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Update files from the datasets library (from 1.3.0)

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Release notes: https://github.com/huggingface/datasets/releases/tag/1.3.0

Files changed (5) hide show
  1. .gitattributes +27 -0
  2. README.md +187 -0
  3. dataset_infos.json +1 -0
  4. dummy/0.0.0/dummy_data.zip +3 -0
  5. sick.py +111 -0
.gitattributes ADDED
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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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+ *.bz2 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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+ *.xz filter=lfs diff=lfs merge=lfs -text
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+ *.zip filter=lfs diff=lfs merge=lfs -text
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+ *.zstandard filter=lfs diff=lfs merge=lfs -text
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+ *tfevents* filter=lfs diff=lfs merge=lfs -text
README.md ADDED
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1
+ ---
2
+ annotations_creators:
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+ - crowdsourced
4
+ language_creators:
5
+ - crowdsourced
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+ languages:
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+ - en
8
+ licenses:
9
+ - CC-BY-NC-SA-3-0
10
+ multilinguality:
11
+ - monolingual
12
+ size_categories:
13
+ - 1k<10K
14
+ source_datasets:
15
+ - extended|image-flickr-8k
16
+ - extended|semeval2012-sts-msr-video
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+ task_categories:
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+ - text-classification
19
+ task_ids:
20
+ - natural-language-inference
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+ ---
22
+
23
+ # Dataset Card for sick
24
+
25
+ ## Table of Contents
26
+ - [Dataset Description](#dataset-description)
27
+ - [Dataset Summary](#dataset-summary)
28
+ - [Supported Tasks](#supported-tasks-and-leaderboards)
29
+ - [Languages](#languages)
30
+ - [Dataset Structure](#dataset-structure)
31
+ - [Data Instances](#data-instances)
32
+ - [Data Fields](#data-instances)
33
+ - [Data Splits](#data-instances)
34
+ - [Dataset Creation](#dataset-creation)
35
+ - [Curation Rationale](#curation-rationale)
36
+ - [Source Data](#source-data)
37
+ - [Annotations](#annotations)
38
+ - [Personal and Sensitive Information](#personal-and-sensitive-information)
39
+ - [Considerations for Using the Data](#considerations-for-using-the-data)
40
+ - [Social Impact of Dataset](#social-impact-of-dataset)
41
+ - [Discussion of Biases](#discussion-of-biases)
42
+ - [Other Known Limitations](#other-known-limitations)
43
+ - [Additional Information](#additional-information)
44
+ - [Dataset Curators](#dataset-curators)
45
+ - [Licensing Information](#licensing-information)
46
+ - [Citation Information](#citation-information)
47
+ - [Contributions](#contributions)
48
+
49
+ ## Dataset Description
50
+
51
+ - **Homepage:** http://marcobaroni.org/composes/sick.html
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+ - **Repository:** [Needs More Information]
53
+ - **Paper:** https://www.aclweb.org/anthology/L14-1314/
54
+ - **Leaderboard:** [Needs More Information]
55
+ - **Point of Contact:** [Needs More Information]
56
+
57
+ ### Dataset Summary
58
+
59
+ Shared and internationally recognized benchmarks are fundamental for the development of any computational system. We aim to help the research community working on compositional distributional semantic models (CDSMs) by providing SICK (Sentences Involving Compositional Knowldedge), a large size English benchmark tailored for them. SICK consists of about 10,000 English sentence pairs that include many examples of the lexical, syntactic and semantic phenomena that CDSMs are expected to account for, but do not require dealing with other aspects of existing sentential data sets (idiomatic multiword expressions, named entities, telegraphic language) that are not within the scope of CDSMs. By means of crowdsourcing techniques, each pair was annotated for two crucial semantic tasks: relatedness in meaning (with a 5-point rating scale as gold score) and entailment relation between the two elements (with three possible gold labels: entailment, contradiction, and neutral). The SICK data set was used in SemEval-2014 Task 1, and it freely available for research purposes.
60
+
61
+
62
+ ### Supported Tasks and Leaderboards
63
+
64
+ [Needs More Information]
65
+
66
+ ### Languages
67
+
68
+ The dataset is in English.
