Datasets:
Commit
•
8224680
1
Parent(s):
79a0451
Update parquet files
Browse files- .gitattributes +0 -37
- README.md +0 -215
- data/lm/.gitattributes +0 -3
- data/mt/.gitattributes +0 -3
- data/mt/en_fr_test.jsonl +0 -3
- data/mt/en_ru_test.jsonl +0 -3
- dataset_infos.json +0 -1
- data/lm/en_fr_test.jsonl → lm_en_de/wino_x-test.parquet +2 -2
- data/lm/en_ru_test.jsonl → lm_en_fr/wino_x-test.parquet +2 -2
- data/mt/en_de_test.jsonl → lm_en_ru/wino_x-test.parquet +2 -2
- data/lm/en_de_test.jsonl → mt_en_de/wino_x-test.parquet +2 -2
- mt_en_fr/wino_x-test.parquet +3 -0
- mt_en_ru/wino_x-test.parquet +3 -0
- wino_x.py +0 -168
.gitattributes
DELETED
@@ -1,37 +0,0 @@
|
|
1 |
-
*.7z filter=lfs diff=lfs merge=lfs -text
|
2 |
-
*.arrow filter=lfs diff=lfs merge=lfs -text
|
3 |
-
*.bin filter=lfs diff=lfs merge=lfs -text
|
4 |
-
*.bz2 filter=lfs diff=lfs merge=lfs -text
|
5 |
-
*.ftz filter=lfs diff=lfs merge=lfs -text
|
6 |
-
*.gz filter=lfs diff=lfs merge=lfs -text
|
7 |
-
*.h5 filter=lfs diff=lfs merge=lfs -text
|
8 |
-
*.joblib filter=lfs diff=lfs merge=lfs -text
|
9 |
-
*.lfs.* filter=lfs diff=lfs merge=lfs -text
|
10 |
-
*.model filter=lfs diff=lfs merge=lfs -text
|
11 |
-
*.msgpack filter=lfs diff=lfs merge=lfs -text
|
12 |
-
*.onnx filter=lfs diff=lfs merge=lfs -text
|
13 |
-
*.ot filter=lfs diff=lfs merge=lfs -text
|
14 |
-
*.parquet filter=lfs diff=lfs merge=lfs -text
|
15 |
-
*.pb filter=lfs diff=lfs merge=lfs -text
|
16 |
-
*.pt filter=lfs diff=lfs merge=lfs -text
|
17 |
-
*.pth filter=lfs diff=lfs merge=lfs -text
|
18 |
-
*.rar filter=lfs diff=lfs merge=lfs -text
|
19 |
-
saved_model/**/* filter=lfs diff=lfs merge=lfs -text
|
20 |
-
*.tar.* filter=lfs diff=lfs merge=lfs -text
|
21 |
-
*.tflite filter=lfs diff=lfs merge=lfs -text
|
22 |
-
*.tgz filter=lfs diff=lfs merge=lfs -text
|
23 |
-
*.wasm filter=lfs diff=lfs merge=lfs -text
|
24 |
-
*.xz filter=lfs diff=lfs merge=lfs -text
|
25 |
-
*.zip filter=lfs diff=lfs merge=lfs -text
|
26 |
-
*.zstandard filter=lfs diff=lfs merge=lfs -text
|
27 |
-
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
28 |
-
# Audio files - uncompressed
|
29 |
-
*.pcm filter=lfs diff=lfs merge=lfs -text
|
30 |
-
*.sam filter=lfs diff=lfs merge=lfs -text
|
31 |
-
*.raw filter=lfs diff=lfs merge=lfs -text
|
32 |
-
# Audio files - compressed
|
33 |
-
*.aac filter=lfs diff=lfs merge=lfs -text
|
34 |
-
*.flac filter=lfs diff=lfs merge=lfs -text
|
35 |
-
*.mp3 filter=lfs diff=lfs merge=lfs -text
|
36 |
-
*.ogg filter=lfs diff=lfs merge=lfs -text
|
37 |
-
*.wav filter=lfs diff=lfs merge=lfs -text
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
README.md
DELETED
@@ -1,215 +0,0 @@
|
|
1 |
-
---
|
2 |
-
annotations_creators:
|
3 |
-
- no-annotation
|
4 |
-
language:
|
5 |
-
- en
|
6 |
-
- de
|
7 |
-
- fr
|
8 |
-
- ru
|
9 |
-
language_creators:
|
10 |
-
- machine-generated
|
11 |
-
- expert-generated
|
12 |
-
license:
|
13 |
-
- mit
|
14 |
-
multilinguality:
|
15 |
-
- multilingual
|
16 |
-
- translation
|
17 |
-
pretty_name: Wino-X
|
18 |
-
size_categories:
|
19 |
-
- 1K<n<10K
|
20 |
-
source_datasets:
|
21 |
-
- original
|
22 |
-
task_categories:
|
23 |
-
- translation
|
24 |
-
- coreference resolution
|
25 |
-
- commonsense reasoning
|
26 |
-
task_ids:
|
27 |
-
- multiple-choice-qa
|
28 |
-
- language-modeling
|
29 |
-
---
|
30 |
-
|
31 |
-
# Dataset Card for Wino-X
|
32 |
-
|
33 |
-
## Table of Contents
|
34 |
-
- [Dataset Description](#dataset-description)
|
35 |
-
- [Dataset Summary](#dataset-summary)
|
36 |
-
- [Supported Tasks](#supported-tasks-and-leaderboards)
|
37 |
-
- [Languages](#languages)
|
38 |
-
- [Dataset Structure](#dataset-structure)
|
39 |
-
- [Data Instances](#data-instances)
|
40 |
-
- [Data Fields](#data-instances)
|
41 |
-
- [Data Splits](#data-instances)
|
42 |
-
- [Dataset Creation](#dataset-creation)
|
43 |
-
- [Curation Rationale](#curation-rationale)
|
44 |
-
- [Source Data](#source-data)
|
45 |
-
- [Annotations](#annotations)
|
46 |
-
- [Personal and Sensitive Information](#personal-and-sensitive-information)
|
47 |
-
- [Considerations for Using the Data](#considerations-for-using-the-data)
|
48 |
-
- [Social Impact of Dataset](#social-impact-of-dataset)
|
49 |
-
- [Discussion of Biases](#discussion-of-biases)
|
50 |
-
- [Other Known Limitations](#other-known-limitations)
|
51 |
-
- [Additional Information](#additional-information)
|
52 |
-
- [Dataset Curators](#dataset-curators)
|
53 |
-
