sashketka commited on
Commit
c9082cc
1 Parent(s): 9c45107

Update spaCy pipeline

Browse files
.gitattributes CHANGED
@@ -32,3 +32,9 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
 
 
 
 
 
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ en_food_entity_extractor_v2-any-py3-none-any.whl filter=lfs diff=lfs merge=lfs -text
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+ vocab/key2row filter=lfs diff=lfs merge=lfs -text
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+ vocab/vectors filter=lfs diff=lfs merge=lfs -text
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+ vocab/strings.json filter=lfs diff=lfs merge=lfs -text
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+ tok2vec/model filter=lfs diff=lfs merge=lfs -text
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+ ner/model filter=lfs diff=lfs merge=lfs -text
README.md ADDED
@@ -0,0 +1,97 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
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+ tags:
3
+ - spacy
4
+ - token-classification
5
+ language:
6
+ - en
7
+ license: mit
8
+ model-index:
9
+ - name: en_food_entity_extractor_v2
10
+ results:
11
+ - task:
12
+ name: NER
13
+ type: token-classification
14
+ metrics:
15
+ - name: NER Precision
16
+ type: precision
17
+ value: 0.8535469108
18
+ - name: NER Recall
19
+ type: recall
20
+ value: 0.8592748397
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+ - name: NER F Score
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+ type: f_score
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+ value: 0.8564012977
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+ - task:
25
+ name: TAG
26
+ type: token-classification
27
+ metrics:
28
+ - name: TAG (XPOS) Accuracy
29
+ type: accuracy
30
+ value: 0.9734404547
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+ - task:
32
+ name: UNLABELED_DEPENDENCIES
33
+ type: token-classification
34
+ metrics:
35
+ - name: Unlabeled Attachment Score (UAS)
36
+ type: f_score
37
+ value: 0.9204363007
38
+ - task:
39
+ name: LABELED_DEPENDENCIES
40
+ type: token-classification
41
+ metrics:
42
+ - name: Labeled Attachment Score (LAS)
43
+ type: f_score
44
+ value: 0.9023174614
45
+ - task:
46
+ name: SENTS
47
+ type: token-classification
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+ metrics:
49
+ - name: Sentences F-Score
50
+ type: f_score
51
+ value: 0.90444794
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+ ---
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+ English pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attribute_ruler, lemmatizer.
54
+
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+ | Feature | Description |
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+ | --- | --- |
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+ | **Name** | `en_food_entity_extractor_v2` |
58
+ | **Version** | `3.4.1` |
59
+ | **spaCy** | `>=3.4.0,<3.5.0` |
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+ | **Default Pipeline** | `tok2vec`, `tagger`, `parser`, `attribute_ruler`, `lemmatizer`, `ner` |