69
+
70
+ ## Dataset Structure
71
+
72
+ ### Data Instances
73
+
74
+ Example instance:
75
+ ```
76
+ {
77
+ "entailment_AB": "A_neutral_B",
78
+ "entailment_BA": "B_neutral_A",
79
+ "label": 1,
80
+ "id": "1",
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+ "relatedness_score": 4.5,
82
+ "sentence_A": "A group of kids is playing in a yard and an old man is standing in the background",
83
+ "sentence_A_dataset": "FLICKR",
84
+ "sentence_A_original": "A group of children playing in a yard, a man in the background.",
85
+ "sentence_B": "A group of boys in a yard is playing and a man is standing in the background",
86
+ "sentence_B_dataset": "FLICKR",
87
+ "sentence_B_original": "A group of children playing in a yard, a man in the background."
88
+ }
89
+ ```
90
+
91
+ ### Data Fields
92
+
93
+ - pair_ID: sentence pair ID
94
+ - sentence_A: sentence A
95
+ - sentence_B: sentence B
96
+ - label: textual entailment gold label: entailment (0), neutral (1) or contradiction (2)
97
+ - relatedness_score: semantic relatedness gold score (on a 1-5 continuous scale)
98
+ - entailment_AB: entailment for the A-B order (A_neutral_B, A_entails_B, or A_contradicts_B)
99
+ - entailment_BA: entailment for the B-A order (B_neutral_A, B_entails_A, or B_contradicts_A)
100
+ - sentence_A_original: original sentence from which sentence A is derived
101
+ - sentence_B_original: original sentence from which sentence B is derived
102
+ - sentence_A_dataset: dataset from which the original sentence A was extracted (FLICKR vs. SEMEVAL)
103
+ - sentence_B_dataset: dataset from which the original sentence B was extracted (FLICKR vs. SEMEVAL)
104
+
105
+ ### Data Splits
106
+
107
+ Train Trial Test
108
+ 4439 495 4906
109
+
110
+ ## Dataset Creation
111
+
112
+ ### Curation Rationale
113
+
114
+ [Needs More Information]
115
+
116
+ ### Source Data
117
+
118
+ #### Initial Data Collection and Normalization
119
+
120
+ [Needs More Information]
121
+
122
+ #### Who are the source language producers?
123
+
124
+ [Needs More Information]
125
+
126
+ ### Annotations
127
+
128
+ #### Annotation process
129
+
130
+ [Needs More Information]
131
+
132
+ #### Who are the annotators?
133
+
134
+ [Needs More Information]
135
+
136
+ ### Personal and Sensitive Information
137
+
138
+ [Needs More Information]
139
+
140
+ ## Considerations for Using the Data
141
+
142
+ ### Social Impact of Dataset
143
+
144
+ [Needs More Information]
145
+
146
+ ### Discussion of Biases
147
+
148
+ [Needs More Information]
149
+
150
+ ### Other Known Limitations
151
+
152
+ [Needs More Information]
153
+
154
+ ## Additional Information
155
+
156
+ ### Dataset Curators
157
+
158
+ [Needs More Information]
159
+
160
+ ### Licensing Information
161
+
162
+ [Needs More Information]
163
+
164
+ ### Citation Information
165
+
166
+ ```
167
+ @inproceedings{marelli-etal-2014-sick,
168
+ title = "A {SICK} cure for the evaluation of compositional distributional semantic models",
169
+ author = "Marelli, Marco and
170
+ Menini, Stefano and
171
+ Baroni, Marco and
172
+ Bentivogli, Luisa and
173
+ Bernardi, Raffaella and
174
+ Zamparelli, Roberto",
175
+ booktitle = "Proceedings of the Ninth International Conference on Language Resources and Evaluation ({LREC}'14)",
176
+ month = may,
177
+ year = "2014",
178
+ address = "Reykjavik, Iceland",
179
+ publisher = "European Language Resources Association (ELRA)",
180
+ url = "http://www.lrec-conf.org/proceedings/lrec2014/pdf/363_Paper.pdf",
181
+ pages = "216--223",
182
+ }
183
+ ```
184
+
185
+ ### Contributions
186
+
187
+ Thanks to [@calpt](https://github.com/calpt) for adding this dataset.
