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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

Files changed (5) hide show
  1. .gitattributes +27 -0
  2. README.md +150 -0
  3. dane.py +283 -0
  4. dataset_infos.json +1 -0
  5. dummy/0.0.0/dummy_data.zip +3 -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:
3
+ - expert-generated
4
+ language_creators:
5
+ - found
6
+ languages:
7
+ - da
8
+ licenses:
9
+ - cc-by-sa-4-0
10
+ multilinguality:
11
+ - monolingual
12
+ size_categories:
13
+ - 1K<n<10K
14
+ source_datasets: []
15
+ task_categories:
16
+ - structure-prediction
17
+ task_ids:
18
+ - named-entity-recognition
19
+ ---
20
+
21
+ # Dataset Card for [Dataset Name]
22
+
23
+ ## Table of Contents
24
+ - [Dataset Description](#dataset-description)
25
+ - [Dataset Summary](#dataset-summary)
26
+ - [Supported Tasks](#supported-tasks-and-leaderboards)
27
+ - [Languages](#languages)
28
+ - [Dataset Structure](#dataset-structure)
29
+ - [Data Instances](#data-instances)
30
+ - [Data Fields](#data-instances)
31
+ - [Data Splits](#data-instances)
32
+ - [Dataset Creation](#dataset-creation)
33
+ - [Curation Rationale](#curation-rationale)
34
+ - [Source Data](#source-data)
35
+ - [Annotations](#annotations)
36
+ - [Personal and Sensitive Information](#personal-and-sensitive-information)
37
+ - [Considerations for Using the Data](#considerations-for-using-the-data)
38
+ - [Social Impact of Dataset](#social-impact-of-dataset)
39
+ - [Discussion of Biases](#discussion-of-biases)
40
+ - [Other Known Limitations](#other-known-limitations)
41
+ - [Additional Information](#additional-information)
42
+ - [Dataset Curators](#dataset-curators)
43
+ - [Licensing Information](#licensing-information)
44
+ - [Citation Information](#citation-information)
45
+
46
+ ## Dataset Description
47
+
48
+ - **Homepage:** [Github](https://github.com/alexandrainst/danlp/blob/master/docs/docs/datasets.md#dane)
49
+ - **Repository:** [Github](https://github.com/alexandrainst/danlp)
50
+ - **Paper:** [Aclweb](https://www.aclweb.org/anthology/2020.lrec-1.565)
51
+ - **Leaderboard:**
52
+ - **Point of Contact:**
53
+
54
+ ### Dataset Summary
55
+
56
+ [More Information Needed]
57
+
58
+ ### Supported Tasks and Leaderboards
59
+
60
+ [More Information Needed]
61
+
62
+ ### Languages
63
+
64
+ [More Information Needed]
65
+
66
+ ## Dataset Structure
67
+
68
+ ### Data Instances
69
+
70
+ [More Information Needed]
71
+
72
+ ### Data Fields
73
+
74
+ Data Fields:
75
+ - q_id: a string question identifier for each example, corresponding to its ID in the Pushshift.io Reddit submission dumps.
76
+ - subreddit: One of explainlikeimfive, askscience, or AskHistorians, indicating which subreddit the question came from
77
+ - title: title of the question, with URLs extracted and replaced by URL_n tokens
78
+ - title_urls: list of the extracted URLs, the nth element of the list was replaced by URL_n
79
+ - sent_id: a string identifier for each example
80
+ - text: a string, the original sentence (not tokenized)
81
+ - tok_ids: a list of ids (int), one for each token
82
+ - tokens: a list of strings, the tokens
83
+ - lemmas: a list of strings, the lemmas of the tokens
84
+ - pos_tags: a list of strings, the part-of-speech tags of the tokens
85
+ - morph_tags: a list of strings, the morphological tags of the tokens
86
+ - dep_ids: a list of ids (int), the id of the head of the incoming dependency for each token
87
+ - dep_labels: a list of strings, the dependency labels
88
+ - ner_tags: a list of strings, the named entity tags (BIO format)
89
+
90
+ ### Data Splits
91
+
92
+ [More Information Needed]
93
+
94
+ ## Dataset Creation
95
+
96
+ ### Curation Rationale
97
+
98
+ [More Information Needed]
99
+
100
+ ### Source Data
101
+
102
+ #### Initial Data Collection and Normalization
103
+
104
+ [More Information Needed]
105
+
106
+ #### Who are the source language producers?
