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zh_core_web_trf

Token Classification
spaCy
Chinese
Eval Results
Model card Files Community
adrianeboyd commited on
Commit
c70655e
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1 Parent(s): e4a727e

Update spaCy pipeline

Browse files
Files changed (11) hide show
  1. README.md +19 -19
  2. accuracy.json +203 -203
  3. config.cfg +51 -24
  4. meta.json +208 -208
  5. ner/model +2 -2
  6. parser/model +2 -2
  7. tagger/model +2 -2
  8. transformer/cfg +1 -1
  9. transformer/model +2 -2
  10. vocab/strings.json +2 -2
  11. zh_core_web_trf-any-py3-none-any.whl +2 -2
README.md CHANGED
@@ -14,51 +14,51 @@ model-index:
14
  metrics:
15
  - name: NER Precision
16
  type: precision
17
- value: 0.7588215895
18
  - name: NER Recall
19
  type: recall
20
- value: 0.7585714286
21
  - name: NER F Score
22
  type: f_score
23
- value: 0.7586964884
24
  - task:
25
  name: TAG
26
  type: token-classification
27
  metrics:
28
  - name: TAG (XPOS) Accuracy
29
  type: accuracy
30
- value: 0.9248343224
31
  - task:
32
  name: UNLABELED_DEPENDENCIES
33
  type: token-classification
34
  metrics:
35
  - name: Unlabeled Attachment Score (UAS)
36
  type: f_score
37
- value: 0.7702832979
38
  - task:
39
  name: LABELED_DEPENDENCIES
40
  type: token-classification
41
  metrics:
42
  - name: Labeled Attachment Score (LAS)
43
  type: f_score
44
- value: 0.7326537539
45
  - task:
46
  name: SENTS
47
  type: token-classification
48
  metrics:
49
  - name: Sentences F-Score
50
  type: f_score
51
- value: 0.6942608696
52
  ---
53
  ### Details: https://spacy.io/models/zh#zh_core_web_trf
54
 
55
- Chinese transformer pipeline (bert-base-chinese). Components: transformer, tagger, parser, ner, attribute_ruler.
56
 
57
  | Feature | Description |
58
  | --- | --- |
59
  | **Name** | `zh_core_web_trf` |
60
- | **Version** | `3.6.1` |
61
- | **spaCy** | `>=3.6.0,<3.7.0` |
62
  | **Default Pipeline** | `transformer`, `tagger`, `parser`, `attribute_ruler`, `ner` |
63
  | **Components** | `transformer`, `tagger`, `parser`, `attribute_ruler`, `ner` |
64
  | **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
@@ -88,12 +88,12 @@ Chinese transformer pipeline (bert-base-chinese). Components: transformer, tagge
88
  | `TOKEN_P` | 94.58 |
89
  | `TOKEN_R` | 91.36 |
90
  | `TOKEN_F` | 92.94 |
91
- | `TAG_ACC` | 92.48 |
92
- | `SENTS_P` | 72.67 |
93
- | `SENTS_R` | 66.46 |
94
- | `SENTS_F` | 69.43 |
95
- | `DEP_UAS` | 77.03 |
96
- | `DEP_LAS` | 73.27 |
97
- | `ENTS_P` | 75.88 |
98
- | `ENTS_R` | 75.86 |
99
- | `ENTS_F` | 75.87 |
 
14
  metrics:
15
  - name: NER Precision
16
  type: precision
17
+ value: 0.7608897127
18
  - name: NER Recall
19
  type: recall
20
+ value: 0.7217582418
21
  - name: NER F Score
22
  type: f_score
23
+ value: 0.7408075795
24
  - task:
25
  name: TAG
26
  type: token-classification
27
  metrics:
28
  - name: TAG (XPOS) Accuracy
29
  type: accuracy
30
+ value: 0.9175332527
31
  - task:
32
  name: UNLABELED_DEPENDENCIES
33
  type: token-classification
34
  metrics:
35
  - name: Unlabeled Attachment Score (UAS)
36
  type: f_score
37
+ value: 0.7572203056
38
  - task:
39
  name: LABELED_DEPENDENCIES
40
  type: token-classification
41
  metrics:
42
  - name: Labeled Attachment Score (LAS)
43
  type: f_score
44
+ value: 0.7145288854
45
  - task:
46
  name: SENTS
47
  type: token-classification
48
  metrics:
49
  - name: Sentences F-Score
50
  type: f_score
51
+ value: 0.6920716113
52
  ---
53
  ### Details: https://spacy.io/models/zh#zh_core_web_trf
54
 