- [Licensing Information](#licensing-information)
|
54 |
-
- [Citation Information](#citation-information)
|
55 |
-
|
56 |
-
## Dataset Description
|
57 |
-
|
58 |
-
- **Homepage:** [Wino-X repository](https://github.com/demelin/Wino-X)
|
59 |
-
- **Repository:** [Wino-X repository](https://github.com/demelin/Wino-X)
|
60 |
-
- **Paper:** [Wino-X: Multilingual Winograd Schemas for Commonsense Reasoning and Coreference Resolution](https://aclanthology.org/2021.emnlp-main.670/)
|
61 |
-
- **Leaderboard:** [N/A]
|
62 |
-
- **Point of Contact:** [Denis Emelin](demelin.github.io)
|
63 |
-
|
64 |
-
### Dataset Summary
|
65 |
-
|
66 |
-
Wino-X is a parallel dataset of German, French, and Russian Winograd schemas, aligned with their English
|
67 |
-
counterparts, used to examine whether neural machine translation models can perform coreference resolution that
|
68 |
-
requires commonsense knowledge, and whether multilingual language models are capable of commonsense reasoning across
|
69 |
-
multiple languages.
|
70 |
-
|
71 |
-
### Supported Tasks and Leaderboards
|
72 |
-
|
73 |
-
- translation: The dataset can be used to evaluate translations of ambiguous source sentences, as produced by translation models . A [pretrained transformer-based NMT model](https://huggingface.co/Helsinki-NLP/opus-mt-en-de) can be used for this purpose.
|
74 |
-
- coreference-resolution: The dataset can be used to rank alternative translations of an ambiguous source sentence that differ in the chosen referent of an ambiguous source pronoun. A [pretrained transformer-based NMT model](https://huggingface.co/Helsinki-NLP/opus-mt-en-de) can be used for this purpose.
|
75 |
-
- commonsense-reasoning: The dataset can also be used evaluate whether pretrained multilingual language models can perform commonsense reasoning in (or across) multiple languages by identifying the correct filler in a cloze completion task. An [XLM-based model](https://huggingface.co/xlm-roberta-base) can be used for this purpose.
|
76 |
-
|
77 |
-
### Languages
|
78 |
-
|
79 |
-
The dataset (both its MT and LM portions) is available in the following translation pairs: English-German, English-French, English-Russian. All English sentences included in *Wino-X* were extracted from publicly available parallel corpora, as detailed in the accompanying paper, and represent the dataset-specific language varieties. All non-English sentences were obtained through machine translation and may, as such, exhibit features of translationese.
|
80 |
-
|
81 |
-
## Dataset Structure
|
82 |
-
|
83 |
-
### Data Instances
|
84 |
-
|
85 |
-
The following represents a typical *MT-Wino-X* instance (for the English-German translation pair):
|
86 |
-
|
87 |
-
{"qID": "3UDTAB6HH8D37OQL3O6F3GXEEOF09Z-1",
|
88 |
-
"sentence": "The woman looked for a different vase for the bouquet because it was too small.",
|
89 |
-
"translation1": "Die Frau suchte nach einer anderen Vase für den Blumenstrauß, weil sie zu klein war.",
|
90 |
-
"translation2": "Die Frau suchte nach einer anderen Vase für den Blumenstrauß, weil er zu klein war.",
|
91 |
-
"answer": 1,
|
92 |
-
"pronoun1": "sie",
|
93 |
-
"pronoun2": "er",
|
94 |
-
"referent1_en": "vase",
|
95 |
-
"referent2_en": "bouquet",
|
96 |
-
"true_translation_referent_of_pronoun1_de": "Vase",
|
97 |
-
"true_translation_referent_of_pronoun2_de": "Blumenstrauß",
|
98 |
-
"false_translation_referent_of_pronoun1_de": "Vase",
|
99 |
-
"false_translation_referent_of_pronoun2_de": "Blumenstrauß"}
|
100 |
-
|
101 |
-
|
102 |
-
The following represents a typical *LM-Wino-X* instance (for the English-French translation pair):
|
103 |
-
|
104 |
-
{"qID": "3UDTAB6HH8D37OQL3O6F3GXEEOF09Z-1",
|
105 |
-
"sentence": "The woman looked for a different vase for the bouquet because it was too small.",
|
106 |
-
"context_en": "The woman looked for a different vase for the bouquet because _ was too small.",
|
107 |
-
"context_fr": "La femme a cherché un vase différent pour le bouquet car _ était trop petit.",
|
108 |
-
"option1_en": "the bouquet",
|
109 |
-
"option2_en": "the vase",
|
110 |
-
"option1_fr": "le bouquet",
|
111 |
-
"option2_fr": "le vase",
|
112 |
-
"answer": 2,
|
113 |
-
"context_referent_of_option1_fr": "bouquet",
|
114 |
-
"context_referent_of_option2_fr": "vase"}
|
115 |
-
|
116 |
-
### Data Fields
|
117 |
-
|
118 |
-
For *MT-Wino-X*:
|
119 |
-
|
120 |
-
- "qID": Unique identifier ID for this dataset instance.