61
+ | **Components** | `tok2vec`, `tagger`, `parser`, `senter`, `attribute_ruler`, `lemmatizer`, `ner` |
62
+ | **Vectors** | 514157 keys, 514157 unique vectors (300 dimensions) |
63
+ | **Sources** | [OntoNotes 5](https://catalog.ldc.upenn.edu/LDC2013T19) (Ralph Weischedel, Martha Palmer, Mitchell Marcus, Eduard Hovy, Sameer Pradhan, Lance Ramshaw, Nianwen Xue, Ann Taylor, Jeff Kaufman, Michelle Franchini, Mohammed El-Bachouti, Robert Belvin, Ann Houston)<br />[ClearNLP Constituent-to-Dependency Conversion](https://github.com/clir/clearnlp-guidelines/blob/master/md/components/dependency_conversion.md) (Emory University)<br />[WordNet 3.0](https://wordnet.princeton.edu/) (Princeton University)<br />[Explosion Vectors (OSCAR 2109 + Wikipedia + OpenSubtitles + WMT News Crawl)](https://github.com/explosion/spacy-vectors-builder) (Explosion) |
64
+ | **License** | `MIT` |
65
+ | **Author** | [Explosion](https://explosion.ai) |
66
+
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+ ### Label Scheme
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+
69
+ <details>
70
+
71
+ <summary>View label scheme (114 labels for 3 components)</summary>
72
+
73
+ | Component | Labels |
74
+ | --- | --- |
75
+ | **`tagger`** | `$`, `''`, `,`, `-LRB-`, `-RRB-`, `.`, `:`, `ADD`, `AFX`, `CC`, `CD`, `DT`, `EX`, `FW`, `HYPH`, `IN`, `JJ`, `JJR`, `JJS`, `LS`, `MD`, `NFP`, `NN`, `NNP`, `NNPS`, `NNS`, `PDT`, `POS`, `PRP`, `PRP$`, `RB`, `RBR`, `RBS`, `RP`, `SYM`, `TO`, `UH`, `VB`, `VBD`, `VBG`, `VBN`, `VBP`, `VBZ`, `WDT`, `WP`, `WP$`, `WRB`, `XX`, `_SP`, ```` |
76
+ | **`parser`** | `ROOT`, `acl`, `acomp`, `advcl`, `advmod`, `agent`, `amod`, `appos`, `attr`, `aux`, `auxpass`, `case`, `cc`, `ccomp`, `compound`, `conj`, `csubj`, `csubjpass`, `dative`, `dep`, `det`, `dobj`, `expl`, `intj`, `mark`, `meta`, `neg`, `nmod`, `npadvmod`, `nsubj`, `nsubjpass`, `nummod`, `oprd`, `parataxis`, `pcomp`, `pobj`, `poss`, `preconj`, `predet`, `prep`, `prt`, `punct`, `quantmod`, `relcl`, `xcomp` |
77
+ | **`ner`** | `CARDINAL`, `DATE`, `EVENT`, `FAC`, `FOOD`, `GPE`, `LANGUAGE`, `LAW`, `LOC`, `MONEY`, `NORP`, `ORDINAL`, `ORG`, `PERCENT`, `PERSON`, `PRODUCT`, `QUANTITY`, `TIME`, `WORK_OF_ART` |
78
+
79
+ </details>
80
+
81
+ ### Accuracy
82
+
83
+ | Type | Score |
84
+ | --- | --- |
85
+ | `TOKEN_ACC` | 99.93 |
86
+ | `TOKEN_P` | 99.57 |
87
+ | `TOKEN_R` | 99.58 |
88
+ | `TOKEN_F` | 99.57 |
89
+ | `TAG_ACC` | 97.34 |
90
+ | `SENTS_P` | 91.79 |
91
+ | `SENTS_R` | 89.14 |
92
+ | `SENTS_F` | 90.44 |
93
+ | `DEP_UAS` | 92.04 |
94
+ | `DEP_LAS` | 90.23 |
95
+ | `ENTS_P` | 85.35 |
96
+ | `ENTS_R` | 85.93 |
97
+ | `ENTS_F` | 85.64 |
attribute_ruler/patterns ADDED
Binary file (14.8 kB). View file
 
config.cfg ADDED
@@ -0,0 +1,266 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ [paths]
2
+ train = null
3
+ dev = null
4
+ vectors = null
5
+ init_tok2vec = null
6
+
7
+ [system]
8
+ gpu_allocator = null
9
+ seed = 0
10
+
11
+ [nlp]
12
+ lang = "en"
13
+ pipeline = ["tok2vec","tagger","parser","senter","attribute_ruler","lemmatizer","ner"]