55
+ Chinese transformer pipeline (Transformer(name='bert-base-chinese', piece_encoder='bert-wordpiece', stride=152, type='bert', width=768, window=208, vocab_size=21128)). Components: transformer, tagger, parser, ner, attribute_ruler.
56
 
57
  | Feature | Description |
58
  | --- | --- |
59
  | **Name** | `zh_core_web_trf` |
60
+ | **Version** | `3.7.2` |
61
+ | **spaCy** | `>=3.7.0,<3.8.0` |
62
  | **Default Pipeline** | `transformer`, `tagger`, `parser`, `attribute_ruler`, `ner` |
63
  | **Components** | `transformer`, `tagger`, `parser`, `attribute_ruler`, `ner` |
64
  | **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
 
88
  | `TOKEN_P` | 94.58 |
89
  | `TOKEN_R` | 91.36 |
90
  | `TOKEN_F` | 92.94 |
91
+ | `TAG_ACC` | 91.75 |
92
+ | `SENTS_P` | 70.92 |
93
+ | `SENTS_R` | 67.57 |
94
+ | `SENTS_F` | 69.21 |
95
+ | `DEP_UAS` | 75.72 |
96
+ | `DEP_LAS` | 71.45 |
97
+ | `ENTS_P` | 76.09 |
98
+ | `ENTS_R` | 72.18 |
99
+ | `ENTS_F` | 74.08 |
accuracy.json CHANGED
@@ -3,212 +3,207 @@
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@@ -216,14 +211,19 @@
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@@ -231,105 +231,105 @@
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+ "f": 0.7366304785
112
  },
113
  "advmod:rcomp": {
114
+ "p": 0.8369230769,
115
+ "r": 0.7534626039,
116
+ "f": 0.7930029155
117
  },
118
  "nmod:topic": {
119
+ "p": 0.4624505929,
120
+ "r": 0.3798701299,
121
+ "f": 0.4171122995
122
  },
123
  "cop": {
124
+ "p": 0.8350515464,
125
+ "r": 0.6254826255,
126
+ "f": 0.7152317881
127
  },
128
  "discourse": {
129
+ "p": 0.5836267606,
130
+ "r": 0.547029703,
131
+ "f": 0.5647359455
132
  },
133
  "neg": {
134
+ "p": 0.8730650155,
135
+ "r": 0.6706302021,
136
+ "f": 0.7585743107
137
  },
138
  "aux:modal": {
139
+ "p": 0.8915401302,
140
+ "r": 0.8500517063,
141
+ "f": 0.870301747
142
  },
143
  "nmod": {
144
+ "p": 0.7740524781,
145
+ "r": 0.7204884668,
146
+ "f": 0.7463106114
147
  },
148
  "aux:ba": {
149
+ "p": 0.9106145251,
150
+ "r": 0.8670212766,
151
+ "f": 0.8882833787
152
  },
153
  "advmod:loc": {
154
+ "p": 0.7519379845,
155
+ "r": 0.5756676558,
156
+ "f": 0.6521008403
157
  },
158
  "aux:asp": {
159
+ "p": 0.9163179916,
160
+ "r": 0.8732057416,
161
+ "f": 0.894242548
162
  },
163
  "conj": {
164
+ "p": 0.6111647672,
165
+ "r": 0.5981096408,
166
+ "f": 0.6045667335
167
  },
168
  "nsubjpass": {
169
+ "p": 0.9,
170
+ "r": 0.72,
171
+ "f": 0.8
172
  },
173
  "compound:vc": {
174
+ "p": 0.4628820961,
175
+ "r": 0.5492227979,
176
+ "f": 0.5023696682
177
  },
178
  "advcl:loc": {
179
+ "p": 0.6488549618,
180
+ "r": 0.6071428571,
181
+ "f": 0.6273062731
182
  },
183
  "cc": {
184
+ "p": 0.7943396226,
185
+ "r": 0.7471162378,
186
+ "f": 0.7700045725
187
  },
188
  "advmod:dvp": {
189
+ "p": 0.9212598425,
190
  "r": 0.7267080745,
191
+ "f": 0.8125
 
 
 
 
 