|
121 |
-
- "sentence": English sentence containing the ambiguous pronoun 'it'.
|
122 |
-
- "translation1": First translation candidate.
|
123 |
-
- "translation2": Second translation candidate.
|
124 |
-
- "answer": ID of the correct translation.
|
125 |
-
- "pronoun1": Translation of the ambiguous source pronoun in translation1.
|
126 |
-
- "pronoun2": Translation of the ambiguous source pronoun in translation2.
|
127 |
-
- "referent1_en": English referent of the translation of the ambiguous source pronoun in translation1.
|
128 |
-
- "referent2_en": English referent of the translation of the ambiguous source pronoun in translation2.
|
129 |
-
- "true_translation_referent_of_pronoun1_[TGT-LANG]": Target language referent of pronoun1 in the correct translation.
|
130 |
-
- "true_translation_referent_of_pronoun2_[TGT-LANG]": Target language referent of pronoun2 in the correct translation.
|
131 |
-
- "false_translation_referent_of_pronoun1_[TGT-LANG]": Target language referent of pronoun1 in the incorrect translation.
|
132 |
-
- "false_translation_referent_of_pronoun2_[TGT-LANG]": Target language referent of pronoun2 in the incorrect translation.
|
133 |
-
|
134 |
-
|
135 |
-
For *LM-Wino-X*:
|
136 |
-
|
137 |
-
- "qID": Unique identifier ID for this dataset instance.
|
138 |
-
- "sentence": English sentence containing the ambiguous pronoun 'it'.
|
139 |
-
- "context_en": Same English sentence, where 'it' is replaced by a gap.
|
140 |
-
- "context_fr": Target language translation of the English sentence, where the translation of 'it' is replaced by a gap.
|
141 |
-
- "option1_en": First filler option for the English sentence.
|
142 |
-
- "option2_en": Second filler option for the English sentence.
|
143 |
-
- "option1_[TGT-LANG]": First filler option for the target language sentence.
|
144 |
-
- "option2_[TGT-LANG]": Second filler option for the target language sentence.
|
145 |
-
- "answer": ID of the correct gap filler.
|
146 |
-
- "context_referent_of_option1_[TGT-LANG]": English translation of option1_[TGT-LANG].
|
147 |
-
- "context_referent_of_option2_[TGT-LANG]": English translation of option2_[TGT-LANG]
|
148 |
-
|
149 |
-
### Data Splits
|
150 |
-
|
151 |
-
*Wno-X* was designed as an evaluation-only benchmark and therefore is intended to be used for zero-shot testing only. However, users are very welcome to split the data as they wish :) .
|
152 |
-
|
153 |
-
|
154 |
-
## Dataset Creation
|
155 |
-
|
156 |
-
### Curation Rationale
|
157 |
-
|
158 |
-
Please refer to [Section 2 in the dataset paper](https://aclanthology.org/2021.emnlp-main.670.pdf).
|
159 |
-
|
160 |
-
### Source Data
|
161 |
-
|
162 |
-
#### Initial Data Collection and Normalization
|
163 |
-
|
164 |
-
Please refer to [Section 2 in the dataset paper](https://aclanthology.org/2021.emnlp-main.670.pdf).
|
165 |
-
|
166 |
-
#### Who are the source language producers?
|
167 |
-
|
168 |
-
Please refer to [Section 2 in the dataset paper](https://aclanthology.org/2021.emnlp-main.670.pdf).
|
169 |
-
|
170 |
-
### Annotations
|
171 |
-
|
172 |
-
#### Annotation process
|
173 |
-
|
174 |
-
Please refer to [Section 2 in the dataset paper](https://aclanthology.org/2021.emnlp-main.670.pdf).
|
175 |
-
|
176 |
-
#### Who are the annotators?
|
177 |
-
|
178 |
-
Annotations were generated automatically and verified by the dataset author / curator for correctness.
|
179 |
-
|
180 |
-
### Personal and Sensitive Information
|
181 |
-
|
182 |
-
[N/A]
|
183 |
-
|
184 |
-
## Considerations for Using the Data
|
185 |
-
|
186 |
-
### Social Impact of Dataset
|
187 |
-
|
188 |
-
Please refer to ['Ethical Considerations' in the dataset paper](https://aclanthology.org/2021.emnlp-main.670.pdf).
|
189 |
-
|
190 |
-
### Discussion of Biases
|
191 |
-
|
192 |
-
Please refer to ['Ethical Considerations' in the dataset paper](https://aclanthology.org/2021.emnlp-main.670.pdf).
|
193 |
-
|
194 |
-
### Other Known Limitations
|
195 |
-
|
196 |
-
Please refer to ['Ethical Considerations' in the dataset paper](https://aclanthology.org/2021.emnlp-main.670.pdf).