14
+ disabled = ["senter"]
15
+ before_creation = null
16
+ after_creation = null
17
+ after_pipeline_creation = null
18
+ batch_size = 256
19
+ tokenizer = {"@tokenizers":"spacy.Tokenizer.v1"}
20
+
21
+ [components]
22
+
23
+ [components.attribute_ruler]
24
+ factory = "attribute_ruler"
25
+ scorer = {"@scorers":"spacy.attribute_ruler_scorer.v1"}
26
+ validate = false
27
+
28
+ [components.lemmatizer]
29
+ factory = "lemmatizer"
30
+ mode = "rule"
31
+ model = null
32
+ overwrite = false
33
+ scorer = {"@scorers":"spacy.lemmatizer_scorer.v1"}
34
+
35
+ [components.ner]
36
+ factory = "ner"
37
+ incorrect_spans_key = null
38
+ moves = null
39
+ scorer = {"@scorers":"spacy.ner_scorer.v1"}
40
+ update_with_oracle_cut_size = 100
41
+
42
+ [components.ner.model]
43
+ @architectures = "spacy.TransitionBasedParser.v2"
44
+ state_type = "ner"
45
+ extra_state_tokens = false
46
+ hidden_width = 64
47
+ maxout_pieces = 2
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+ use_upper = true
49
+ nO = null
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+
51
+ [components.ner.model.tok2vec]
52
+ @architectures = "spacy.Tok2Vec.v2"
53
+
54
+ [components.ner.model.tok2vec.embed]
55
+ @architectures = "spacy.MultiHashEmbed.v2"
56
+ width = 96
57
+ attrs = ["NORM","PREFIX","SUFFIX","SHAPE","SPACY"]
58
+ rows = [5000,1000,2500,2500,50]
59
+ include_static_vectors = true
60
+
61
+ [components.ner.model.tok2vec.encode]
62
+ @architectures = "spacy.MaxoutWindowEncoder.v2"
63
+ width = 96
64
+ depth = 4
65
+ window_size = 1
66
+ maxout_pieces = 3
67
+
68
+ [components.parser]
69
+ factory = "parser"
70
+ learn_tokens = false
71
+ min_action_freq = 30
72
+ moves = null
73
+ scorer = {"@scorers":"spacy.parser_scorer.v1"}
74
+ update_with_oracle_cut_size = 100
75
+
76
+ [components.parser.model]
77
+ @architectures = "spacy.TransitionBasedParser.v2"
78
+ state_type = "parser"
79
+ extra_state_tokens = false
80
+ hidden_width = 64
81
+ maxout_pieces = 2
82
+ use_upper = true
83
+ nO = null
84
+
85
+ [components.parser.model.tok2vec]
86
+ @architectures = "spacy.Tok2VecListener.v1"
87
+ width = ${components.tok2vec.model.encode:width}
88
+ upstream = "tok2vec"
89
+
90
+ [components.senter]
91
+ factory = "senter"
92
+ overwrite = false
93
+ scorer = {"@scorers":"spacy.senter_scorer.v1"}
94
+
95
+ [components.senter.model]
96
+ @architectures = "spacy.Tagger.v2"
97
+ nO = null
98
+ normalize = false
99
+
100
+ [components.senter.model.tok2vec]
101
+ @architectures = "spacy.Tok2Vec.v2"
102
+
103
+ [components.senter.model.tok2vec.embed]
104
+ @architectures = "spacy.MultiHashEmbed.v2"
105
+ width = 16
106
+ attrs = ["NORM","PREFIX","SUFFIX","SHAPE","SPACY"]
107
+ rows = [1000,500,500,500,50]
108
+ include_static_vectors = true
109
+
110
+ [components.senter.model.tok2vec.encode]
111
+ @architectures = "spacy.MaxoutWindowEncoder.v2"
112
+ width = 16
113
+ depth = 2
114
+ window_size = 1
115
+ maxout_pieces = 2
116
+
117
+ [components.tagger]
118
+ factory = "tagger"
119
+ neg_prefix = "!"
