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zh_core_web_lg

Token Classification
spaCy
Chinese
Eval Results
Model card Files Community
adrianeboyd commited on
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1 Parent(s): 68251ed

Update spaCy pipeline

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Files changed (11) hide show
  1. README.md +19 -19
  2. accuracy.json +193 -193
  3. attribute_ruler/patterns +0 -0
  4. config.cfg +3 -2
  5. meta.json +196 -196
  6. ner/model +1 -1
  7. parser/model +1 -1
  8. senter/model +1 -1
  9. tagger/model +1 -1
  10. tok2vec/model +2 -2
  11. zh_core_web_lg-any-py3-none-any.whl +2 -2
README.md CHANGED
@@ -14,41 +14,41 @@ model-index:
14
  metrics:
15
  - name: NER Precision
16
  type: precision
17
- value: 0.7335270192
18
  - name: NER Recall
19
  type: recall
20
- value: 0.6936263736
21
  - name: NER F Score
22
  type: f_score
23
- value: 0.7130189212
24
  - task:
25
  name: TAG
26
  type: token-classification
27
  metrics:
28
  - name: TAG (XPOS) Accuracy
29
  type: accuracy
30
- value: 0.9027089042
31
  - task:
32
  name: UNLABELED_DEPENDENCIES
33
  type: token-classification
34
  metrics:
35
  - name: Unlabeled Attachment Score (UAS)
36
  type: f_score
37
- value: 0.7072780475
38
  - task:
39
  name: LABELED_DEPENDENCIES
40
  type: token-classification
41
  metrics:
42
  - name: Labeled Attachment Score (LAS)
43
  type: f_score
44
- value: 0.6564620448
45
  - task:
46
  name: SENTS
47
  type: token-classification
48
  metrics:
49
  - name: Sentences F-Score
50
  type: f_score
51
- value: 0.7530260108
52
  ---
53
  ### Details: https://spacy.io/models/zh#zh_core_web_lg
54
 
@@ -57,8 +57,8 @@ Chinese pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter,
57
  | Feature | Description |
58
  | --- | --- |
59
  | **Name** | `zh_core_web_lg` |
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- | **Version** | `3.4.0` |
61
- | **spaCy** | `>=3.4.0,<3.5.0` |
62
  | **Default Pipeline** | `tok2vec`, `tagger`, `parser`, `attribute_ruler`, `ner` |
63
  | **Components** | `tok2vec`, `tagger`, `parser`, `senter`, `attribute_ruler`, `ner` |
64
  | **Vectors** | 500000 keys, 500000 unique vectors (300 dimensions) |
@@ -84,16 +84,16 @@ Chinese pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter,
84
 
85
  | Type | Score |
86
  | --- | --- |
87
- | `TOKEN_ACC` | 97.88 |
88
  | `TOKEN_P` | 94.58 |
89
  | `TOKEN_R` | 91.36 |
90
  | `TOKEN_F` | 92.94 |
91
- | `TAG_ACC` | 90.27 |
92
- | `SENTS_P` | 77.74 |
93
- | `SENTS_R` | 73.01 |
94
- | `SENTS_F` | 75.30 |
95
- | `DEP_UAS` | 70.73 |
96
- | `DEP_LAS` | 65.65 |
97
- | `ENTS_P` | 73.35 |
98
- | `ENTS_R` | 69.36 |
99
- | `ENTS_F` | 71.30 |
14
  metrics:
15
  - name: NER Precision
16
  type: precision
17
+ value: 0.7355275444
18
  - name: NER Recall
19
  type: recall
20
+ value: 0.6925274725
21
  - name: NER F Score
22
  type: f_score
23
+ value: 0.7133801223
24
  - task:
25
  name: TAG
26
  type: token-classification
27
  metrics:
28
  - name: TAG (XPOS) Accuracy
29
  type: accuracy
30
+ value: 0.9033086963
31
  - task:
32
  name: UNLABELED_DEPENDENCIES
33
  type: token-classification
34
  metrics:
35
  - name: Unlabeled Attachment Score (UAS)
36
  type: f_score
37
+ value: 0.7085620979
38
  - task:
39
  name: LABELED_DEPENDENCIES
40
  type: token-classification
41
  metrics:
42
  - name: Labeled Attachment Score (LAS)
43
  type: f_score
44
+ value: 0.6571012366
45
  - task:
46
  name: SENTS
47
  type: token-classification
48
  metrics:
49
  - name: Sentences F-Score
50
  type: f_score
51
+ value: 0.7524359748
52
  ---
53
  ### Details: https://spacy.io/models/zh#zh_core_web_lg
54
 