|
197 |
-
|
198 |
-
## Additional Information
|
199 |
-
|
200 |
-
### Dataset Curators
|
201 |
-
|
202 |
-
[Denis Emelin](demelin.github.io)
|
203 |
-
|
204 |
-
### Licensing Information
|
205 |
-
|
206 |
-
MIT
|
207 |
-
|
208 |
-
### Citation Information
|
209 |
-
|
210 |
-
@inproceedings{Emelin2021WinoXMW,
|
211 |
-
title={Wino-X: Multilingual Winograd Schemas for Commonsense Reasoning and Coreference Resolution},
|
212 |
-
author={Denis Emelin and Rico Sennrich},
|
213 |
-
booktitle={EMNLP},
|
214 |
-
year={2021}
|
215 |
-
}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
data/lm/.gitattributes
DELETED
@@ -1,3 +0,0 @@
|
|
1 |
-
en_de_test.jsonl filter=lfs diff=lfs merge=lfs -text
|
2 |
-
en_fr_test.jsonl filter=lfs diff=lfs merge=lfs -text
|
3 |
-
en_ru_test.jsonl filter=lfs diff=lfs merge=lfs -text
|
|
|
|
|
|
|
|
data/mt/.gitattributes
DELETED
@@ -1,3 +0,0 @@
|
|
1 |
-
en_de_test.jsonl filter=lfs diff=lfs merge=lfs -text
|
2 |
-
en_fr_test.jsonl filter=lfs diff=lfs merge=lfs -text
|
3 |
-
en_ru_test.jsonl filter=lfs diff=lfs merge=lfs -text
|
|
|
|
|
|
|
|
data/mt/en_fr_test.jsonl
DELETED
@@ -1,3 +0,0 @@
|
|
1 |
-
version https://git-lfs.github.com/spec/v1
|
2 |
-
oid sha256:b5b64309b596769e1faabdf2c302c5fe9a0869d972ce39000015f0d8d76c72bb
|
3 |
-
size 2189704
|
|
|
|
|
|
|
|
data/mt/en_ru_test.jsonl
DELETED
@@ -1,3 +0,0 @@
|
|
1 |
-
version https://git-lfs.github.com/spec/v1
|
2 |
-
oid sha256:94f232e299b1f27cb97cdae15e25f2e6157b974abfda0754fca91a6d5bea0d7d
|
3 |
-
size 1965547
|
|
|
|
|
|
|
|
dataset_infos.json
DELETED
@@ -1 +0,0 @@
|
|
1 |
-
{"mt_en_de": {"description": "Wino-X is a parallel dataset of German, French, and Russian Winograd schemas, aligned with their English \ncounterparts, used to examine whether neural machine translation models can perform coreference resolution that \nrequires commonsense knowledge and whether multilingual language models are capable of commonsense reasoning across \nmultiple languages.\n", "citation": "@inproceedings{Emelin2021WinoXMW,\n title={Wino-X: Multilingual Winograd Schemas for Commonsense Reasoning and Coreference Resolution},\n author={Denis Emelin and Rico Sennrich},\n booktitle={EMNLP},\n year={2021}\n}\n", "homepage": "https://github.com/demelin/Wino-X", "license": "MIT", "features": {"qID": {"dtype": "string", "id": null, "_type": "Value"}, "sentence": {"dtype": "string", "id": null, "_type": "Value"}, "translation1": {"dtype": "string", "id": null, "_type": "Value"}, "translation2": {"dtype": "string", "id": null, "_type": "Value"}, "answer": {"dtype": "int64", "id": null, "_type": "Value"}, "pronoun1": {"dtype": "string", "id": null, "_type": "Value"}, "pronoun2": {"dtype": "string", "id": null, "_type": "Value"}, "referent1_en": {"dtype": "string", "id": null, "_type": "Value"}, "referent2_en": {"dtype": "string", "id": null, "_type": "Value"}, "true_translation_referent_of_pronoun1_de": {"dtype": "string", "id": null, "_type": "Value"}, "true_translation_referent_of_pronoun2_de": {"dtype": "string", "id": null, "_type": "Value"}, "false_translation_referent_of_pronoun1_de": {"dtype": "string", "id": null, "_type": "Value"}, "false_translation_referent_of_pronoun2_de": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "wino_x", "config_name": "mt_en_de", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"test": {"name": "test", "num_bytes": 1601793, "num_examples": 3774, "dataset_name": "wino_x"}}, "download_checksums": {"https://huggingface.co/datasets/demelin/wino_x/resolve/main/data/mt/en_de_test.jsonl": {"num_bytes": 2700026, "checksum": "1d18e0dcff2aae54b0ae28a43b61483e97225451d7286e4224657b2b3d93a03a"}}, "download_size": 2700026, "post_processing_size": null, "dataset_size": 1601793, "size_in_bytes": 4301819}, "mt_en_fr": {"description": "Wino-X is a parallel dataset of German, French, and Russian Winograd schemas, aligned with their English \ncounterparts, used to examine whether neural machine translation models can perform coreference resolution that \nrequires commonsense knowledge and whether multilingual language models are capable of commonsense reasoning across \nmultiple languages.\n", "citation": "@inproceedings{Emelin2021WinoXMW,\n title={Wino-X: Multilingual Winograd Schemas for Commonsense Reasoning and Coreference Resolution},\n author={Denis Emelin and Rico Sennrich},\n booktitle={EMNLP},\n year={2021}\n}\n", "homepage": "https://github.com/demelin/Wino-X", "license": "MIT", "features": {"qID": {"dtype": "string", "id": null, "_type": "Value"}, "sentence": {"dtype": "string", "id": null, "_type": "Value"}, "translation1": {"dtype": "string", "id": null, "_type": "Value"}, "translation2": {"dtype": "string", "id": null, "_type": "Value"}, "answer": {"dtype": "int64", "id": null, "_type": "Value"}, "pronoun1": {"dtype": "string", "id": null, "_type": "Value"}, "pronoun2": {"dtype": "string", "id": null, "_type": "Value"}, "referent1_en": {"dtype": "string", "id": null, "_type": "Value"}, "referent2_en": {"dtype": "string", "id": null, "_type": "Value"}, "true_translation_referent_of_pronoun1_fr": {"dtype": "string", "id": null, "_type": "Value"}, "true_translation_referent_of_pronoun2_fr": {"dtype": "string", "id": null, "_type": "Value"}, "false_translation_referent_of_pronoun1_fr": {"dtype": "string", "id": null, "_type": "Value"}, "false_translation_referent_of_pronoun2_fr": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "wino_x", "config_name": "mt_en_fr", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"test": {"name": "test", "num_bytes": 