107
+
108
+ [More Information Needed]
109
+
110
+ ### Annotations
111
+
112
+ #### Annotation process
113
+
114
+ [More Information Needed]
115
+
116
+ #### Who are the annotators?
117
+
118
+ [More Information Needed]
119
+
120
+ ### Personal and Sensitive Information
121
+
122
+ [More Information Needed]
123
+
124
+ ## Considerations for Using the Data
125
+
126
+ ### Social Impact of Dataset
127
+
128
+ [More Information Needed]
129
+
130
+ ### Discussion of Biases
131
+
132
+ [More Information Needed]
133
+
134
+ ### Other Known Limitations
135
+
136
+ [More Information Needed]
137
+
138
+ ## Additional Information
139
+
140
+ ### Dataset Curators
141
+
142
+ [More Information Needed]
143
+
144
+ ### Licensing Information
145
+
146
+ [More Information Needed]
147
+
148
+ ### Citation Information
149
+
150
+ [More Information Needed]
dane.py ADDED
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1
+ # coding=utf-8
2
+ # Copyright 2020 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
+ """DaNE: named entity annotation for the Danish Universal Dependencies
16
+ treebank using the CoNLL-2003 annotation scheme."""
17
+
18
+ from __future__ import absolute_import, division, print_function
19
+
20
+ import os
21
+
22
+ import datasets
23
+
24
+
25
+ _CITATION = """\
26
+ @inproceedings{hvingelby-etal-2020-dane,
27
+ title = "{D}a{NE}: A Named Entity Resource for {D}anish",
28
+ author = "Hvingelby, Rasmus and
29
+ Pauli, Amalie Brogaard and
30
+ Barrett, Maria and
31
+ Rosted, Christina and
32
+ Lidegaard, Lasse Malm and
33
+ Søgaard, Anders",
34
+ booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
35
+ month = may,
36
+ year = "2020",
37
+ address = "Marseille, France",
38
+ publisher = "European Language Resources Association",
39
+ url = "https://www.aclweb.org/anthology/2020.lrec-1.565",
40
+ pages = "4597--4604",
41
+ abstract = "We present a named entity annotation for the Danish Universal Dependencies treebank using the CoNLL-2003 annotation scheme: DaNE. It is the largest publicly available, Danish named entity gold annotation. We evaluate the quality of our annotations intrinsically by double annotating the entire treebank and extrinsically by comparing our annotations to a recently released named entity annotation of the validation and test sections of the Danish Universal Dependencies treebank. We benchmark the new resource by training and evaluating competitive architectures for supervised named entity recognition (NER), including FLAIR, monolingual (Danish) BERT and multilingual BERT. We explore cross-lingual transfer in multilingual BERT from five related languages in zero-shot and direct transfer setups, and we show that even with our modestly-sized training set, we improve Danish NER over a recent cross-lingual approach, as well as over zero-shot transfer from five related languages. Using multilingual BERT, we achieve higher performance by fine-tuning on both DaNE and a larger Bokm{\aa}l (Norwegian) training set compared to only using DaNE. However, the highest performance isachieved by using a Danish BERT fine-tuned on DaNE. Our dataset enables improvements and applicability for Danish NER beyond cross-lingual methods. We employ a thorough error analysis of the predictions of the best models for seen and unseen entities, as well as their robustness on un-capitalized text. The annotated dataset and all the trained models are made publicly available.",
42
+ language = "English",
43
+ ISBN = "979-10-95546-34-4",
44
+ }
45
+ """
46
+
47
+ _DESCRIPTION = """\
48
+ The DaNE dataset has been annotated with Named Entities for PER, ORG and LOC
49
+ by the Alexandra Institute.
50
+ It is a reannotation of the UD-DDT (Universal Dependency - Danish Dependency Treebank)
51
+ which has annotations for dependency parsing and part-of-speech (POS) tagging.
52
+ The Danish UD treebank (Johannsen et al., 2015, UD-DDT) is a conversion of
53
+ the Danish Dependency Treebank (Buch-Kromann et al. 2003) based on texts
54
+ from Parole (Britt, 1998).
