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albertvillanova HF staff commited on
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a47ee7a
1 Parent(s): 320355e

Convert dataset to Parquet (#5)

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- Convert dataset to Parquet (45d4cbe1ea0f96b660f3bd2fcf6095b8fdc40f27)
- Add v2.1 data files (86db073e1f9e9d2add57e03fee802e005f244c2e)
- Delete loading script (92f7ed789a9f8bf6bd35f842dc6975beb523211f)
- Delete legacy dataset_infos.json (2ba5b9fd40b5084bde19e40898e6e472a4b4b5fb)

README.md CHANGED
@@ -26,16 +26,16 @@ dataset_info:
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  sequence: string
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  splits:
28
  - name: validation
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- num_bytes: 42710107
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  num_examples: 10047
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  - name: train
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- num_bytes: 350884446
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  num_examples: 82326
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  - name: test
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- num_bytes: 41020711
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  num_examples: 9650
37
- download_size: 168698008
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- dataset_size: 434615264
39
  - config_name: v2.1
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  features:
41
  - name: answers
@@ -58,16 +58,33 @@ dataset_info:
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  sequence: string
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  splits:
60
  - name: validation
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- num_bytes: 414286005
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  num_examples: 101093
63
  - name: train
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- num_bytes: 3466972085
65
  num_examples: 808731
66
  - name: test
67
- num_bytes: 406197152
68
  num_examples: 101092
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- download_size: 1384271865
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- dataset_size: 4287455242
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
71
  ---
72
 
73
  # Dataset Card for "ms_marco"
 
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  sequence: string
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  splits:
28
  - name: validation
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+ num_bytes: 42665198
30
  num_examples: 10047
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  - name: train
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+ num_bytes: 350516260
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  num_examples: 82326
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  - name: test
35
+ num_bytes: 40977580
36
  num_examples: 9650
37
+ download_size: 217328153
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+ dataset_size: 434159038
39
  - config_name: v2.1
40
  features:
41
  - name: answers
 
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  sequence: string
59
  splits:
60
  - name: validation
61
+ num_bytes: 413765365
62
  num_examples: 101093
63
  - name: train
64
+ num_bytes: 3462807709
65
  num_examples: 808731
66
  - name: test
67
+ num_bytes: 405691932
68
  num_examples: 101092
69
+ download_size: 2105722550
70
+ dataset_size: 4282265006
71
+ configs:
72
+ - config_name: v1.1
73
+ data_files:
74
+ - split: validation
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+ path: v1.1/validation-*
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+ - split: train
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+ path: v1.1/train-*
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+ - split: test
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+ path: v1.1/test-*
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+ - config_name: v2.1
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+ data_files:
82