1320197, "num_examples": 2988, "dataset_name": "wino_x"}}, "download_checksums": {"https://huggingface.co/datasets/demelin/wino_x/resolve/main/data/mt/en_fr_test.jsonl": {"num_bytes": 2189704, "checksum": "b5b64309b596769e1faabdf2c302c5fe9a0869d972ce39000015f0d8d76c72bb"}}, "download_size": 2189704, "post_processing_size": null, "dataset_size": 1320197, "size_in_bytes": 3509901}, "mt_en_ru": {"description": "Wino-X is a parallel dataset of German, French, and Russian Winograd schemas, aligned with their English \ncounterparts, used to examine whether neural machine translation models can perform coreference resolution that \nrequires commonsense knowledge and whether multilingual language models are capable of commonsense reasoning across \nmultiple languages.\n", "citation": "@inproceedings{Emelin2021WinoXMW,\n title={Wino-X: Multilingual Winograd Schemas for Commonsense Reasoning and Coreference Resolution},\n author={Denis Emelin and Rico Sennrich},\n booktitle={EMNLP},\n year={2021}\n}\n", "homepage": "https://github.com/demelin/Wino-X", "license": "MIT", "features": {"qID": {"dtype": "string", "id": null, "_type": "Value"}, "sentence": {"dtype": "string", "id": null, "_type": "Value"}, "translation1": {"dtype": "string", "id": null, "_type": "Value"}, "translation2": {"dtype": "string", "id": null, "_type": "Value"}, "answer": {"dtype": "int64", "id": null, "_type": "Value"}, "pronoun1": {"dtype": "string", "id": null, "_type": "Value"}, "pronoun2": {"dtype": "string", "id": null, "_type": "Value"}, "referent1_en": {"dtype": "string", "id": null, "_type": "Value"}, "referent2_en": {"dtype": "string", "id": null, "_type": "Value"}, "true_translation_referent_of_pronoun1_ru": {"dtype": "string", "id": null, "_type": "Value"}, "true_translation_referent_of_pronoun2_ru": {"dtype": "string", "id": null, "_type": "Value"}, "false_translation_referent_of_pronoun1_ru": {"dtype": "string", "id": null, "_type": "Value"}, "false_translation_referent_of_pronoun2_ru": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "wino_x", "config_name": "mt_en_ru", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"test": {"name": "test", "num_bytes": 1314290, "num_examples": 2238, "dataset_name": "wino_x"}}, "download_checksums": {"https://huggingface.co/datasets/demelin/wino_x/resolve/main/data/mt/en_ru_test.jsonl": {"num_bytes": 1965547, "checksum": "94f232e299b1f27cb97cdae15e25f2e6157b974abfda0754fca91a6d5bea0d7d"}}, "download_size": 1965547, "post_processing_size": null, "dataset_size": 1314290, "size_in_bytes": 3279837}, "lm_en_de": {"description": "Wino-X is a parallel dataset of German, French, and Russian Winograd schemas, aligned with their English \ncounterparts, used to examine whether neural machine translation models can perform coreference resolution that \nrequires commonsense knowledge and whether multilingual language models are capable of commonsense reasoning across \nmultiple languages.\n", "citation": "@inproceedings{Emelin2021WinoXMW,\n title={Wino-X: Multilingual Winograd Schemas for Commonsense Reasoning and Coreference Resolution},\n author={Denis Emelin and Rico Sennrich},\n booktitle={EMNLP},\n year={2021}\n}\n", "homepage": "https://github.com/demelin/Wino-X", "license": "MIT", "features": {"qID": {"dtype": "string", "id": null, "_type": "Value"}, "sentence": {"dtype": "string", "id": null, "_type": "Value"}, "context_en": {"dtype": "string", "id": null, "_type": "Value"}, "context_de": {"dtype": "string", "id": null, "_type": "Value"}, "answer": {"dtype": "int64", "id": null, "_type": "Value"}, "option1_en": {"dtype": "string", "id": null, "_type": "Value"}, "option2_en": {"dtype": "string", "id": null, "_type": "Value"}, "option1_de": {"dtype": "string", "id": null, "_type": "Value"}, "option2_de": {"dtype": "string", "id": null, "_type": "Value"}, "context_referent_of_option1_de": {"dtype": "string", "id": null, "_type": "Value"}, "context_referent_of_option2_de": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "wino_x", "config_name": "lm_en_de", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"test": {"name": "test", "num_bytes": 2432303, "num_examples": 5835, "dataset_name": "wino_x"}}, "download_checksums": {"https://huggingface.co/datasets/demelin/wino_x/resolve/main/data/lm/en_de_test.jsonl": {"num_bytes": 3465097, "checksum": "aa91c114832c351c7c4a836041120efc9b5cca2c1620ffcdc5f7f4fa1a7af613"}}, "download_size": 3465097, "post_processing_size": null, "dataset_size": 2432303, "size_in_bytes": 5897400}, "lm_en_fr": {"description": "Wino-X is a parallel dataset of German, French, and Russian Winograd schemas, aligned with their English \ncounterparts, used to examine whether neural machine translation models can perform coreference resolution that \nrequires commonsense knowledge and whether multilingual language models are capable of commonsense reasoning across \nmultiple languages.