value)
+- A core test set (`test_core`) with 1,132 examples (no ground truth provided)
+- An expanded test set (`test_all`) encompassing `test_core` with the addition of adversarial examples for a total of
+3,146 examples. See section 2.2 of [the paper] for a discussion of how these examples are constructed.
+
+## Dataset Creation
+
+### Curation Rationale
+
+The purpose of this dataset is "to study whether PTLMs capture numerical commonsense knowledge, i.e., commonsense
+knowledge that provides an understanding of the numeric relation between entities." This work is motivated by the
+prior research exploring whether language models possess _commonsense knowledge_.
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+The dataset is an extension of the [Open Mind Common Sense](https://huggingface.co/datasets/open_mind_common_sense)
+corpus. A query was performed to discover sentences containing numbers between 0-12, after which the resulting
+sentences were manually evaluated for inaccuracies, typos, and the expression of commonsense knowledge. The numerical
+values were then masked.
+
+#### Who are the source language producers?
+
+The [Open Mind Common Sense](https://huggingface.co/datasets/open_mind_common_sense) corpus, from which this dataset
+is sourced, is a crowdsourced dataset maintained by the MIT Media Lab.
+
+### Annotations
+
+#### Annotation process
+
+No annotations are present in this dataset beyond the `target` values automatically sourced from the masked
+sentences, as discussed above.
+
+#### Who are the annotators?
+
+The curation and inspection was done in two rounds by graduate students.
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+The motivation of measuring a model's ability to associate numerical values with real-world concepts appears
+relatively innocuous. However, as discussed in the following section, the source dataset may well have biases encoded
+from crowdworkers, particularly in terms of factoid coverage. A model's ability to perform well on this benchmark
+should therefore not be considered evidence that it is more unbiased or objective than a human performing similar
+tasks.
+
+[More Information Needed]
+
+### Discussion of Biases
+
+This dataset is sourced from a crowdsourced commonsense knowledge base. While the information contained in the graph
+is generally considered to be of high quality, the coverage is considered to very low as a representation of all
+possible commonsense knowledge. The representation of certain factoids may also be skewed by the demographics of the
+crowdworkers. As one possible example, the term "homophobia" is connected with "Islam" in the ConceptNet knowledge
+base, but not with any other religion or group, possibly due to the biases of crowdworkers contributing to the
+project.
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+This dataset was collected by Bill Yuchen Lin, Seyeon Lee, Rahul Khanna, and Xiang Ren, Computer Science researchers
+at the at the University of Southern California.
+
+### Licensing Information
+
+The data is hosted in a GitHub repositor with the
+[MIT License](https://github.com/INK-USC/NumerSense/blob/main/LICENSE).
+
+### Citation Information
+
+```
+@inproceedings{lin2020numersense,
+  title={Birds have four legs?! NumerSense: Probing Numerical Commonsense Knowledge of Pre-trained Language Models},
+  author={Bill Yuchen Lin and Seyeon Lee and Rahul Khanna and Xiang Ren}, 
+  booktitle={Proceedings of EMNLP},
+  year={2020},
+  note={to appear}
+}
+```