dataset_infos.json ADDED
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+ {"default": {"description": "Shared and internationally recognized benchmarks are fundamental for the development of any computational system.\nWe aim to help the research community working on compositional distributional semantic models (CDSMs) by providing SICK (Sentences Involving Compositional Knowldedge), a large size English benchmark tailored for them.\nSICK consists of about 10,000 English sentence pairs that include many examples of the lexical, syntactic and semantic phenomena that CDSMs are expected to account for, but do not require dealing with other aspects of existing sentential data sets (idiomatic multiword expressions, named entities, telegraphic language) that are not within the scope of CDSMs.\nBy means of crowdsourcing techniques, each pair was annotated for two crucial semantic tasks: relatedness in meaning (with a 5-point rating scale as gold score) and entailment relation between the two elements (with three possible gold labels: entailment, contradiction, and neutral).\nThe SICK data set was used in SemEval-2014 Task 1, and it freely available for research purposes.\n", "citation": "@inproceedings{marelli-etal-2014-sick,\n title = \"A {SICK} cure for the evaluation of compositional distributional semantic models\",\n author = \"Marelli, Marco and\n Menini, Stefano and\n Baroni, Marco and\n Bentivogli, Luisa and\n Bernardi, Raffaella and\n Zamparelli, Roberto\",\n booktitle = \"Proceedings of the Ninth International Conference on Language Resources and Evaluation ({LREC}'14)\",\n month = may,\n year = \"2014\",\n address = \"Reykjavik, Iceland\",\n publisher = \"European Language Resources Association (ELRA)\",\n url = \"http://www.lrec-conf.org/proceedings/lrec2014/pdf/363_Paper.pdf\",\n pages = \"216--223\",\n}\n", "homepage": "http://marcobaroni.org/composes/sick.html", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "sentence_A": {"dtype": "string", "id": null, "_type": "Value"}, "sentence_B": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 3, "names": ["entailment", "neutral", "contradiction"], "names_file": null, "id": null, "_type": "ClassLabel"}, "relatedness_score": {"dtype": "float32", "id": null, "_type": "Value"}, "entailment_AB": {"dtype": "string", "id": null, "_type": "Value"}, "entailment_BA": {"dtype": "string", "id": null, "_type": "Value"}, "sentence_A_original": {"dtype": "string", "id": null, "_type": "Value"}, "sentence_B_original": {"dtype": "string", "id": null, "_type": "Value"}, "sentence_A_dataset": {"dtype": "string", "id": null, "_type": "Value"}, "sentence_B_dataset": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "sick", "config_name": "default", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 1180530, "num_examples": 4439, "dataset_name": "sick"}, "validation": {"name": "validation", "num_bytes": 132913, "num_examples": 495, "dataset_name": "sick"}, "test": {"name": "test", "num_bytes": 1305846, "num_examples": 4906, "dataset_name": "sick"}}, "download_checksums": {"https://zenodo.org/record/2787612/files/SICK.zip?download=1": {"num_bytes": 217584, "checksum": "da272af924399fc2de4889ed55383e38822c10d16c4a949a89d597d09a0b7912"}}, "download_size": 217584, "post_processing_size": null, "dataset_size": 2619289, "size_in_bytes": 2836873}}
dummy/0.0.0/dummy_data.zip ADDED
@@ -0,0 +1,3 @@
 
 
 
 
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:adc4a79ab8d38559fe7666d6d1f6df1401ecec6a7ba9835e5dfe15406b5cf545
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+ size 1091
sick.py ADDED
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1
+ # coding=utf-8
2
+ # Copyright 2021 The HuggingFace Datasets Authors and the current dataset script contributor.
3
+ #
4
+ # 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
+ import os
16
+
17
+ import datasets
18
+
19
+
20
+ _CITATION = """\
21
+ @inproceedings{marelli-etal-2014-sick,
22
+ title = "A {SICK} cure for the evaluation of compositional distributional semantic models",
23
+ author = "Marelli, Marco and
24
+ Menini, Stefano and
25
+ Baroni, Marco and
26
+ Bentivogli, Luisa and
27
+ Bernardi, Raffaella and
28
+ Zamparelli, Roberto",
29
+ booktitle = "Proceedings of the Ninth International Conference on Language Resources and Evaluation ({LREC}'14)",
30
+ month = may,
31
+ year = "2014",
32
+ address = "Reykjavik, Iceland",
33
+ publisher = "European Language Resources Association (ELRA)",
34
+ url = "http://www.lrec-conf.org/proceedings/lrec2014/pdf/363_Paper.pdf",
35
+ pages = "216--223",
36
+ }
37
+ """
38
+
39
+ _DESCRIPTION = """\
40
+ Shared and internationally recognized benchmarks are fundamental for the development of any computational system.