55
+ """
56
+
57
+ _HOMEPAGE = "https://github.com/alexandrainst/danlp/blob/master/docs/docs/datasets.md#dane"
58
+
59
+ _LICENSE = "CC BY-SA 4.0"
60
+
61
+ # The HuggingFace dataset library don't host the datasets but only point to the original files
62
+ # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
63
+ _URL = "https://danlp.alexandra.dk/304bd159d5de/datasets/ddt.zip"
64
+
65
+
66
+ class Dane(datasets.GeneratorBasedBuilder):
67
+ """DaNE dataset"""
68
+
69
+ def _info(self):
70
+ features = datasets.Features(
71
+ {
72
+ "sent_id": datasets.Value("string"),
73
+ "text": datasets.Value("string"),
74
+ "tok_ids": datasets.Sequence(datasets.Value("int64")),
75
+ "tokens": datasets.Sequence(datasets.Value("string")),
76
+ "lemmas": datasets.Sequence(datasets.Value("string")),
77
+ "pos_tags": datasets.Sequence(
78
+ datasets.features.ClassLabel(
79
+ names=[
80
+ "NUM",
81
+ "CCONJ",
82
+ "PRON",
83
+ "VERB",
84
+ "INTJ",
85
+ "AUX",
86
+ "ADJ",
87
+ "PROPN",
88
+ "PART",
89
+ "ADV",
90
+ "PUNCT",
91
+ "ADP",
92
+ "NOUN",
93
+ "X",
94
+ "DET",
95
+ "SYM",
96
+ "SCONJ",
97
+ ]
98
+ )
99
+ ),
100
+ "morph_tags": datasets.Sequence(datasets.Value("string")),
101
+ "dep_ids": datasets.Sequence(datasets.Value("int64")),
102
+ "dep_labels": datasets.Sequence(
103
+ datasets.ClassLabel(
104
+ names=[
105
+ "parataxis",
106
+ "mark",
107
+ "nummod",
108
+ "discourse",
109
+ "compound:prt",
110
+ "reparandum",
111
+ "vocative",
112
+ "list",
113
+ "obj",
114
+ "dep",
115
+ "det",
116
+ "obl:loc",
117
+ "flat",
118
+ "iobj",
119
+ "cop",
120
+ "expl",
121
+ "obl",
122
+ "conj",
123
+ "nmod",
124
+ "root",
125
+ "acl:relcl",
126
+ "goeswith",
127
+ "appos",
128
+ "fixed",
129
+ "obl:tmod",
130
+ "xcomp",
131
+ "advmod",
132
+ "nmod:poss",
133
+ "aux",
134
+ "ccomp",
135
+ "amod",
136
+ "cc",
137
+ "advcl",
138
+ "nsubj",
139
+ "punct",
140
+ "case",
141
+ ]
142
+ )
143
+ ),
144
+ "ner_tags": datasets.Sequence(
145
+ datasets.features.ClassLabel(
146
+ names=[
147
+ "O",
148
+ "B-PER",
149
+ "I-PER",
150
+ "B-ORG",
151
+ "I-ORG",
152
+ "B-LOC",
153
+ "I-LOC",
154
+ "B-MISC",
155
+ "I-MISC",
156
+ ]
157
+ )
158
+ ),
159
+ }
160
+ )
161
+
162
+ return datasets.DatasetInfo(
163
+ # This is the description that will appear on the datasets page.
164
+ description=_DESCRIPTION,
165
+ # This defines the different columns of the dataset and their types
166
+ features=features, # Here we define them above because they are different between the two configurations
167
+ # If there's a common (input, target) tuple from the features,
168
+ # specify them here. They'll be used if as_supervised=True in
169
+ # builder.as_dataset.
170
+ supervised_keys=None,
171
+ # Homepage of the dataset for documentation
172
+ homepage=_HOMEPAGE,
173
+ # License for the dataset if available
174
+ license=_LICENSE,
175
+ # Citation for the dataset
176
+ citation=_CITATION,
177
+ )
178
+
179
+ def _split_generators(self, dl_manager):
180
+ """Returns SplitGenerators."""