+ - split: validation
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+ path: v2.1/validation-*
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+ - split: train
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+ path: v2.1/train-*
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+ - split: test
87
+ path: v2.1/test-*
88
  ---
89
 
90
  # Dataset Card for "ms_marco"
dataset_infos.json DELETED
@@ -1 +0,0 @@
1
- {"v1.1": {"description": "\nStarting with a paper released at NIPS 2016, MS MARCO is a collection of datasets focused on deep learning in search.\n\nThe first dataset was a question answering dataset featuring 100,000 real Bing questions and a human generated answer. \nSince then we released a 1,000,000 question dataset, a natural langauge generation dataset, a passage ranking dataset, \nkeyphrase extraction dataset, crawling dataset, and a conversational search.\n\nThere have been 277 submissions. 20 KeyPhrase Extraction submissions, 87 passage ranking submissions, 0 document ranking \nsubmissions, 73 QnA V2 submissions, 82 NLGEN submisions, and 15 QnA V1 submissions\n\nThis data comes in three tasks/forms: Original QnA dataset(v1.1), Question Answering(v2.1), Natural Language Generation(v2.1). \n\nThe original question answering datset featured 100,000 examples and was released in 2016. Leaderboard is now closed but data is availible below.\n\nThe current competitive tasks are Question Answering and Natural Language Generation. Question Answering features over 1,000,000 queries and \nis much like the original QnA dataset but bigger and with higher quality. The Natural Language Generation dataset features 180,000 examples and \nbuilds upon the QnA dataset to deliver answers that could be spoken by a smart speaker.\n\n\nversion v1.1", "citation": "\n@article{DBLP:journals/corr/NguyenRSGTMD16,\n author = {Tri Nguyen and\n Mir Rosenberg and\n Xia Song and\n Jianfeng Gao and\n Saurabh Tiwary and\n Rangan Majumder and\n Li Deng},\n title = {{MS} {MARCO:} {A} Human Generated MAchine Reading COmprehension Dataset},\n journal = {CoRR},\n volume = {abs/1611.09268},\n year = {2016},\n url = {http://arxiv.org/abs/1611.09268},\n archivePrefix = {arXiv},\n eprint = {1611.09268},\n timestamp = {Mon, 13 Aug 2018 16:49:03 +0200},\n biburl = {https://dblp.org/rec/journals/corr/NguyenRSGTMD16.bib},\n bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n}\n", "homepage": "https://microsoft.github.io/msmarco/", "license": "", "features": {"answers": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "passages": {"feature": {"is_selected": {"dtype": "int32", "id": null, "_type": "Value"}, "passage_text": {"dtype": "string", "id": null, "_type": "Value"}, "url": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}, "query": {"dtype": "string", "id": null, "_type": "Value"}, "query_id": {"dtype": "int32", "id": null, "_type": "Value"}, "query_type": {"dtype": "string", "id": null, "_type": "Value"}, "wellFormedAnswers": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "supervised_keys": null, "builder_name": "ms_marco", "config_name": "v1.1", "version": {"version_str": "1.1.0", "description": "", "datasets_version_to_prepare": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"validation": {"name": "validation", "num_bytes": 42710107, "num_examples": 10047, "dataset_name": "ms_marco"}, "train": {"name": "train", "num_bytes": 350884446, "num_examples": 82326, "dataset_name": "ms_marco"}, "test": {"name": "test", "num_bytes": 41020711, "num_examples": 9650, "dataset_name": "ms_marco"}}, "download_checksums": {"https://msmarco.blob.core.windows.net/msmsarcov1/train_v1.1.json.gz": {"num_bytes": 110704491, "checksum": "2aaa60df3a758137f0bb7c01fe334858477eb46fa8665ea01588e553cda6aa9f"}, "https://msmarco.blob.core.windows.net/msmsarcov1/dev_v1.1.json.gz": {"num_bytes": 13493661, "checksum": "c70fcb1de78e635cf501264891a1a56d52e7f63e69623da7dd41d89a785d67ca"}, "https://msmarco.blob.core.windows.net/msmsarcov1/test_hidden_v1.1.json": {"num_bytes": 44499856, "checksum": "083aa4f4d86ba0cedb830ca9972eff69f73cbc32b1da26b8617205f0dedea757"}}, "download_size": 168698008, "dataset_size": 434615264, "size_in_bytes": 603313272}, "v2.1": {"description": "\nStarting with a paper released at NIPS 2016, MS MARCO is a collection of datasets focused on deep learning in search.