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Update files from the datasets library (from 1.2.0)

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Release notes: https://github.com/huggingface/datasets/releases/tag/1.2.0

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  1. .gitattributes +27 -0
  2. README.md +189 -0
  3. dataset_infos.json +1 -0
  4. dummy/1.0.0/dummy_data.zip +3 -0
  5. limit.py +114 -0
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+ *.7z filter=lfs diff=lfs merge=lfs -text
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+ *.arrow filter=lfs diff=lfs merge=lfs -text
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+ *.bin filter=lfs diff=lfs merge=lfs -text
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+ *.bin.* filter=lfs diff=lfs merge=lfs -text
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+ *.ftz filter=lfs diff=lfs merge=lfs -text
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+ *.gz filter=lfs diff=lfs merge=lfs -text
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+ *.h5 filter=lfs diff=lfs merge=lfs -text
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+ *.joblib filter=lfs diff=lfs merge=lfs -text
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+ *.lfs.* filter=lfs diff=lfs merge=lfs -text
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+ *.model filter=lfs diff=lfs merge=lfs -text
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+ *.msgpack filter=lfs diff=lfs merge=lfs -text
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+ *.onnx filter=lfs diff=lfs merge=lfs -text
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+ *.ot filter=lfs diff=lfs merge=lfs -text
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+ *.parquet filter=lfs diff=lfs merge=lfs -text
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+ *.pb filter=lfs diff=lfs merge=lfs -text
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+ *.pt filter=lfs diff=lfs merge=lfs -text
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+ *.pth filter=lfs diff=lfs merge=lfs -text
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+ *.rar filter=lfs diff=lfs merge=lfs -text
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+ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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+ *.tar.* filter=lfs diff=lfs merge=lfs -text
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+ *.tflite filter=lfs diff=lfs merge=lfs -text
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+ *.tgz filter=lfs diff=lfs merge=lfs -text
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+ *.zip filter=lfs diff=lfs merge=lfs -text
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README.md ADDED
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+ ---
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+ annotations_creators:
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+ - crowdsourced
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+ language_creators:
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+ - found
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+ languages:
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+ - en
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+ licenses:
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+ - cc-by-sa-4-0
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+ multilinguality:
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+ - monolingual
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+ size_categories:
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+ - 10K<n<100K
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+ source_datasets:
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+ - extended|net-activities-captions
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+ - original
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+ task_categories:
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+ - structure-prediction
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+ - text-classification
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+ task_ids:
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+ - multi-class-classification
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+ - named-entity-recognition
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+ ---
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+
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+ # Dataset Card Creation Guide
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+
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+ ## Table of Contents
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+ - [Dataset Description](#dataset-description)
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+ - [Dataset Summary](#dataset-summary)
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+ - [Supported Tasks](#supported-tasks-and-leaderboards)
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+ - [Languages](#languages)
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+ - [Dataset Structure](#dataset-structure)
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+ - [Data Instances](#data-instances)
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+ - [Data Fields](#data-instances)
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+ - [Data Splits](#data-instances)
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+ - [Dataset Creation](#dataset-creation)
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+ - [Curation Rationale](#curation-rationale)
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+ - [Source Data](#source-data)
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+ - [Annotations](#annotations)
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+ - [Personal and Sensitive Information](#personal-and-sensitive-information)
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+ - [Considerations for Using the Data](#considerations-for-using-the-data)
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+ - [Social Impact of Dataset](#social-impact-of-dataset)
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+ - [Discussion of Biases](#discussion-of-biases)
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+ - [Other Known Limitations](#other-known-limitations)
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+ - [Additional Information](#additional-information)
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+ - [Dataset Curators](#dataset-curators)
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+ - [Licensing Information](#licensing-information)
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+ - [Citation Information](#citation-information)
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+
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+ ## Dataset Description
51
+
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+ - **Homepage:** -
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+ - **Repository:** [github](https://github.com/ilmgut/limit_dataset)
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+ - **Paper:** [LiMiT: The Literal Motion in Text Dataset](https://www.aclweb.org/anthology/2020.findings-emnlp.88/)
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+ - **Leaderboard:** N/A
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+ - **Point of Contact:** [More Information Needed]
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+
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+ ### Dataset Summary
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+
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+ Motion recognition is one of the basic cognitive capabilities of many life forms, yet identifying
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+ motion of physical entities in natural language have not been explored extensively and empirically.