181
+ # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
182
+ # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
183
+
184
+ # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
185
+ # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
186
+ # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
187
+ data_dir = dl_manager.download_and_extract(_URL)
188
+ return [
189
+ datasets.SplitGenerator(
190
+ name=datasets.Split.TRAIN,
191
+ # These kwargs will be passed to _generate_examples
192
+ gen_kwargs={
193
+ "filepath": os.path.join(data_dir, "ddt.train.conllu"),
194
+ "split": "train",
195
+ },
196
+ ),
197
+ datasets.SplitGenerator(
198
+ name=datasets.Split.TEST,
199
+ # These kwargs will be passed to _generate_examples
200
+ gen_kwargs={"filepath": os.path.join(data_dir, "ddt.test.conllu"), "split": "test"},
201
+ ),
202
+ datasets.SplitGenerator(
203
+ name=datasets.Split.VALIDATION,
204
+ # These kwargs will be passed to _generate_examples
205
+ gen_kwargs={
206
+ "filepath": os.path.join(data_dir, "ddt.dev.conllu"),
207
+ "split": "dev",
208
+ },
209
+ ),
210
+ ]
211
+
212
+ def _generate_examples(self, filepath, split):
213
+ """ Yields examples. """
214
+
215
+ with open(filepath, encoding="utf-8") as f:
216
+ guid = 0
217
+ sent_id = ""
218
+ text = ""
219
+ tok_ids = []
220
+ tokens = []
221
+ lemmas = []
222
+ pos_tags = []
223
+ morph_tags = []
224
+ dephead_ids = []
225
+ dep_labels = []
226
+ ner_tags = []
227
+ for line in f:
228
+ if line.startswith("#"):
229
+ var, val = line.split(" = ", maxsplit=1)
230
+ var = var.replace("# ", "")
231
+ if var == "sent_id":
232
+ sent_id = val
233
+ elif var == "text":
234
+ text = val
235
+ elif line == "" or line == "\n":
236
+ if tokens:
237
+ yield guid, {
238
+ "sent_id": sent_id,
239
+ "text": text,
240
+ "tok_ids": tok_ids,
241
+ "tokens": tokens,
242
+ "lemmas": lemmas,
243
+ "pos_tags": pos_tags,
244
+ "morph_tags": morph_tags,
245
+ "dep_ids": dephead_ids,
246
+ "dep_labels": dep_labels,
247
+ "ner_tags": ner_tags,
248
+ }
249
+ guid += 1
250
+ sent_id = ""
251
+ text = ""
252
+ tok_ids = []
253
+ tokens = []
254
+ lemmas = []
255
+ pos_tags = []
256
+ morph_tags = []
257
+ dephead_ids = []
258
+ dep_labels = []
259
+ ner_tags = []
260
+ else:
261
+ # conllu tokens tab space separated
262
+ splits = line.split("\t")
263
+ tok_ids.append(int(splits[0]))
264
+ tokens.append(splits[1])
265
+ lemmas.append(splits[2])
266
+ pos_tags.append(splits[3])
267
+ morph_tags.append(splits[5])
268
+ dephead_ids.append(splits[6])
269
+ dep_labels.append(splits[7])
270
+ ner_tags.append(splits[9].rstrip().replace("name=", "").split("|")[0])
271
+ # last example
272
+ yield guid, {
273
+ "sent_id": sent_id,
274
+ "text": text,
275
+ "tok_ids": tok_ids,
276
+ "tokens": tokens,
277
+ "lemmas": lemmas,
278
+ "pos_tags": pos_tags,
279
+ "morph_tags": morph_tags,
280
+ "dep_ids": dephead_ids,
281
+ "dep_labels": dep_labels,
282
+ "ner_tags": ner_tags,
283
+ }
dataset_infos.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"default": {"description": "The DaNE dataset has been annotated with Named Entities for PER, ORG and LOC\nby the Alexandra Institute.\nIt is a reannotation of the UD-DDT (Universal Dependency - Danish Dependency Treebank)\nwhich has annotations for dependency parsing and part-of-speech (POS) tagging.\nThe Danish UD treebank (Johannsen et al., 2015, UD-DDT) is a conversion of\nthe Danish Dependency Treebank (Buch-Kromann et al. 2003) based on texts\nfrom Parole (Britt, 1998).