\n\nThe first dataset was a question answering dataset featuring 100,000 real Bing questions and a human generated answer. \nSince then we released a 1,000,000 question dataset, a natural langauge generation dataset, a passage ranking dataset, \nkeyphrase extraction dataset, crawling dataset, and a conversational search.\n\nThere have been 277 submissions. 20 KeyPhrase Extraction submissions, 87 passage ranking submissions, 0 document ranking \nsubmissions, 73 QnA V2 submissions, 82 NLGEN submisions, and 15 QnA V1 submissions\n\nThis data comes in three tasks/forms: Original QnA dataset(v1.1), Question Answering(v2.1), Natural Language Generation(v2.1). \n\nThe original question answering datset featured 100,000 examples and was released in 2016. Leaderboard is now closed but data is availible below.\n\nThe current competitive tasks are Question Answering and Natural Language Generation. Question Answering features over 1,000,000 queries and \nis much like the original QnA dataset but bigger and with higher quality. The Natural Language Generation dataset features 180,000 examples and \nbuilds upon the QnA dataset to deliver answers that could be spoken by a smart speaker.\n\n\nversion v2.1", "citation": "\n@article{DBLP:journals/corr/NguyenRSGTMD16,\n author = {Tri Nguyen and\n Mir Rosenberg and\n Xia Song and\n Jianfeng Gao and\n Saurabh Tiwary and\n Rangan Majumder and\n Li Deng},\n title = {{MS} {MARCO:} {A} Human Generated MAchine Reading COmprehension Dataset},\n journal = {CoRR},\n volume = {abs/1611.09268},\n year = {2016},\n url = {http://arxiv.org/abs/1611.09268},\n archivePrefix = {arXiv},\n eprint = {1611.09268},\n timestamp = {Mon, 13 Aug 2018 16:49:03 +0200},\n biburl = {https://dblp.org/rec/journals/corr/NguyenRSGTMD16.bib},\n bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n}\n", "homepage": "https://microsoft.github.io/msmarco/", "license": "", "features": {"answers": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "passages": {"feature": {"is_selected": {"dtype": "int32", "id": null, "_type": "Value"}, "passage_text": {"dtype": "string", "id": null, "_type": "Value"}, "url": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}, "query": {"dtype": "string", "id": null, "_type": "Value"}, "query_id": {"dtype": "int32", "id": null, "_type": "Value"}, "query_type": {"dtype": "string", "id": null, "_type": "Value"}, "wellFormedAnswers": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "supervised_keys": null, "builder_name": "ms_marco", "config_name": "v2.1", "version": {"version_str": "2.1.0", "description": "", "datasets_version_to_prepare": null, "major": 2, "minor": 1, "patch": 0}, "splits": {"validation": {"name": "validation", "num_bytes": 414286005, "num_examples": 101093, "dataset_name": "ms_marco"}, "train": {"name": "train", "num_bytes": 3466972085, "num_examples": 808731, "dataset_name": "ms_marco"}, "test": {"name": "test", "num_bytes": 406197152, "num_examples": 101092, "dataset_name": "ms_marco"}}, "download_checksums": {"https://msmarco.blob.core.windows.net/msmarco/train_v2.1.json.gz": {"num_bytes": 1112116929, "checksum": "e91745411ca81e441a3bb75deb71ce000dc2fc31334085b7d499982f14218fe2"}, "https://msmarco.blob.core.windows.net/msmarco/dev_v2.1.json.gz": {"num_bytes": 138303699, "checksum": "5b3c9c20d1808ee199a930941b0d96f79e397e9234f77a1496890b138df7cb3c"}, "https://msmarco.blob.core.windows.net/msmarco/eval_v2.1_public.json.gz": {"num_bytes": 133851237, "checksum": "05ac0e448450d507e7ff8e37f48a41cc2d015f5bd2c7974d2445f00a53625db6"}}, "download_size": 1384271865, "dataset_size": 4287455242, "size_in_bytes": 5671727107}}
 
 
ms_marco.py DELETED
@@ -1,204 +0,0 @@
1
- # coding=utf-8
2
- # Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
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
-
16
- # Lint as: python3
17
- """MS MARCO dataset."""