57
  | Feature | Description |
58
  | --- | --- |
59
  | **Name** | `zh_core_web_lg` |
60
+ | **Version** | `3.5.0` |
61
+ | **spaCy** | `>=3.5.0,<3.6.0` |
62
  | **Default Pipeline** | `tok2vec`, `tagger`, `parser`, `attribute_ruler`, `ner` |
63
  | **Components** | `tok2vec`, `tagger`, `parser`, `senter`, `attribute_ruler`, `ner` |
64
  | **Vectors** | 500000 keys, 500000 unique vectors (300 dimensions) |
84
 
85
  | Type | Score |
86
  | --- | --- |
87
+ | `TOKEN_ACC` | 95.85 |
88
  | `TOKEN_P` | 94.58 |
89
  | `TOKEN_R` | 91.36 |
90
  | `TOKEN_F` | 92.94 |
91
+ | `TAG_ACC` | 90.33 |
92
+ | `SENTS_P` | 78.05 |
93
+ | `SENTS_R` | 72.63 |
94
+ | `SENTS_F` | 75.24 |
95
+ | `DEP_UAS` | 70.86 |
96
+ | `DEP_LAS` | 65.71 |
97
+ | `ENTS_P` | 73.55 |
98
+ | `ENTS_R` | 69.25 |
99
+ | `ENTS_F` | 71.34 |
accuracy.json CHANGED
@@ -1,209 +1,209 @@
1
  {
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- "token_acc": 0.9788303388,
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  "token_p": 0.9458325855,
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  "token_r": 0.9136060443,
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  "token_f": 0.9294400505,
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- "tag_acc": 0.9027089042,
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- "sents_p": 0.7773839064,
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- "sents_r": 0.7301481605,
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- "sents_f": 0.7530260108,
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- "dep_uas": 0.7072780475,
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- "dep_las": 0.6564620448,
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  "dep_las_per_type": {
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  "dep": {
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- "p": 0.483921457,
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- "r": 0.3436394867,
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- "f": 0.4018906942
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  },
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  "case": {
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- "p": 0.815636692,
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  "nmod:tmod": {
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- "p": 0.7218543046,
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  "nummod": {
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  "compound:vc": {
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  "advcl:loc": {
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- "p": 0.5040650407,
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  "cc": {
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- },
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- "name": {
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  "appos": {
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  "nmod:poss": {
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- "p": 0.7058823529,
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- "r": 0.4444444444,
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- "f": 0.5454545455
 
 
 
 
 
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  },
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  "nsubj:xsubj": {
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  "p": 0.0,
@@ -211,19 +211,19 @@
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  "f": 0.0
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  },
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  "nmod:range": {
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- "p": 0.7131782946,
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  "parataxis:prnmod": {
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  "r": 0.1203007519,
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- "r": 0.609375,
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  "erased": {
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  "p": 0.0,
@@ -231,89 +231,89 @@
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  "f": 0.0
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  },
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  "etc": {
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- "r": 0.9047619048,
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  }
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- "ents_p": 0.7335270192,
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- "ents_r": 0.6936263736,
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  "ents_per_type": {
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  "DATE": {
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  "GPE": {
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  "ORDINAL": {
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  "FAC": {
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  },
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  "ORG": {
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- "p": 0.6924939467,
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- "f": 0.6721504113
 
 
 
 
 
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  },
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  "QUANTITY": {
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  "PERSON": {
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  "CARDINAL": {
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- "LOC": {
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  "NORP": {
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  "TIME": {
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  "WORK_OF_ART": {
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  "MONEY": {
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- },
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- "PERCENT": {
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  "EVENT": {
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  },
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  "PRODUCT": {
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  "p": 0.0,
@@ -321,15 +321,15 @@
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  "f": 0.0
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  },
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  "LAW": {
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- "p": 0.3684210526,
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  "LANGUAGE": {
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  }
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  },
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- "speed": 7526.7870509872
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  }
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  "token_p": 0.9458325855,
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  "token_r": 0.9136060443,
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  "token_f": 0.9294400505,
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+ "tag_acc": 0.9033086963,
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  "nummod": {
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  "xcomp": {
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  "nmod:assmod": {
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134
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150
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189
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192
  },
193
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194
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214
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219
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231
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233
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234
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237
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239
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240
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259
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261
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263
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271
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283
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284
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285
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286
+ "f": 0.5650759219
 
