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spacy
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zh_core_web_trf

Token Classification
spaCy
Chinese
Eval Results
Model card Files Community
EC2 Default User commited on
Commit
3270d9e
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1 Parent(s): 5c423e0

Update spaCy pipeline

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Files changed (13) hide show
  1. README.md +28 -34
  2. accuracy.json +194 -194
  3. attribute_ruler/patterns +0 -0
  4. config.cfg +3 -1
  5. meta.json +197 -197
  6. ner/model +1 -1
  7. parser/model +1 -1
  8. parser/moves +1 -1
  9. tagger/cfg +1 -0
  10. tagger/model +2 -2
  11. transformer/model +2 -2
  12. vocab/strings.json +2 -2
  13. zh_core_web_trf-any-py3-none-any.whl +2 -2
README.md CHANGED
@@ -14,47 +14,41 @@ model-index:
14
  metrics:
15
  - name: NER Precision
16
  type: precision
17
- value: 0.746365105
18
  - name: NER Recall
19
  type: recall
20
- value: 0.7615384615
21
  - name: NER F Score
22
  type: f_score
23
- value: 0.7538754419
24
  - task:
25
- name: POS
26
  type: token-classification
27
  metrics:
28
- - name: POS Accuracy
29
  type: accuracy
30
- value: 0.9247167985
31
  - task:
32
- name: SENTER
33
  type: token-classification
34
  metrics:
35
- - name: SENTER Precision
36
- type: precision
37
- value: 0.7110739502
38
- - name: SENTER Recall
39
- type: recall
40
- value: 0.6370900616
41
- - name: SENTER F Score
42
  type: f_score
43
- value: 0.67205198
44
  - task:
45
- name: UNLABELED_DEPENDENCIES
46
  type: token-classification
47
  metrics:
48
- - name: Unlabeled Dependencies Accuracy
49
- type: accuracy
50
- value: 0.7683558244
51
  - task:
52
- name: LABELED_DEPENDENCIES
53
  type: token-classification
54
  metrics:
55
- - name: Labeled Dependencies Accuracy
56
- type: accuracy
57
- value: 0.7683558244
58
  ---
59
  ### Details: https://spacy.io/models/zh#zh_core_web_trf
60
 
@@ -63,8 +57,8 @@ Chinese transformer pipeline (bert-base-chinese). Components: transformer, tagge
63
  | Feature | Description |
64
  | --- | --- |
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  | **Name** | `zh_core_web_trf` |
66
- | **Version** | `3.2.0` |
67
- | **spaCy** | `>=3.2.0,<3.3.0` |
68
  | **Default Pipeline** | `transformer`, `tagger`, `parser`, `attribute_ruler`, `ner` |
69
  | **Components** | `transformer`, `tagger`, `parser`, `attribute_ruler`, `ner` |
70
  | **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
@@ -94,12 +88,12 @@ Chinese transformer pipeline (bert-base-chinese). Components: transformer, tagge
94
  | `TOKEN_P` | 94.58 |
95
  | `TOKEN_R` | 91.36 |
96
  | `TOKEN_F` | 92.94 |
97
- | `TAG_ACC` | 92.47 |
98
- | `SENTS_P` | 71.11 |
99
- | `SENTS_R` | 63.71 |
100
- | `SENTS_F` | 67.21 |
101
- | `DEP_UAS` | 76.84 |
102
- | `DEP_LAS` | 73.07 |
103
- | `ENTS_P` | 74.64 |
104
- | `ENTS_R` | 76.15 |
105
- | `ENTS_F` | 75.39 |
14
  metrics:
15
  - name: NER Precision
16
  type: precision
17
+ value: 0.6942860106
18
  - name: NER Recall
19
  type: recall
20
+ value: 0.7357142857
21
  - name: NER F Score
22
  type: f_score
23
+ value: 0.7144000427
24
  - task:
25
+ name: TAG
26
  type: token-classification
27
  metrics:
28
+ - name: TAG (XPOS) Accuracy
29
  type: accuracy
30
+ value: 0.9235746441
31
  - task:
32
+ name: UNLABELED_DEPENDENCIES
33
  type: token-classification
34
  metrics:
35
+ - name: Unlabeled Attachment Score (UAS)
 
 
 
 
 
 
36
  type: f_score
37
+ value: 0.7606435996
38
  - task:
39
+ name: LABELED_DEPENDENCIES
40
  type: token-classification
41
  metrics:
42
+ - name: Labeled Attachment Score (LAS)
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+ type: f_score
44
+ value: 0.7210395809
45
  - task:
46
+ name: SENTS
47
  type: token-classification
48
  metrics:
49
+ - name: Sentences F-Score
50
+ type: f_score
51
+ value: 0.6541033435
52
  ---
53
  ### Details: https://spacy.io/models/zh#zh_core_web_trf
54
 