41
+ We aim to help the research community working on compositional distributional semantic models (CDSMs) by providing SICK (Sentences Involving Compositional Knowldedge), a large size English benchmark tailored for them.
42
+ SICK consists of about 10,000 English sentence pairs that include many examples of the lexical, syntactic and semantic phenomena that CDSMs are expected to account for, but do not require dealing with other aspects of existing sentential data sets (idiomatic multiword expressions, named entities, telegraphic language) that are not within the scope of CDSMs.
43
+ By means of crowdsourcing techniques, each pair was annotated for two crucial semantic tasks: relatedness in meaning (with a 5-point rating scale as gold score) and entailment relation between the two elements (with three possible gold labels: entailment, contradiction, and neutral).
44
+ The SICK data set was used in SemEval-2014 Task 1, and it freely available for research purposes.
45
+ """
46
+
47
+ _DOWNLOAD_URL = "https://zenodo.org/record/2787612/files/SICK.zip?download=1"
48
+
49
+
50
+ class SICK(datasets.GeneratorBasedBuilder):
51
+ """The SICK (Sentences Involving Compositional Knowldedge) dataset."""
52
+
53
+ def _info(self):
54
+ return datasets.DatasetInfo(
55
+ description=_DESCRIPTION,
56
+ features=datasets.Features(
57
+ {
58
+ "id": datasets.Value("string"),
59
+ "sentence_A": datasets.Value("string"),
60
+ "sentence_B": datasets.Value("string"),
61
+ "label": datasets.features.ClassLabel(names=["entailment", "neutral", "contradiction"]),
62
+ "relatedness_score": datasets.Value("float"),
63
+ "entailment_AB": datasets.Value("string"),
64
+ "entailment_BA": datasets.Value("string"),
65
+ "sentence_A_original": datasets.Value("string"),
66
+ "sentence_B_original": datasets.Value("string"),
67
+ "sentence_A_dataset": datasets.Value("string"),
68
+ "sentence_B_dataset": datasets.Value("string"),
69
+ }
70
+ ),
71
+ supervised_keys=None,
72
+ homepage="http://marcobaroni.org/composes/sick.html",
73
+ citation=_CITATION,
74
+ )
75
+
76
+ def _split_generators(self, dl_manager):
77
+ dl_dir = dl_manager.download_and_extract(_DOWNLOAD_URL)
78
+
79
+ return [
80
+ datasets.SplitGenerator(
81
+ name=datasets.Split.TRAIN,
82
+ gen_kwargs={"filepath": os.path.join(dl_dir, "SICK.txt"), "key": "TRAIN"},
83
+ ),
84
+ datasets.SplitGenerator(
85
+ name=datasets.Split.VALIDATION,
86
+ gen_kwargs={"filepath": os.path.join(dl_dir, "SICK.txt"), "key": "TRIAL"},
87
+ ),
88
+ datasets.SplitGenerator(
89
+ name=datasets.Split.TEST,
90
+ gen_kwargs={"filepath": os.path.join(dl_dir, "SICK.txt"), "key": "TEST"},
91
+ ),
92
+ ]
93
+
94
+ def _generate_examples(self, filepath, key):
95
+ with open(filepath, "r", encoding="utf-8") as f:
96
+ for line in f:
97
+ data = [s.strip() for s in line.split("\t")]
98
+ if data[-1] == key:
99
+ yield data[0], {
100
+ "id": data[0],
101
+ "sentence_A": data[1],
102
+ "sentence_B": data[2],
103
+ "label": data[3].lower(),
104
+ "relatedness_score": data[4],
105
+ "entailment_AB": data[5],
106
+ "entailment_BA": data[6],
107
+ "sentence_A_original": data[7],
108
+ "sentence_B_original": data[8],
109
+ "sentence_A_dataset": data[9],
110
+ "sentence_B_dataset": data[10],
111
+ }