120
+ overwrite = false
121
+ scorer = {"@scorers":"spacy.tagger_scorer.v1"}
122
+
123
+ [components.tagger.model]
124
+ @architectures = "spacy.Tagger.v2"
125
+ nO = null
126
+ normalize = false
127
+
128
+ [components.tagger.model.tok2vec]
129
+ @architectures = "spacy.Tok2VecListener.v1"
130
+ width = ${components.tok2vec.model.encode:width}
131
+ upstream = "tok2vec"
132
+
133
+ [components.tok2vec]
134
+ factory = "tok2vec"
135
+
136
+ [components.tok2vec.model]
137
+ @architectures = "spacy.Tok2Vec.v2"
138
+
139
+ [components.tok2vec.model.embed]
140
+ @architectures = "spacy.MultiHashEmbed.v2"
141
+ width = ${components.tok2vec.model.encode:width}
142
+ attrs = ["NORM","PREFIX","SUFFIX","SHAPE","SPACY"]
143
+ rows = [5000,1000,2500,2500,50]
144
+ include_static_vectors = true
145
+
146
+ [components.tok2vec.model.encode]
147
+ @architectures = "spacy.MaxoutWindowEncoder.v2"
148
+ width = 96
149
+ depth = 4
150
+ window_size = 1
151
+ maxout_pieces = 3
152
+
153
+ [corpora]
154
+
155
+ [corpora.dev]
156
+ @readers = "spacy.Corpus.v1"
157
+ path = ${paths.dev}
158
+ gold_preproc = false
159
+ max_length = 0
160
+ limit = 0
161
+ augmenter = null
162
+
163
+ [corpora.train]
164
+ @readers = "spacy.Corpus.v1"
165
+ path = ${paths.train}
166
+ gold_preproc = false
167
+ max_length = 0
168
+ limit = 0
169
+ augmenter = null
170
+
171
+ [training]
172
+ train_corpus = "corpora.train"
173
+ dev_corpus = "corpora.dev"
174
+ seed = ${system:seed}
175
+ gpu_allocator = ${system:gpu_allocator}
176
+ dropout = 0.1
177
+ accumulate_gradient = 1
178
+ patience = 5000
179
+ max_epochs = 0
180
+ max_steps = 100000
181
+ eval_frequency = 1000
182
+ frozen_components = []
183
+ before_to_disk = null
184
+ annotating_components = []
185
+
186
+ [training.batcher]
187
+ @batchers = "spacy.batch_by_words.v1"
188
+ discard_oversize = false
189
+ tolerance = 0.2
190
+ get_length = null
191
+
192
+ [training.batcher.size]
193
+ @schedules = "compounding.v1"
194
+ start = 100
195
+ stop = 1000
196
+ compound = 1.001
197
+ t = 0.0
198
+
199
+ [training.logger]
200
+ @loggers = "spacy.ConsoleLogger.v1"
201
+ progress_bar = false
202
+
203
+ [training.optimizer]
204
+ @optimizers = "Adam.v1"
205
+ beta1 = 0.9
206
+ beta2 = 0.999
207
+ L2_is_weight_decay = true
208
+ L2 = 0.01
209
+ grad_clip = 1.0
210
+ use_averages = true
211
+ eps = 0.00000001
212
+ learn_rate = 0.001
213
+
214
+ [training.score_weights]
215
+ tag_acc = 0.16
216
+ dep_uas = 0.0
217
+ dep_las = 0.16
218
+ dep_las_per_type = null
219
+ sents_p = null
220
+ sents_r = null
221
+ sents_f = 0.02
222
+ lemma_acc = 0.5
223
+ ents_f = 0.16
224
+ ents_p = 0.0
225
+ ents_r = 0.0
226
+ ents_per_type = null
227
+ speed = 0.0
228
+
229
+ [pretraining]
230
+
231
+ [initialize]
232
+ vocab_data = null
233
+ vectors = ${paths.vectors}
234
+ init_tok2vec = ${paths.init_tok2vec}
235
+ before_init = null
236
+ after_init = null
237
+
238
+ [initialize.components]
239
+
240
+ [initialize.components.ner]
241
+
242
+ [initialize.components.ner.labels]