\n", "citation": "@inproceedings{hvingelby-etal-2020-dane,\n title = \"{D}a{NE}: A Named Entity Resource for {D}anish\",\n author = \"Hvingelby, Rasmus and\n Pauli, Amalie Brogaard and\n Barrett, Maria and\n Rosted, Christina and\n Lidegaard, Lasse Malm and\n S\u00f8gaard, Anders\",\n booktitle = \"Proceedings of the 12th Language Resources and Evaluation Conference\",\n month = may,\n year = \"2020\",\n address = \"Marseille, France\",\n publisher = \"European Language Resources Association\",\n url = \"https://www.aclweb.org/anthology/2020.lrec-1.565\",\n pages = \"4597--4604\",\n abstract = \"We present a named entity annotation for the Danish Universal Dependencies treebank using the CoNLL-2003 annotation scheme: DaNE. It is the largest publicly available, Danish named entity gold annotation. We evaluate the quality of our annotations intrinsically by double annotating the entire treebank and extrinsically by comparing our annotations to a recently released named entity annotation of the validation and test sections of the Danish Universal Dependencies treebank. We benchmark the new resource by training and evaluating competitive architectures for supervised named entity recognition (NER), including FLAIR, monolingual (Danish) BERT and multilingual BERT. We explore cross-lingual transfer in multilingual BERT from five related languages in zero-shot and direct transfer setups, and we show that even with our modestly-sized training set, we improve Danish NER over a recent cross-lingual approach, as well as over zero-shot transfer from five related languages. Using multilingual BERT, we achieve higher performance by fine-tuning on both DaNE and a larger Bokm{\u0007a}l (Norwegian) training set compared to only using DaNE. However, the highest performance isachieved by using a Danish BERT fine-tuned on DaNE. Our dataset enables improvements and applicability for Danish NER beyond cross-lingual methods. We employ a thorough error analysis of the predictions of the best models for seen and unseen entities, as well as their robustness on un-capitalized text. The annotated dataset and all the trained models are made publicly available.\",\n language = \"English\",\n ISBN = \"979-10-95546-34-4\",\n}\n", "homepage": "https://github.com/alexandrainst/danlp/blob/master/docs/docs/datasets.md#dane", "license": "CC BY-SA 4.0", "features": {"sent_id": {"dtype": "string", "id": null, "_type": "Value"}, "text": {"dtype": "string", "id": null, "_type": "Value"}, "tok_ids": {"feature": {"dtype": "int64", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "tokens": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "lemmas": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "pos_tags": {"feature": {"num_classes": 17, "names": ["NUM", "CCONJ", "PRON", "VERB", "INTJ", "AUX", "ADJ", "PROPN", "PART", "ADV", "PUNCT", "ADP", "NOUN", "X", "DET", "SYM", "SCONJ"], "names_file": null, "id": null, "_type": "ClassLabel"}, "length": -1, "id": null, "_type": "Sequence"}, "morph_tags": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "dep_ids": {"feature": {"dtype": "int64", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "dep_labels": {"feature": {"num_classes": 36, "names": ["parataxis", "mark", "nummod", "discourse", "compound:prt", "reparandum", "vocative", "list", "obj", "dep", "det", "obl:loc", "flat", "iobj", "cop", "expl", "obl", "conj", "nmod", "root", "acl:relcl", "goeswith", "appos", "fixed", "obl:tmod", "xcomp", "advmod", "nmod:poss", "aux", "ccomp", "amod", "cc", "advcl", "nsubj", "punct", "case"], "names_file": null, "id": null, "_type": "ClassLabel"}, "length": -1, "id": null, "_type": "Sequence"}, "ner_tags": {"feature": {"num_classes": 9, "names": ["O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC", "B-MISC", "I-MISC"], "names_file": null, "id": null, "_type": "ClassLabel"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "dane", "config_name": "default", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 7311252, "num_examples": 4384, "dataset_name": "dane"}, "test": {"name": "test", "num_bytes": 909739, "num_examples": 566, "dataset_name": "dane"}, "validation": {"name": "validation", "num_bytes": 940453, "num_examples": 565, "dataset_name": "dane"}}, "download_checksums": {"https://danlp.alexandra.dk/304bd159d5de/datasets/ddt.zip": {"num_bytes": 1209710, "checksum": "df97f3eaa396fd52bf35060c63960aebefaa47e5e6125fb75fe3be098384ebd2"}}, "download_size": 1209710, "post_processing_size": null, "dataset_size": 9161444, "size_in_bytes": 10371154}}
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