192
  },
193
  "appos": {
194
+ "p": 0.9382716049,
195
+ "r": 0.8735632184,
196
+ "f": 0.9047619048
197
  },
198
  "nmod:poss": {
199
+ "p": 0.7280701754,
200
+ "r": 0.6148148148,
201
+ "f": 0.6666666667
202
  },
203
  "name": {
204
+ "p": 0.6261682243,
205
+ "r": 0.4962962963,
206
+ "f": 0.5537190083
207
  },
208
  "nsubj:xsubj": {
209
  "p": 0.0,
 
211
  "f": 0.0
212
  },
213
  "nmod:range": {
214
+ "p": 0.8098859316,
215
+ "r": 0.7147651007,
216
+ "f": 0.7593582888
217
  },
218
  "parataxis:prnmod": {
219
+ "p": 0.3442622951,
220
+ "r": 0.1578947368,
221
+ "f": 0.2164948454
222
+ },
223
+ "amod:ordmod": {
224
+ "p": 0.7547169811,
225
+ "r": 0.625,
226
+ "f": 0.6837606838
227
  },
228
  "erased": {
229
  "p": 0.0,
 
231
  "f": 0.0
232
  },
233
  "etc": {
234
+ "p": 0.9277108434,
235
+ "r": 0.9166666667,
236
+ "f": 0.9221556886
237
  }
238
  },
239
+ "ents_p": 0.7608897127,
240
+ "ents_r": 0.7217582418,
241
+ "ents_f": 0.7408075795,
242
  "ents_per_type": {
243
  "DATE": {
244
+ "p": 0.7811607992,
245
+ "r": 0.8136769078,
246
+ "f": 0.7970873786
247
  },
248
  "GPE": {
249
+ "p": 0.8325837081,
250
+ "r": 0.8142717498,
251
+ "f": 0.8233259204
 
 
 
 
 
252
  },
253
  "ORDINAL": {
254
+ "p": 0.8488372093,
255
+ "r": 0.7684210526,
256
+ "f": 0.8066298343
257
  },
258
  "FAC": {
259
+ "p": 0.3906976744,
260
+ "r": 0.4516129032,
261
+ "f": 0.4189526185
 
 
 
 
 
 
 
 
 
 
262
  },
263
  "LOC": {
264
+ "p": 0.5012406948,
265
+ "r": 0.5430107527,
266
+ "f": 0.5212903226
267
  },
268
  "QUANTITY": {
269
+ "p": 0.696,
270
+ "r": 0.6444444444,
271
+ "f": 0.6692307692
272
  },
273
+ "ORG": {
274
+ "p": 0.7461476075,
275
+ "r": 0.700152207,
276
+ "f": 0.7224185316
277
+ },
278
+ "PERSON": {
279
+ "p": 0.8739386022,
280
+ "r": 0.8621134021,
281
+ "f": 0.8679857282
282
+ },
283
+ "CARDINAL": {
284
+ "p": 0.6729088639,
285
+ "r": 0.5433467742,
286
+ "f": 0.6012269939
287
+ },
288
+ "NORP": {
289
+ "p": 0.6961038961,
290
+ "r": 0.5630252101,
291
+ "f": 0.6225319396
292
  },
293
  "WORK_OF_ART": {
294
+ "p": 0.5625,
295
+ "r": 0.3,
296
+ "f": 0.3913043478
297
+ },
298
+ "TIME": {
299
+ "p": 0.7875647668,
300
+ "r": 0.7378640777,
301
+ "f": 0.7619047619
302
  },
303
  "MONEY": {
304
+ "p": 0.9256198347,
305
  "r": 0.8296296296,
306
+ "f": 0.875
307
  },
308
  "EVENT": {
309
+ "p": 0.5430463576,
310
+ "r": 0.6029411765,
311
+ "f": 0.5714285714
 
 
 
 
 
 
 
 
 
 
312
  },
313
  "PERCENT": {
314
+ "p": 0.869047619,
315
+ "r": 0.8795180723,
316
+ "f": 0.874251497
317
+ },
318
+ "PRODUCT": {
319
+ "p": 0.3793103448,
320
+ "r": 0.2244897959,
321
+ "f": 0.2820512821
322
  },
323
  "LAW": {
324
+ "p": 0.3571428571,
325
+ "r": 0.25,
326
+ "f": 0.2941176471
327
  },
328
  "LANGUAGE": {
329
+ "p": 0.4666666667,
330
+ "r": 0.7777777778,
331
+ "f": 0.5833333333
332
  }
333
  },
334
+ "speed": 2677.6055974261
335
  }
config.cfg CHANGED
@@ -16,6 +16,7 @@ before_creation = null
16
  after_creation = null
17
  after_pipeline_creation = null
18
  batch_size = 64
 