18
-
19
-
20
- import json
21
-
22
- import datasets
23
-
24
-
25
- _CITATION = """
26
- @article{DBLP:journals/corr/NguyenRSGTMD16,
27
- author = {Tri Nguyen and
28
- Mir Rosenberg and
29
- Xia Song and
30
- Jianfeng Gao and
31
- Saurabh Tiwary and
32
- Rangan Majumder and
33
- Li Deng},
34
- title = {{MS} {MARCO:} {A} Human Generated MAchine Reading COmprehension Dataset},
35
- journal = {CoRR},
36
- volume = {abs/1611.09268},
37
- year = {2016},
38
- url = {http://arxiv.org/abs/1611.09268},
39
- archivePrefix = {arXiv},
40
- eprint = {1611.09268},
41
- timestamp = {Mon, 13 Aug 2018 16:49:03 +0200},
42
- biburl = {https://dblp.org/rec/journals/corr/NguyenRSGTMD16.bib},
43
- bibsource = {dblp computer science bibliography, https://dblp.org}
44
- }
45
- }
46
- """
47
-
48
- _DESCRIPTION = """
49
- Starting with a paper released at NIPS 2016, MS MARCO is a collection of datasets focused on deep learning in search.
50
-
51
- The first dataset was a question answering dataset featuring 100,000 real Bing questions and a human generated answer.
52
- Since then we released a 1,000,000 question dataset, a natural langauge generation dataset, a passage ranking dataset,
53
- keyphrase extraction dataset, crawling dataset, and a conversational search.
54
-
55
- There have been 277 submissions. 20 KeyPhrase Extraction submissions, 87 passage ranking submissions, 0 document ranking
56
- submissions, 73 QnA V2 submissions, 82 NLGEN submisions, and 15 QnA V1 submissions
57
-
58
- This data comes in three tasks/forms: Original QnA dataset(v1.1), Question Answering(v2.1), Natural Language Generation(v2.1).
59
-
60
- The original question answering datset featured 100,000 examples and was released in 2016. Leaderboard is now closed but data is availible below.
61
-
62
- The current competitive tasks are Question Answering and Natural Language Generation. Question Answering features over 1,000,000 queries and
63
- is much like the original QnA dataset but bigger and with higher quality. The Natural Language Generation dataset features 180,000 examples and
64
- builds upon the QnA dataset to deliver answers that could be spoken by a smart speaker.
65
-
66
- """
67
- _V2_URLS = {
68
- "train": "https://msmarco.blob.core.windows.net/msmarco/train_v2.1.json.gz",
69
- "dev": "https://msmarco.blob.core.windows.net/msmarco/dev_v2.1.json.gz",
70
- "test": "https://msmarco.blob.core.windows.net/msmarco/eval_v2.1_public.json.gz",
71
- }
72
-
73
- _V1_URLS = {
74
- "train": "https://msmarco.blob.core.windows.net/msmsarcov1/train_v1.1.json.gz",
75
- "dev": "https://msmarco.blob.core.windows.net/msmsarcov1/dev_v1.1.json.gz",
76
- "test": "https://msmarco.blob.core.windows.net/msmsarcov1/test_hidden_v1.1.json",
77
- }
78
-
79
-
80
- class MsMarcoConfig(datasets.BuilderConfig):
81
- """BuilderConfig for MS MARCO."""
82
-
83
- def __init__(self, **kwargs):
84
- """BuilderConfig for MS MARCO
85
-
86
- Args:
87
- **kwargs: keyword arguments forwarded to super.