dataset_infos.json

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+{"default": {"description": "NumerSense is a new numerical commonsense reasoning probing task, with a diagnostic dataset consisting of 3,145 masked-word-prediction probes.\n\nWe propose to study whether numerical commonsense knowledge can be induced from pre-trained language models like BERT, and to what extent this access to knowledge robust against adversarial examples is. We hope this will be beneficial for tasks such as knowledge base completion and open-domain question answering.\n", "citation": "@inproceedings{lin2020numersense,\n    title={Birds have four legs?! NumerSense: Probing Numerical Commonsense Knowledge of Pre-trained Language Models},\n    author={Bill Yuchen Lin and Seyeon Lee and Rahul Khanna and Xiang Ren}, \n    booktitle={Proceedings of EMNLP},\n    year={2020},\n    note={to appear}\n}\n", "homepage": "https://inklab.usc.edu/NumerSense/", "license": "", "features": {"sentence": {"dtype": "string", "id": null, "_type": "Value"}, "target": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "numer_sense", "config_name": "default", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 825865, "num_examples": 10444, "dataset_name": "numer_sense"}, "test_core": {"name": "test_core", "num_bytes": 62652, "num_examples": 1132, "dataset_name": "numer_sense"}, "test_all": {"name": "test_all", "num_bytes": 184180, "num_examples": 3146, "dataset_name": "numer_sense"}}, "download_checksums": {"https://raw.githubusercontent.com/INK-USC/NumerSense/main/data/train.masked.tsv": {"num_bytes": 763185, "checksum": "34cd706f4070907b8a9fa7200504bea099a6f34a343e282ae4f3a987ecd63d95"}, "https://raw.githubusercontent.com/INK-USC/NumerSense/main/data/test.core.masked.txt": {"num_bytes": 56983, "checksum": "ed8abedebf6875085619db2c1b966da63409a9b3d0ee3c8f1b1a6a6bcfe0d094"}, "https://raw.githubusercontent.com/INK-USC/NumerSense/main/data/test.all.masked.txt": {"num_bytes": 165295, "checksum": "eefc2649b8dd679d2722d41494b01821be5d76d45ba058c0bf71840fa353a89b"}}, "download_size": 985463, "post_processing_size": null, "dataset_size": 1072697, "size_in_bytes": 2058160}}

dummy/0.0.0/dummy_data.zip

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+oid sha256:313b2e4a18f140b28d3066c7eccff497eced2337dab893bfb5328bc6fc500df5
+size 1045

numer_sense.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.
+
+"""The NumerSense Dataset"""
+
+from __future__ import absolute_import, division, print_function
+
+import csv
+
+import datasets
+
+
+_CITATION = """\
+@inproceedings{lin2020numersense,
+    title={Birds have four legs?! NumerSense: Probing Numerical Commonsense Knowledge of Pre-trained Language Models},
+    author={Bill Yuchen Lin and Seyeon Lee and Rahul Khanna and Xiang Ren},
+    booktitle={Proceedings of EMNLP},
+    year={2020},
+    note={to appear}
+}
+"""
+
+_DESCRIPTION = """\
+NumerSense is a new numerical commonsense reasoning probing task, with a diagnostic dataset consisting of 3,145 masked-word-prediction probes.
+
+We propose to study whether numerical commonsense knowledge can be induced from pre-trained language models like BERT, and to what extent this access to knowledge robust against adversarial examples is. We hope this will be beneficial for tasks such as knowledge base completion and open-domain question answering.
+"""
+
+_HOMEPAGE_URL = "https://inklab.usc.edu/NumerSense/"
+_BASE_DOWNLOAD_URL = "https://raw.githubusercontent.com/INK-USC/NumerSense/main/data/"
+
+
+class NumerSense(datasets.GeneratorBasedBuilder):
+    """The Multilingual Amazon Reviews Corpus"""
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "sentence": datasets.Value("string"),
+                    "target": datasets.Value("string"),
+                }
+            ),
+            supervised_keys=None,
+            homepage=_HOMEPAGE_URL,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        train_url = _BASE_DOWNLOAD_URL + "train.masked.tsv"
+        test_core_url = _BASE_DOWNLOAD_URL + "test.core.masked.txt"
+        test_all_url = _BASE_DOWNLOAD_URL + "test.all.masked.txt"
+
+        train_path = dl_manager.download_and_extract(train_url)
+        test_core_path = dl_manager.download_and_extract(test_core_url)
+        test_all_path = dl_manager.download_and_extract(test_all_url)
+
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"file_path": train_path, "is_test": False}),
+            datasets.SplitGenerator(name="test_core", gen_kwargs={"file_path": test_core_path, "is_test": True}),
+            datasets.SplitGenerator(name="test_all", gen_kwargs={"file_path": test_all_path, "is_test": True}),
+        ]
+
+    def _generate_examples(self, file_path, is_test):
+        with open(file_path, "r", encoding="utf-8") as f:
+            if is_test:
+                for i, sentence in enumerate(f):
+                    yield i, {"sentence": sentence.rstrip(), "target": ""}
+            else:
+                reader = csv.DictReader(f, delimiter="\t", fieldnames=["sentence", "target"])
+                for i, row in enumerate(reader):
+                    yield i, row