19
 
20
  [nlp.tokenizer]
21
  @tokenizers = "spacy.zh.ChineseTokenizer"
@@ -45,10 +46,11 @@ use_upper = false
45
  nO = null
46
 
47
  [components.ner.model.tok2vec]
48
- @architectures = "spacy-transformers.TransformerListener.v1"
49
- grad_factor = 1.0
50
  upstream = "transformer"
51
  pooling = {"@layers":"reduce_mean.v1"}
 
52
 
53
  [components.parser]
54
  factory = "parser"
@@ -68,10 +70,11 @@ use_upper = false
68
  nO = null
69
 
70
  [components.parser.model.tok2vec]
71
- @architectures = "spacy-transformers.TransformerListener.v1"
72
- grad_factor = 1.0
73
  upstream = "transformer"
74
  pooling = {"@layers":"reduce_mean.v1"}
 
75
 
76
  [components.tagger]
77
  factory = "tagger"
@@ -86,32 +89,44 @@ nO = null
86
  normalize = false
87
 
88
  [components.tagger.model.tok2vec]
89
- @architectures = "spacy-transformers.TransformerListener.v1"
90
- grad_factor = 1.0
91
  upstream = "transformer"
92
  pooling = {"@layers":"reduce_mean.v1"}
 
93
 
94
  [components.transformer]
95
- factory = "transformer"
96
- max_batch_items = 4096
97
- set_extra_annotations = {"@annotation_setters":"spacy-transformers.null_annotation_setter.v1"}
98
 
99
  [components.transformer.model]
100
- name = "bert-base-chinese"
101
- @architectures = "spacy-transformers.TransformerModel.v3"
 
 
 
 
 
 
 
 
 
 
 
 
 
 
102
  mixed_precision = false
103
-
104
- [components.transformer.model.get_spans]
105
- @span_getters = "spacy-transformers.strided_spans.v1"
106
- window = 128
107
- stride = 96
108
 
109
  [components.transformer.model.grad_scaler_config]
110
 
111
- [components.transformer.model.tokenizer_config]
112
- use_fast = true
113
-
114
- [components.transformer.model.transformer_config]
 
115
 
116
  [corpora]
117
 
@@ -148,11 +163,11 @@ annotating_components = []
148
  before_update = null
149
 
150
  [training.batcher]
151
- @batchers = "spacy.batch_by_padded.v1"
152
- discard_oversize = true
153
- get_length = null
154
  size = 2000
155
- buffer = 256
 
156
 
157
  [training.logger]
158
  @loggers = "spacy.ConsoleLogger.v1"
@@ -220,6 +235,18 @@ require = false
220
  path = "corpus/labels/tagger.json"
221
  require = false
222
 
 
 
 
 
 
 
 
 
 
 
 
 
223
  [initialize.lookups]
224
  @misc = "spacy.LookupsDataLoader.v1"
225
  lang = ${nlp.lang}
 
16
  after_creation = null
17
  after_pipeline_creation = null
18
  batch_size = 64
19
+ vectors = {"@vectors":"spacy.Vectors.v1"}
20
 
21
  [nlp.tokenizer]
22
  @tokenizers = "spacy.zh.ChineseTokenizer"
 
46
  nO = null
47
 
48
  [components.ner.model.tok2vec]
49
+ @architectures = "spacy-curated-transformers.LastTransformerLayerListener.v1"
50
+ width = ${components.transformer.model.hidden_width}
51
  upstream = "transformer"
52
  pooling = {"@layers":"reduce_mean.v1"}
53
+ grad_factor = 1.0
54
 
55
  [components.parser]
56
  factory = "parser"
 
70
  nO = null
71
 
72
  [components.parser.model.tok2vec]
73
+ @architectures = "spacy-curated-transformers.LastTransformerLayerListener.v1"
74
+ width = ${components.transformer.model.hidden_width}
75
  upstream = "transformer"
76
  pooling = {"@layers":"reduce_mean.v1"}
77
+ grad_factor = 1.0
78
 
79
  [components.tagger]
80
  factory = "tagger"
 