88
- """
89
- super(MsMarcoConfig, self).__init__(**kwargs)
90
-
91
-
92
- class MsMarco(datasets.GeneratorBasedBuilder):
93
-
94
- BUILDER_CONFIGS = [
95
- MsMarcoConfig(
96
- name="v1.1",
97
- description="""version v1.1""",
98
- version=datasets.Version("1.1.0", ""),
99
- ),
100
- MsMarcoConfig(
101
- name="v2.1",
102
- description="""version v2.1""",
103
- version=datasets.Version("2.1.0", ""),
104
- ),
105
- ]
106
-
107
- def _info(self):
108
- return datasets.DatasetInfo(
109
- description=_DESCRIPTION + "\n" + self.config.description,
110
- features=datasets.Features(
111
- {
112
- "answers": datasets.features.Sequence(datasets.Value("string")),
113
- "passages": datasets.features.Sequence(
114
- {
115
- "is_selected": datasets.Value("int32"),
116
- "passage_text": datasets.Value("string"),
117
- "url": datasets.Value("string"),
118
- }
119
- ),
120
- "query": datasets.Value("string"),
121
- "query_id": datasets.Value("int32"),
122
- "query_type": datasets.Value("string"),
123
- "wellFormedAnswers": datasets.features.Sequence(datasets.Value("string")),
124
- }
125
- ),
126
- homepage="https://microsoft.github.io/msmarco/",
127
- citation=_CITATION,
128
- )
129
-
130
- def _split_generators(self, dl_manager):
131
- """Returns SplitGenerators."""
132
- if self.config.name == "v2.1":
133
- dl_path = dl_manager.download_and_extract(_V2_URLS)
134
- else:
135
- dl_path = dl_manager.download_and_extract(_V1_URLS)
136
- return [
137
- datasets.SplitGenerator(
138
- name=datasets.Split.VALIDATION,
139
- gen_kwargs={"filepath": dl_path["dev"]},
140
- ),
141
- datasets.SplitGenerator(
142
- name=datasets.Split.TRAIN,
143
- gen_kwargs={"filepath": dl_path["train"]},
144
- ),
145
- datasets.SplitGenerator(
146
- name=datasets.Split.TEST,
147
- gen_kwargs={"filepath": dl_path["test"]},
148
- ),
149
- ]
150
-
151
- def _generate_examples(self, filepath):
152
- """Yields examples."""
153
- with open(filepath, encoding="utf-8") as f:
154
- if self.config.name == "v2.1":
155
- data = json.load(f)
156
- questions = data["query"]
157
- answers = data.get("answers", {})
158
- passages = data["passages"]
159
- query_ids = data["query_id"]
160
- query_types = data["query_type"]
161
- wellFormedAnswers = data.get("wellFormedAnswers", {})
162
- for key in questions:
163
-
164
- is_selected = [passage.get("is_selected", -1) for passage in passages[key]]
165
- passage_text = [passage["passage_text"] for passage in passages[key]]
166
- urls = [passage["url"] for passage in passages[key]]
167
- question = questions[key]
168
- answer = answers.get(key, [])
169
- query_id = query_ids[key]
170
- query_type = query_types[key]
171
- wellFormedAnswer = wellFormedAnswers.get(key, [])
172
- if wellFormedAnswer == "[]":
173
- wellFormedAnswer = []
174
- yield query_id, {
175
- "answers": answer,
176
- "passages": {"is_selected": is_selected, "passage_text": passage_text, "url": urls},
177
- "query": question,
178
- "query_id": query_id,
179
- "query_type": query_type,
180
- "wellFormedAnswers": wellFormedAnswer,
181
- }
182
- if self.config.name == "v1.1":
183
- for row in f:
184
- data = json.loads(row)
185
- question = data["query"]
186
- answer = data.get("answers", [])
187
- passages = data["passages"]
188
- query_id = data["query_id"]
189
- query_type = data["query_type"]
190
- wellFormedAnswer = data.get("wellFormedAnswers", [])
191
-
192
- is_selected = [passage.get("is_selected", -1) for passage in passages]
193
- passage_text = [passage["passage_text"] for passage in passages]
194
- urls = [passage["url"] for passage in passages]
195
- if wellFormedAnswer == "[]":
196
- wellFormedAnswer = []
197
- yield query_id, {
198
- "answers": answer,
199
- "passages": {"is_selected": is_selected, "passage_text": passage_text, "url": urls},
200
- "query": question,
201
- "query_id": query_id,
202
- "query_type": query_type,
203
- "wellFormedAnswers": wellFormedAnswer,
204
- }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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