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+ Literal-Motion-in-Text (LiMiT) dataset, is a large human-annotated collection of English text sentences
63
+ describing physical occurrence of motion, with annotated physical entities in motion.
64
+
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+ ### Supported Tasks and Leaderboards
66
+
67
+ [More Information Needed]
68
+
69
+ ### Languages
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+
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+ The text in the dataset is in English (`en`).
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+
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+ ## Dataset Structure
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+
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+ ### Data Instances
76
+
77
+ Example of one instance in the dataset
78
+
79
+ ```
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+ {
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+ "id": 0,
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+ "motion": "yes",
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+ "motion_entities": [
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+ {
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+ "entity": "little boy",
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+ "start_index": 2
87
+ },
88
+ {
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+ "entity": "ball",
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+ "start_index": 30
91
+ }
92
+ ],
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+ "sentence": " A little boy holding a yellow ball walks by."
94
+ }
95
+ ```
96
+
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+ ### Data Fields
98
+
99
+ - `id`: intger index of the example
100
+ - `motion`: indicates whether the sentence is literal motion i.e. describes the movement of a physical entity or not
101
+ - `motion_entities`: A `list` of `dicts` with following keys
102
+ - `entity`: the extracted entity in motion
103
+ - `start_index`: index in the sentence for the first char of the entity text
104
+
105
+ ### Data Splits
106
+
107
+ The dataset is split into a `train`, and `test` split with the following sizes:
108
+
109
+ | | Tain | Valid |
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+ | ----- | ------ | ----- |
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+ | Number of examples | 23559 | 1000 |
112
+
113
+ ## Dataset Creation
114
+
115
+ ### Curation Rationale
116
+
117
+ [More Information Needed]
118
+
119
+ ### Source Data
120
+
121
+ [More Information Needed]
122
+
123
+ #### Initial Data Collection and Normalization
124
+
125
+ [More Information Needed]
126
+
127
+ #### Who are the source language producers?
128
+
129
+ [More Information Needed]
130
+
131
+ ### Annotations
132
+
133
+ [More Information Needed]
134
+
135
+ #### Annotation process
136
+
137
+ [More Information Needed]
138
+
139
+ #### Who are the annotators?
140
+
141
+ [More Information Needed]
142
+
143
+ ### Personal and Sensitive Information
144
+
145
+ [More Information Needed]
146
+
147
+ ## Considerations for Using the Data
148
+
149
+ ### Social Impact of Dataset
150
+
151
+ [More Information Needed]
152
+
153
+ ### Discussion of Biases
154
+
155
+ [More Information Needed]
156
+
157
+ ### Other Known Limitations
158
+
159
+ [More Information Needed]
160
+
161
+ ## Additional Information
162
+
163
+ ### Dataset Curators
164
+
165
+ [More Information Needed]
166
+
167
+ ### Licensing Information
168
+
169
+ [More Information Needed]
170
+
171
+ ### Citation Information
172
+
173
+ ```
174
+ @inproceedings{manotas-etal-2020-limit,
175
+ title = "{L}i{M}i{T}: The Literal Motion in Text Dataset",
176
+ author = "Manotas, Irene and
177
+ Vo, Ngoc Phuoc An and
178
+ Sheinin, Vadim",
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+ booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
180
+ month = nov,
181
+ year = "2020",
182
+ address = "Online",
183
+ publisher = "Association for Computational Linguistics",
184
+ url = "https://www.aclweb.org/anthology/2020.findings-emnlp.88",
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+ doi = "10.18653/v1/2020.findings-emnlp.88",
186
+ pages = "991--1000",
187