\n", "citation": "@inproceedings{Emelin2021WinoXMW,\n title={Wino-X: Multilingual Winograd Schemas for Commonsense Reasoning and Coreference Resolution},\n author={Denis Emelin and Rico Sennrich},\n booktitle={EMNLP},\n year={2021}\n}\n", "homepage": "https://github.com/demelin/Wino-X", "license": "MIT", "features": {"qID": {"dtype": "string", "id": null, "_type": "Value"}, "sentence": {"dtype": "string", "id": null, "_type": "Value"}, "context_en": {"dtype": "string", "id": null, "_type": "Value"}, "context_fr": {"dtype": "string", "id": null, "_type": "Value"}, "answer": {"dtype": "int64", "id": null, "_type": "Value"}, "option1_en": {"dtype": "string", "id": null, "_type": "Value"}, "option2_en": {"dtype": "string", "id": null, "_type": "Value"}, "option1_fr": {"dtype": "string", "id": null, "_type": "Value"}, "option2_fr": {"dtype": "string", "id": null, "_type": "Value"}, "context_referent_of_option1_fr": {"dtype": "string", "id": null, "_type": "Value"}, "context_referent_of_option2_fr": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "wino_x", "config_name": "lm_en_fr", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"test": {"name": "test", "num_bytes": 1184932, "num_examples": 2793, "dataset_name": "wino_x"}}, "download_checksums": {"https://huggingface.co/datasets/demelin/wino_x/resolve/main/data/lm/en_fr_test.jsonl": {"num_bytes": 1679293, "checksum": "5eb5baeba31db9c20aafb5379e80d14b0a32d69d4d3a9925c4a1ee7e77eb11d1"}}, "download_size": 1679293, "post_processing_size": null, "dataset_size": 1184932, "size_in_bytes": 2864225}, "lm_en_ru": {"description": "Wino-X is a parallel dataset of German, French, and Russian Winograd schemas, aligned with their English \ncounterparts, used to examine whether neural machine translation models can perform coreference resolution that \nrequires commonsense knowledge and whether multilingual language models are capable of commonsense reasoning across \nmultiple languages.\n", "citation": "@inproceedings{Emelin2021WinoXMW,\n title={Wino-X: Multilingual Winograd Schemas for Commonsense Reasoning and Coreference Resolution},\n author={Denis Emelin and Rico Sennrich},\n booktitle={EMNLP},\n year={2021}\n}\n", "homepage": "https://github.com/demelin/Wino-X", "license": "MIT", "features": {"qID": {"dtype": "string", "id": null, "_type": "Value"}, "sentence": {"dtype": "string", "id": null, "_type": "Value"}, "context_en": {"dtype": "string", "id": null, "_type": "Value"}, "context_ru": {"dtype": "string", "id": null, "_type": "Value"}, "answer": {"dtype": "int64", "id": null, "_type": "Value"}, "option1_en": {"dtype": "string", "id": null, "_type": "Value"}, "option2_en": {"dtype": "string", "id": null, "_type": "Value"}, "option1_ru": {"dtype": "string", "id": null, "_type": "Value"}, "option2_ru": {"dtype": "string", "id": null, "_type": "Value"}, "context_referent_of_option1_ru": {"dtype": "string", "id": null, "_type": "Value"}, "context_referent_of_option2_ru": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "wino_x", "config_name": "lm_en_ru", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"test": {"name": "test", "num_bytes": 753838, "num_examples": 1487, "dataset_name": "wino_x"}}, "download_checksums": {"https://huggingface.co/datasets/demelin/wino_x/resolve/main/data/lm/en_ru_test.jsonl": {"num_bytes": 1017037, "checksum": "1bd110d3bc11e5f3034f7040fca56a818607182a52df42dcfa16d606528c2a25"}}, "download_size": 1017037, "post_processing_size": null, "dataset_size": 753838, "size_in_bytes": 1770875}}
|
|
|
|
data/lm/en_fr_test.jsonl → lm_en_de/wino_x-test.parquet
RENAMED
@@ -1,3 +1,3 @@
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
-
oid sha256:
|
3 |
-
size
|
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:90947395ce7677409b2a5753653a4c15eb50df47a65b61170c28f16ded41c0fc
|
3 |
+
size 1002935
|
data/lm/en_ru_test.jsonl → lm_en_fr/wino_x-test.parquet
RENAMED
@@ -1,3 +1,3 @@
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
-
oid sha256:
|
3 |
-
size
|
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:4fbbe4f0ec7cd34527844509af3c2ee5c897657226588825316feafdcaaaadcb
|
3 |
+
size 484734
|
data/mt/en_de_test.jsonl → lm_en_ru/wino_x-test.parquet
RENAMED
@@ -1,3 +1,3 @@
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
-
oid sha256:
|
3 |
-
size
|
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:9302e0c22b6a2e827f09715d287174c6a8d590e4f042e9e72625ac210c719e3e
|
3 |
+
size 295417
|
data/lm/en_de_test.jsonl → mt_en_de/wino_x-test.parquet
RENAMED
@@ -1,3 +1,3 @@
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
-
oid sha256:
|
3 |
-
size
|
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:19bc3c49a8e1ec040b7c51a184b57e77fe1eb5da3bee63458b23ca6bee15b9ed
|
3 |
+
size 680039
|
mt_en_fr/wino_x-test.parquet
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:fe59e11d1a1fd8d43645e3b9599dcf2ae96eb596dbaf4559b58452f6d85928c4
|
3 |
+
size 533392
|
mt_en_ru/wino_x-test.parquet
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:eba4f7f29eacdc90e0d071c2980962a7d6b88329c3f2ad7856089ddb95a98b77