89
  normalize = false
90
 
91
  [components.tagger.model.tok2vec]
92
+ @architectures = "spacy-curated-transformers.LastTransformerLayerListener.v1"
93
+ width = ${components.transformer.model.hidden_width}
94
  upstream = "transformer"
95
  pooling = {"@layers":"reduce_mean.v1"}
96
+ grad_factor = 1.0
97
 
98
  [components.transformer]
99
+ factory = "curated_transformer"
100
+ all_layer_outputs = false
101
+ frozen = false
102
 
103
  [components.transformer.model]
104
+ @architectures = "spacy-curated-transformers.BertTransformer.v1"
105
+ vocab_size = 21128
106
+ hidden_width = 768
107
+ piece_encoder = {"@architectures":"spacy-curated-transformers.BertWordpieceEncoder.v1"}
108
+ attention_probs_dropout_prob = 0.1
109
+ hidden_act = "gelu"
110
+ hidden_dropout_prob = 0.1
111
+ intermediate_width = 3072
112
+ layer_norm_eps = 0.0
113
+ max_position_embeddings = 512
114
+ model_max_length = 512
115
+ num_attention_heads = 12
116
+ num_hidden_layers = 12
117
+ padding_idx = 0
118
+ type_vocab_size = 2
119
+ torchscript = false
120
  mixed_precision = false
121
+ wrapped_listener = null
 
 
 
 
122
 
123
  [components.transformer.model.grad_scaler_config]
124
 
125
+ [components.transformer.model.with_spans]
126
+ @architectures = "spacy-curated-transformers.WithStridedSpans.v1"
127
+ stride = 152
128
+ window = 208
129
+ batch_size = 384
130
 
131
  [corpora]
132
 
 
163
  before_update = null
164
 
165
  [training.batcher]
166
+ @batchers = "spacy.batch_by_words.v1"
167
+ discard_oversize = false
 
168
  size = 2000
169
+ tolerance = 0.2
170
+ get_length = null
171
 
172
  [training.logger]
173
  @loggers = "spacy.ConsoleLogger.v1"
 