+ abstract = "Motion recognition is one of the basic cognitive capabilities of many life forms, yet identifying motion of physical entities in natural language have not been explored extensively and empirically. We present the Literal-Motion-in-Text (LiMiT) dataset, a large human-annotated collection of English text sentences describing physical occurrence of motion, with annotated physical entities in motion. We describe the annotation process for the dataset, analyze its scale and diversity, and report results of several baseline models. We also present future research directions and applications of the LiMiT dataset and share it publicly as a new resource for the research community.",
188
+ }
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+ ```
dataset_infos.json ADDED
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+ {"default": {"description": "Motion recognition is one of the basic cognitive capabilities of many life forms, yet identifying motion of physical entities in natural language have not been explored extensively and empirically. Literal-Motion-in-Text (LiMiT) dataset, is a large human-annotated collection of English text sentences describing physical occurrence of motion, with annotated physical entities in motion.\n", "citation": "@inproceedings{manotas-etal-2020-limit,\n title = \"{L}i{M}i{T}: The Literal Motion in Text Dataset\",\n author = \"Manotas, Irene and\n Vo, Ngoc Phuoc An and\n Sheinin, Vadim\",\n booktitle = \"Findings of the Association for Computational Linguistics: EMNLP 2020\",\n month = nov,\n year = \"2020\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://www.aclweb.org/anthology/2020.findings-emnlp.88\",\n doi = \"10.18653/v1/2020.findings-emnlp.88\",\n pages = \"991--1000\",\n abstract = \"Motion recognition is one of the basic cognitive capabilities of many life forms, yet identifying motion of physical entities in natural language have not been explored extensively and empirically. We present the Literal-Motion-in-Text (LiMiT) dataset, a large human-annotated collection of English text sentences describing physical occurrence of motion, with annotated physical entities in motion. We describe the annotation process for the dataset, analyze its scale and diversity, and report results of several baseline models. We also present future research directions and applications of the LiMiT dataset and share it publicly as a new resource for the research community.\",\n}\n", "homepage": "https://github.com/ilmgut/limit_dataset", "license": "", "features": {"id": {"dtype": "int32", "id": null, "_type": "Value"}, "sentence": {"dtype": "string", "id": null, "_type": "Value"}, "motion": {"dtype": "string", "id": null, "_type": "Value"}, "motion_entities": [{"entity": {"dtype": "string", "id": null, "_type": "Value"}, "start_index": {"dtype": "int32", "id": null, "_type": "Value"}}]}, "post_processed": null, "supervised_keys": null, "builder_name": "limit", "config_name": "default", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 3064208, "num_examples": 23559, "dataset_name": "limit"}, "test": {"name": "test", "num_bytes": 139742, "num_examples": 1000, "dataset_name": "limit"}}, "download_checksums": {"https://raw.githubusercontent.com/ilmgut/limit_dataset/master/data/train.json": {"num_bytes": 4036108, "checksum": "2d4c1ffe768526c9ad2d9f04da4b29c590901705968c6d775b5e64881870520d"}, "https://raw.githubusercontent.com/ilmgut/limit_dataset/master/data/test.json": {"num_bytes": 178817, "checksum": "be0ce77065ee641673f3a6ecc3b94b98f5b60f7516c17f9afb4af2e46c7a7db6"}}, "download_size": 4214925, "post_processing_size": null, "dataset_size": 3203950, "size_in_bytes": 7418875}}
dummy/1.0.0/dummy_data.zip ADDED
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+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:4c8dc71628813df48bae032323f5088d3c36b9537da462e84258281c5a382b75
3
+ size 1054
limit.py ADDED
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+ # coding=utf-8
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+ # Copyright 2020 The HuggingFace Datasets Authors.
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+ #
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+ # Licensed under the Apache License, Version 2.0 (the "License");
5
+ # you may not use this file except in compliance with the License.
6
+ # You may obtain a copy of the License at
7
+ #
8
+ # http://www.apache.org/licenses/LICENSE-2.0
9
+ #
10
+ # Unless required by applicable law or agreed to in writing, software
11
+ # distributed under the License is distributed on an "AS IS" BASIS,
12
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13
+ # See the License for the specific language governing permissions and
14
+ # limitations under the License.