|
3 |
+
size 493241
|
wino_x.py
DELETED
@@ -1,168 +0,0 @@
|
|
1 |
-
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
|
2 |
-
#
|
3 |
-
# Licensed under the Apache License, Version 2.0 (the "License");
|
4 |
-
# you may not use this file except in compliance with the License.
|
5 |
-
# You may obtain a copy of the License at
|
6 |
-
#
|
7 |
-
# http://www.apache.org/licenses/LICENSE-2.0
|
8 |
-
#
|
9 |
-
# Unless required by applicable law or agreed to in writing, software
|
10 |
-
# distributed under the License is distributed on an "AS IS" BASIS,
|
11 |
-
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
12 |
-
# See the License for the specific language governing permissions and
|
13 |
-
# limitations under the License.
|
14 |
-
""" Wino-X is a parallel dataset of German, French, and Russian Winograd schemas, aligned with their English
|
15 |
-
counterparts, used to examine whether neural machine translation models can perform coreference resolution that
|
16 |
-
requires commonsense knowledge, and whether multilingual language models are capable of commonsense reasoning across
|
17 |
-
multiple languages. """
|
18 |
-
|
19 |
-
import csv
|
20 |
-
import json
|
21 |
-
import os
|
22 |
-
|
23 |
-
import datasets
|
24 |
-
|
25 |
-
_CITATION = """\
|
26 |
-
@inproceedings{Emelin2021WinoXMW,
|
27 |
-
title={Wino-X: Multilingual Winograd Schemas for Commonsense Reasoning and Coreference Resolution},
|
28 |
-
author={Denis Emelin and Rico Sennrich},
|
29 |
-
booktitle={EMNLP},
|
30 |
-
year={2021}
|
31 |
-
}
|
32 |
-
"""
|
33 |
-
|
34 |
-
# You can copy an official description
|
35 |
-
_DESCRIPTION = """\
|
36 |
-
Wino-X is a parallel dataset of German, French, and Russian Winograd schemas, aligned with their English
|
37 |
-
counterparts, used to examine whether neural machine translation models can perform coreference resolution that
|
38 |
-
requires commonsense knowledge and whether multilingual language models are capable of commonsense reasoning across
|
39 |
-
multiple languages.
|
40 |
-
"""
|
41 |
-
|
42 |
-
_HOMEPAGE = "https://github.com/demelin/Wino-X"
|
43 |
-
|
44 |
-
_LICENSE = "MIT"
|
45 |
-
|
46 |
-
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
|
47 |
-
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
|
48 |
-
_URLS = {
|
49 |
-
"mt_en_de": "https://huggingface.co/datasets/demelin/wino_x/resolve/main/data/mt/en_de_test.jsonl",
|
50 |
-
"mt_en_fr": "https://huggingface.co/datasets/demelin/wino_x/resolve/main/data/mt/en_fr_test.jsonl",
|
51 |
-
"mt_en_ru": "https://huggingface.co/datasets/demelin/wino_x/resolve/main/data/mt/en_ru_test.jsonl",
|
52 |
-
"lm_en_de": "https://huggingface.co/datasets/demelin/wino_x/resolve/main/data/lm/en_de_test.jsonl",
|
53 |
-
"lm_en_fr": "https://huggingface.co/datasets/demelin/wino_x/resolve/main/data/lm/en_fr_test.jsonl",
|
54 |
-
"lm_en_ru": "https://huggingface.co/datasets/demelin/wino_x/resolve/main/data/lm/en_ru_test.jsonl"
|
55 |
-
}
|
56 |
-
|
57 |
-
|
58 |
-
class WinoX(datasets.GeneratorBasedBuilder):
|
59 |
-
""" Wino-X is a dataset of German, French, and Russian Winograd schemas, aligned with their English counterparts """
|
60 |
-
|
61 |
-
VERSION = datasets.Version("1.1.0")
|
62 |
-
BUILDER_CONFIGS = [
|
63 |
-
datasets.BuilderConfig(name="mt_en_de", version=VERSION,
|
64 |
-
description="This is the EN-DE part of the Wino-X translation data."),
|
65 |
-
datasets.BuilderConfig(name="mt_en_fr", version=VERSION,
|
66 |
-
description="This is the EN-FR part of the Wino-X translation data."),
|
67 |
-
datasets.BuilderConfig(name="mt_en_ru", version=VERSION,
|
68 |
-
description="This is the EN-RU part of the Wino-X translation data."),
|
69 |
-
datasets.BuilderConfig(name="lm_en_de", version=VERSION,
|
70 |
-
description="This is the EN-DE part of the Wino-X language modeling data."),
|
71 |
-
datasets.BuilderConfig(name="lm_en_fr", version=VERSION,
|
72 |
-
description="This is the EN-FR part of the Wino-X language modeling data."),
|
73 |
-
datasets.BuilderConfig(name="lm_en_ru", version=VERSION,
|
74 |
-
description="This is the EN-RU part of the Wino-X language modeling data."),
|
75 |
-
]
|
76 |
-
|
77 |
-
def _info(self):
|
78 |
-
|
79 |
-
# MT example:
|
80 |
-
# {"qID": "3UDTAB6HH8D37OQL3O6F3GXEEOF09Z-1",
|
81 |
-
# "sentence": "The woman looked for a different vase for the bouquet because it was too small.",
|