243
+ @readers = "spacy.read_labels.v1"
244
+ path = "corpus/labels/ner.json"
245
+ require = false
246
+
247
+ [initialize.components.parser]
248
+
249
+ [initialize.components.parser.labels]
250
+ @readers = "spacy.read_labels.v1"
251
+ path = "corpus/labels/parser.json"
252
+ require = false
253
+
254
+ [initialize.components.tagger]
255
+
256
+ [initialize.components.tagger.labels]
257
+ @readers = "spacy.read_labels.v1"
258
+ path = "corpus/labels/tagger.json"
259
+ require = false
260
+
261
+ [initialize.lookups]
262
+ @misc = "spacy.LookupsDataLoader.v1"
263
+ lang = ${nlp.lang}
264
+ tables = ["lexeme_norm"]
265
+
266
+ [initialize.tokenizer]
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1
+ {
2
+ "lang":"en",
3
+ "name":"food_entity_extractor_v2",
4
+ "version":"3.4.1",
5
+ "description":"English pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attribute_ruler, lemmatizer.",
6
+ "author":"Explosion",
7
+ "email":"[email protected]",
8
+ "url":"https://explosion.ai",
9
+ "license":"MIT",
10
+ "spacy_version":">=3.4.0,<3.5.0",
11
+ "spacy_git_version":"2b5f955c2",
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+ "vectors":{
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+ "width":300,
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+ "vectors":514157,
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+ "keys":514157,
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+ "name":"en_vectors"
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+ },
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+ "labels":{
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+ "tok2vec":[
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+
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+ ],
22
+ "tagger":[
23
+ "$",
24
+ "''",
25
+ ",",
26
+ "-LRB-",
27
+ "-RRB-",
28
+ ".",
29
+ ":",
30
+ "ADD",
31
+ "AFX",
32
+ "CC",
33
+ "CD",
34
+ "DT",
35
+ "EX",
36
+ "FW",
37
+ "HYPH",
38
+ "IN",
39
+ "JJ",
40
+ "JJR",
41
+ "JJS",
42
+ "LS",
43
+ "MD",
44
+ "NFP",
45
+ "NN",
46
+ "NNP",
47
+ "NNPS",
48
+ "NNS",
49
+ "PDT",
50
+ "POS",
51
+ "PRP",
52
+ "PRP$",
53
+ "RB",
54
+ "RBR",
55
+ "RBS",
56
+ "RP",
57
+ "SYM",
58
+ "TO",
59
+ "UH",
60
+ "VB",
61
+ "VBD",
62
+ "VBG",
63
+ "VBN",
64
+ "VBP",
65
+ "VBZ",
66
+ "WDT",
67
+ "WP",
68
+ "WP$",
69
+ "WRB",
70
+ "XX",
71
+ "_SP",
72
+ "``"
73
+ ],
74
+ "parser":[
75
+ "ROOT",
76
+ "acl",
77
+ "acomp",
78
+ "advcl",
79
+ "advmod",
80
+ "agent",
81
+ "amod",
82
+ "appos",
83
+ "attr",
84
+ "aux",
85
+ "auxpass",
86
+ "case",
87
+ "cc",
88
+ "ccomp",
89
+ "compound",
90
+ "conj",
91
+ "csubj",
92
+ "csubjpass",
93
+ "dative",
94
+ "dep",
95
+ "det",
96
+ "dobj",
97
+ "expl",
98
+ "intj",
99
+ "mark",
100
+ "meta",
101
+ "neg",
102
+ "nmod",
103
+ "npadvmod",
104
+ "nsubj",
105
+ "nsubjpass",
106
+ "nummod",
107
+ "oprd",
108
+ "parataxis",
109
+ "pcomp",
110
+ "pobj",
111
+ "poss",
112
+ "preconj",
113
+ "predet",
114
+ "prep",
115
+ "prt",
116
+ "punct",
117
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