15
+ """LiMiT: The Literal Motion in Text Dataset"""
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+
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+ from __future__ import absolute_import, division, print_function
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+
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+ import json
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+
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+ import datasets
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+
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+
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+ _CITATION = """\
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+ @inproceedings{manotas-etal-2020-limit,
26
+ title = "{L}i{M}i{T}: The Literal Motion in Text Dataset",
27
+ author = "Manotas, Irene and
28
+ Vo, Ngoc Phuoc An and
29
+ Sheinin, Vadim",
30
+ booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
31
+ month = nov,
32
+ year = "2020",
33
+ address = "Online",
34
+ publisher = "Association for Computational Linguistics",
35
+ url = "https://www.aclweb.org/anthology/2020.findings-emnlp.88",
36
+ doi = "10.18653/v1/2020.findings-emnlp.88",
37
+ pages = "991--1000",
38
+ abstract = "Motion recognition is one of the basic cognitive capabilities of many life forms, yet identifying motion of physical entities in natural language have not been explored extensively and empirically. We present the Literal-Motion-in-Text (LiMiT) dataset, a large human-annotated collection of English text sentences describing physical occurrence of motion, with annotated physical entities in motion. We describe the annotation process for the dataset, analyze its scale and diversity, and report results of several baseline models. We also present future research directions and applications of the LiMiT dataset and share it publicly as a new resource for the research community.",
39
+ }
40
+ """
41
+
42
+ _DESCRIPTION = """\
43
+ Motion recognition is one of the basic cognitive capabilities of many life forms, yet identifying \
44
+ motion of physical entities in natural language have not been explored extensively and empirically. \
45
+ Literal-Motion-in-Text (LiMiT) dataset, is a large human-annotated collection of English text sentences \
46
+ describing physical occurrence of motion, with annotated physical entities in motion.
47
+ """
48
+
49
+ _HOMEPAGE = "https://github.com/ilmgut/limit_dataset"
50
+
51
+ _BASE_URL = "https://raw.githubusercontent.com/ilmgut/limit_dataset/master/data"
52
+
53
+ _URLS = {
54
+ "train": f"{_BASE_URL}/train.json",
55
+ "test": f"{_BASE_URL}/test.json",
56
+ }
57
+
58
+
59
+ class Limit(datasets.GeneratorBasedBuilder):
60
+ """LiMiT: The Literal Motion in Text Dataset"""
61
+
62
+ VERSION = datasets.Version("1.0.0")
63
+
64
+ def _info(self):
65
+ features = {
66
+ "id": datasets.Value("int32"),
67
+ "sentence": datasets.Value("string"),
68
+ "motion": datasets.Value("string"),
69
+ "motion_entities": [
70
+ {
71
+ "entity": datasets.Value("string"),
72
+ "start_index": datasets.Value("int32"),
73
+ }
74
+ ],
75
+ }
76
+ return datasets.DatasetInfo(
77
+ description=_DESCRIPTION,
78
+ features=datasets.Features(features),
79
+ supervised_keys=None,
80
+ homepage=_HOMEPAGE,
81
+ citation=_CITATION,
82
+ )
83
+
84
+ def _split_generators(self, dl_manager):
85
+ downloaded_files = dl_manager.download(_URLS)
86
+ return [
87
+ datasets.SplitGenerator(
88
+ name=datasets.Split.TRAIN,
89
+ gen_kwargs={"filepath": downloaded_files["train"]},
90
+ ),
91
+ datasets.SplitGenerator(
92
+ name=datasets.Split.TEST,
93
+ gen_kwargs={"filepath": downloaded_files["test"]},
94
+ ),
95
+ ]
96
+
97
+ def _generate_examples(self, filepath):
98
+ with open(filepath, encoding="utf-8") as f:
99
+ examples = json.load(f)
100
+ for idx, example in examples.items():
101
+ if example["motion_entity"] == "":
102
+ motion_entities = []
103
+ else:
104
+ motion_entities = example["motion_entity"].strip().split("\n")
105
+ motion_entities = [entity.split(":") for entity in motion_entities]
106
+ motion_entities = [
107
+ {"entity": entity, "start_index": int(start_idx)} for entity, start_idx in motion_entities
108
+ ]
109
+
110
+ example.pop("motion_entity")
111
+ example["motion_entities"] = motion_entities
112
+ example["id"] = idx
113
+
114
+ yield idx, example