82 |
-
# "translation1": "Die Frau suchte nach einer anderen Vase für den Blumenstrauß, weil sie zu klein war.",
|
83 |
-
# "translation2": "Die Frau suchte nach einer anderen Vase für den Blumenstrauß, weil er zu klein war.",
|
84 |
-
# "answer": 1,
|
85 |
-
# "pronoun1": "sie",
|
86 |
-
# "pronoun2": "er",
|
87 |
-
# "referent1_en": "vase",
|
88 |
-
# "referent2_en": "bouquet",
|
89 |
-
# "true_translation_referent_of_pronoun1_de": "Vase",
|
90 |
-
# "true_translation_referent_of_pronoun2_de": "Blumenstrauß",
|
91 |
-
# "false_translation_referent_of_pronoun1_de": "Vase",
|
92 |
-
# "false_translation_referent_of_pronoun2_de": "Blumenstrauß"}
|
93 |
-
|
94 |
-
tgt_lang = self.config.name.split('_')[-1]
|
95 |
-
if self.config.name.startswith('mt_'):
|
96 |
-
features = datasets.Features(
|
97 |
-
{
|
98 |
-
"qID": datasets.Value("string"),
|
99 |
-
"sentence": datasets.Value("string"),
|
100 |
-
"translation1": datasets.Value("string"),
|
101 |
-
"translation2": datasets.Value("string"),
|
102 |
-
"answer": datasets.Value("int64"),
|
103 |
-
"pronoun1": datasets.Value("string"),
|
104 |
-
"pronoun2": datasets.Value("string"),
|
105 |
-
"referent1_en": datasets.Value("string"),
|
106 |
-
"referent2_en": datasets.Value("string"),
|
107 |
-
"true_translation_referent_of_pronoun1_{}".format(tgt_lang): datasets.Value("string"),
|
108 |
-
"true_translation_referent_of_pronoun2_{}".format(tgt_lang): datasets.Value("string"),
|
109 |
-
"false_translation_referent_of_pronoun1_{}".format(tgt_lang): datasets.Value("string"),
|
110 |
-
"false_translation_referent_of_pronoun2_{}".format(tgt_lang): datasets.Value("string")
|
111 |
-
}
|
112 |
-
)
|
113 |
-
|
114 |
-
# LM example:
|
115 |
-
# {"qID": "3UDTAB6HH8D37OQL3O6F3GXEEOF09Z-1",
|
116 |
-
# "sentence": "The woman looked for a different vase for the bouquet because it was too small.",
|
117 |
-
# "context_en": "The woman looked for a different vase for the bouquet because _ was too small.",
|
118 |
-
# "context_de": "Die Frau suchte nach einer anderen Vase für den Blumenstrauß, weil _ zu klein war.",
|
119 |
-
# "option1_en": "the vase",
|
120 |
-
# "option2_en": "the bouquet",
|
121 |
-
# "option1_de": "die Vase",
|
122 |
-
# "option2_de": "der Blumenstrauß",
|
123 |
-
# "answer": 1,
|
124 |
-
# "context_referent_of_option1_de": "Vase",
|
125 |
-
# "context_referent_of_option2_de": "Blumenstrauß"}
|
126 |
-
|
127 |
-
else:
|
128 |
-
features = datasets.Features(
|
129 |
-
{
|
130 |
-
"qID": datasets.Value("string"),
|
131 |
-
"sentence": datasets.Value("string"),
|
132 |
-
"context_en": datasets.Value("string"),
|
133 |
-
"context_{}".format(tgt_lang): datasets.Value("string"),
|
134 |
-
"answer": datasets.Value("int64"),
|
135 |
-
"option1_en": datasets.Value("string"),
|
136 |
-
"option2_en": datasets.Value("string"),
|
137 |
-
"option1_{}".format(tgt_lang): datasets.Value("string"),
|
138 |
-
"option2_{}".format(tgt_lang): datasets.Value("string"),
|
139 |
-
"context_referent_of_option1_{}".format(tgt_lang): datasets.Value("string"),
|
140 |
-
"context_referent_of_option2_{}".format(tgt_lang): datasets.Value("string")
|
141 |
-
}
|
142 |
-
)
|
143 |
-
|
144 |
-
return datasets.DatasetInfo(
|
145 |
-
# This is the description that will appear on the datasets page.
|
146 |
-
description=_DESCRIPTION,
|
147 |
-
# This defines the different columns of the dataset and their types
|
148 |
-
features=features,
|
149 |
-
# Homepage of the dataset for documentation
|
150 |
-
homepage=_HOMEPAGE,
|
151 |
-
# License for the dataset if available
|
152 |
-
license=_LICENSE,
|
153 |
-
# Citation for the dataset
|
154 |
-
citation=_CITATION,
|
155 |
-
)
|
156 |
-
|
157 |
-
def _split_generators(self, dl_manager):
|
158 |
-
downloaded_files = dl_manager.download_and_extract(_URLS[self.config.name])
|
159 |
-
return [datasets.SplitGenerator(name=datasets.Split.TEST,
|
160 |
-
gen_kwargs={'filepath': downloaded_files, 'split': 'test'})]
|
161 |
-
|
162 |
-
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
|
163 |
-
def _generate_examples(self, filepath, split):
|
164 |
-
# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
|
165 |
-
with open(filepath, encoding="utf-8") as f:
|
166 |
-
for key, row in enumerate(f):
|
167 |
-
data = json.loads(row)
|
168 |
-
yield key, data
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|