 
 
 
 
287
  },
288
  "NORP": {
289
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290
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291
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303
  "MONEY": {
304
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305
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306
+ "f": 0.8695652174
 
 
 
 
 
307
  },
308
  "EVENT": {
309
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attribute_ruler/patterns CHANGED
Binary files a/attribute_ruler/patterns and b/attribute_ruler/patterns differ
config.cfg CHANGED
@@ -135,8 +135,8 @@ factory = "tok2vec"
135
  [components.tok2vec.model.embed]
136
  @architectures = "spacy.MultiHashEmbed.v2"
137
  width = ${components.tok2vec.model.encode:width}
138
- attrs = ["NORM","PREFIX","SUFFIX","SHAPE"]
139
- rows = [5000,1000,2500,2500]
140
  include_static_vectors = true
141
 
142
  [components.tok2vec.model.encode]
@@ -178,6 +178,7 @@ eval_frequency = 1000
178
  frozen_components = []
179
  before_to_disk = null
180
  annotating_components = []
 
181
 
182
  [training.batcher]
183
  @batchers = "spacy.batch_by_words.v1"
135
  [components.tok2vec.model.embed]
136
  @architectures = "spacy.MultiHashEmbed.v2"
137
  width = ${components.tok2vec.model.encode:width}
138
+ attrs = ["NORM","PREFIX","SUFFIX","SHAPE","IS_SPACE"]
139
+ rows = [5000,1000,2500,2500,50]
140
  include_static_vectors = true
141
 
142
  [components.tok2vec.model.encode]
178
  frozen_components = []
179
  before_to_disk = null
180
  annotating_components = []
181
+ before_update = null
182
 
183
  [training.batcher]
184
  @batchers = "spacy.batch_by_words.v1"
meta.json CHANGED
@@ -1,14 +1,14 @@
1
  {
2
  "lang":"zh",
3
  "name":"core_web_lg",
4
- "version":"3.4.0",
5
  "description":"Chinese pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attribute_ruler.",
6
  "author":"Explosion",
7
  "email":"contact@explosion.ai",
8
  "url":"https://explosion.ai",
9
  "license":"MIT",
10
- "spacy_version":">=3.4.0,<3.5.0",
11
- "spacy_git_version":"dd038b536",
12
  "vectors":{
13
  "width":300,
14
  "vectors":500000,
@@ -148,211 +148,211 @@
148
  "senter"
149
  ],
150
  "performance":{
151
- "token_acc":0.9788303388,
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  "token_p":0.9458325855,
153
  "token_r":0.9136060443,
154
  "token_f":0.9294400505,
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156
- "sents_p":0.7773839064,
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- "sents_f":0.7530260108,
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- "dep_las":0.6564620448,
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  "dep":{
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177
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- "p":0.8270676692,
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- "name":{
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  },
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- "p":0.880195599,
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  },
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  "nmod:poss":{
353
- "p":0.7058823529,
354
- "r":0.4444444444,
355
- "f":0.5454545455
 
 
 
 
 
356
  },
357
  "nsubj:xsubj":{
358
  "p":0.0,
@@ -360,19 +360,19 @@
360
  "f":0.0
361
  },
362
  "nmod:range":{
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- "p":0.7131782946,
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- "r":0.6174496644,
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  },
367
  "parataxis:prnmod":{
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- "p":0.4102564103,
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371
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  "erased":{
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  "p":0.0,
@@ -380,89 +380,89 @@
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  "f":0.0
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  "etc":{
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- "p":0.9047619048,
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- "r":0.9047619048,
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386
  }
387
  },
388
- "ents_p":0.7335270192,
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- "ents_r":0.6936263736,
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- "ents_f":0.7130189212,
391
  "ents_per_type":{
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- "p":0.7721755369,
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  },
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  },
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  "FAC":{
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- "p":0.4268292683,
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  },
412
  "ORG":{
413
- "p":0.6924939467,
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- "r":0.6529680365,
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- "f":0.6721504113
 
 
 
 
 
416
  },
417
  "QUANTITY":{
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- "p":0.7980769231,
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- "r":0.6148148148,
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- "r":0.5322580645,
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  "MONEY":{
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466
  },
467
  "PRODUCT":{
468
  "p":0.0,
@@ -470,17 +470,17 @@
470
  "f":0.0
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  },
472
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- "speed":7526.7870509872
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  "sources":[
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  {
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