235
  path = "corpus/labels/tagger.json"
236
  require = false
237
 
238
+ [initialize.components.transformer]
239
+
240
+ [initialize.components.transformer.encoder_loader]
241
+ @model_loaders = "spacy-curated-transformers.HFTransformerEncoderLoader.v1"
242
+ name = "bert-base-chinese"
243
+ revision = "main"
244
+
245
+ [initialize.components.transformer.piecer_loader]
246
+ @model_loaders = "spacy-curated-transformers.HFPieceEncoderLoader.v1"
247
+ name = "bert-base-chinese"
248
+ revision = "main"
249
+
250
  [initialize.lookups]
251
  @misc = "spacy.LookupsDataLoader.v1"
252
  lang = ${nlp.lang}
meta.json CHANGED
@@ -1,14 +1,14 @@
1
  {
2
  "lang":"zh",
3
  "name":"core_web_trf",
4
- "version":"3.6.1",
5
- "description":"Chinese transformer pipeline (bert-base-chinese). Components: transformer, tagger, parser, ner, attribute_ruler.",
6
  "author":"Explosion",
7
  "email":"contact@explosion.ai",
8
  "url":"https://explosion.ai",
9
  "license":"MIT",
10
- "spacy_version":">=3.6.0,<3.7.0",
11
- "spacy_git_version":"c067b5264",
12
  "vectors":{
13
  "width":0,
14
  "vectors":0,
@@ -150,212 +150,207 @@
150
  "token_p":0.9458325855,
151
  "token_r":0.9136060443,
152
  "token_f":0.9294400505,
153
- "tag_acc":0.9248343224,
154
- "sents_p":0.7267431276,
155
- "sents_r":0.6645580156,
156
- "sents_f":0.6942608696,
157
- "dep_uas":0.7702832979,
158
- "dep_las":0.7326537539,
159
  "dep_las_per_type":{
160
  "dep":{
161
- "p":0.5677689191,
162
- "r":0.4389208851,
163
- "f":0.4950991566
164
  },
165
  "case":{
166
- "p":0.9181494662,
167
- "r":0.8445683802,
168
- "f":0.8798231765
169
  },
170
  "nmod:tmod":{
171
- "p":0.8106995885,
172
- "r":0.8040816327,
173
- "f":0.8073770492
174
  },
175
  "nummod":{
176
- "p":0.9027635619,
177
- "r":0.5876082612,
178
- "f":0.7118644068
179
  },
180
  "mark:clf":{
181
- "p":0.9524691358,
182
- "r":0.5755315181,
183
- "f":0.7175075564
184
  },
185
  "auxpass":{
186
- "p":0.9243243243,
187
  "r":0.9243243243,
188
- "f":0.9243243243
189
  },
190
  "nsubj":{
191
- "p":0.8731413262,
192
- "r":0.8006387422,
193
- "f":0.8353197488
194
  },
195
  "acl":{
196
- "p":0.815270936,
197
- "r":0.7343316694,
198
- "f":0.7726874818
199
  },
200
  "advmod":{
201
- "p":0.8811971373,
202
- "r":0.7676263886,
203
- "f":0.8205003938
204
  },
205
  "mark":{
206
- "p":0.8466637207,
207
- "r":0.8396143734,
208
- "f":0.8431243124
209
  },
210
  "xcomp":{
211
- "p":0.8116760829,
212
- "r":0.7019543974,
213
- "f":0.7528384279
214
  },
215
  "nmod:assmod":{
216
- "p":0.8690516369,
217
- "r":0.8014316838,
218
- "f":0.833873057
219
  },
220
  "det":{
221
- "p":0.8931726908,
222
- "r":0.6514352665,
223
- "f":0.7533875339
224
  },
225
  "amod":{
226
- "p":0.8383475528,
227
- "r":0.7333071485,
228
- "f":0.7823172009
229
  },
230
  "nmod:prep":{
231
- "p":0.839443313,
232
- "r":0.7480338778,
233
- "f":0.7911068458
234
  },
235
  "root":{
236
- "p":0.7950441798,
237
- "r":0.6890294656,
238
- "f":0.7382502453
239
  },
240
  "aux:prtmod":{
241
- "p":0.9437751004,
242
- "r":0.8392857143,
243
- "f":0.8884688091
244
  },
245
  "compound:nn":{
246
- "p":0.8118741059,
247
- "r":0.7681895093,
248
- "f":0.7894279256
249
  },
250
  "dobj":{
251
- "p":0.9199408187,
252
- "r":0.8289142349,
253
- "f":0.8720585944
254
  },
255
  "ccomp":{
256
- "p":0.7878282618,
257
- "r":0.734836703,
258
- "f":0.7604103802
259
  },
260
  "advmod:rcomp":{
261
- "p":0.8501529052,
262
- "r":0.7700831025,
263
- "f":0.8081395349
264
  },
265
  "nmod:topic":{
266
- "p":0.5123674912,
267
- "r":0.4707792208,
268
- "f":0.4906937394
269
  },
270
  "cop":{
271
- "p":0.8493150685,
272
- "r":0.6383526384,
273
- "f":0.7288758266
274
  },
275
  "discourse":{
276
- "p":0.6115044248,
277
- "r":0.5701320132,
278
- "f":0.5900939368
279
  },
280
  "neg":{
281
- "p":0.8767334361,
282
- "r":0.6765755054,
283
- "f":0.7637583893
284
  },
285
  "aux:modal":{
286
- "p":0.9025191676,
287
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288
- "f":0.8765957447
289
  },
290
  "nmod":{
291
- "p":0.8147612156,
292
- "r":0.7639077341,
293
- "f":0.7885154062
294
  },
295
  "aux:ba":{
296
- "p":0.9222222222,
297
- "r":0.8829787234,
298
- "f":0.902173913
299
  },
300
  "advmod:loc":{
301
- "p":0.8146718147,
302
- "r":0.6261127596,
303
- "f":0.7080536913
304
  },
305
  "aux:asp":{
306
- "p":0.930153322,
307
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308
- "f":0.8995057661
309
  },
310
  "conj":{
311
- "p":0.6438065883,
312
- "r":0.631758034,
313
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314
  },
315
  "nsubjpass":{
316
- "p":0.8913043478,
317
- "r":0.82,
318
- "f":0.8541666667
319
  },
320
  "compound:vc":{
321
- "p":0.5483870968,
322
- "r":0.6165803109,
323
- "f":0.5804878049
324
  },
325
  "advcl:loc":{
326
- "p":0.7307692308,
327
- "r":0.6785714286,
328
- "f":0.7037037037
329
  },
330
  "cc":{
331
- "p":0.8328530259,
332
- "r":0.769299024,
333
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