57
  | Feature | Description |
58
  | --- | --- |
59
  | **Name** | `zh_core_web_trf` |
60
+ | **Version** | `3.3.0` |
61
+ | **spaCy** | `>=3.3.0.dev0,<3.4.0` |
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` | 92.36 |
92
+ | `SENTS_P` | 68.37 |
93
+ | `SENTS_R` | 62.69 |
94
+ | `SENTS_F` | 65.41 |
95
+ | `DEP_UAS` | 76.06 |
96
+ | `DEP_LAS` | 72.10 |
97
+ | `ENTS_P` | 69.43 |
98
+ | `ENTS_R` | 73.57 |
99
+ | `ENTS_F` | 71.44 |
accuracy.json CHANGED
@@ -3,227 +3,227 @@
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@@ -231,105 +231,105 @@
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229
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233
  "etc": {
234
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  }
238
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263
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264
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  },
268
  "ORG": {
269
+ "p": 0.749412686,
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+ "r": 0.7283105023,
271
+ "f": 0.7387109224
 
 
 
 
 
272
  },
273
  "LOC": {
274
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275
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  "r": 0.8296296296,
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333
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334
+ "speed": 2802.8382367834
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attribute_ruler/patterns CHANGED
Binary files a/attribute_ruler/patterns and b/attribute_ruler/patterns differ
config.cfg CHANGED
@@ -75,12 +75,14 @@ pooling = {"@layers":"reduce_mean.v1"}
75
 
76
  [components.tagger]
77
  factory = "tagger"
 
78
  overwrite = false
79
  scorer = {"@scorers":"spacy.tagger_scorer.v1"}
80
 
81
  [components.tagger.model]
82
- @architectures = "spacy.Tagger.v1"
83
  nO = null
 
84
 
85
  [components.tagger.model.tok2vec]
86
  @architectures = "spacy-transformers.TransformerListener.v1"
75
 
76
  [components.tagger]
77
  factory = "tagger"
78
+ neg_prefix = "!"
79
  overwrite = false
80
  scorer = {"@scorers":"spacy.tagger_scorer.v1"}
81
 
82
  [components.tagger.model]
83
+ @architectures = "spacy.Tagger.v2"
84
  nO = null
85
+ normalize = false
86
 
87
  [components.tagger.model.tok2vec]
88
  @architectures = "spacy-transformers.TransformerListener.v1"
meta.json CHANGED
@@ -1,14 +1,14 @@
1
  {
2
  "lang":"zh",
3
  "name":"core_web_trf",
4
- "version":"3.2.0",
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":">=3.2.0,<3.3.0",
11
- "spacy_git_version":"bb26550e2",
12
  "vectors":{
13
  "width":0,
14
  "vectors":0,
@@ -150,227 +150,227 @@
150
  "token_p":0.9458325855,
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  "token_r":0.9136060443,
152
  "token_f":0.9294400505,
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- "tag_acc":0.9247167985,
154
- "sents_p":0.7110739502,
155
- "sents_r":0.6370900616,
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157
- "dep_uas":0.7683558244,
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- "dep_las":0.7307018312,
159
  "dep_las_per_type":{
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  "dep":{
161
- "p":0.5656512605,
162
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165
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166
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170
  "nmod:tmod":{
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175
  "nummod":{
176
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180
  "mark:clf":{
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183
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185
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190
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196
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206
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290
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300
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315
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320
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330
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333
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  },
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  },
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- "p":0.9443099274,
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351
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356
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  "parataxis:prnmod":{
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376
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@@ -378,107 +378,107 @@
378
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380
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381
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384
  }
385
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386
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387
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389
  "ents_per_type":{
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  },
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396
- "p":0.7898259705,
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- "r":0.8651026393,
398
- "f":0.8257522743
 
 
 
 
 
399
  },
400
  "ORDINAL":{
401
- "p":0.8720930233,
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- "r":0.7894736842,
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405
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410
  "ORG":{
411
- "p":0.749034749,
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- },
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- "NORP":{
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  },
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- "p":0.8638871858,
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- "CARDINAL":{
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440
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475
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480
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481
- "speed":4253.4050367787
482
  },
483
  "sources":[
484
  {
1
  {
2
  "lang":"zh",
3
  "name":"core_web_trf",
4
+ "version":"3.3.0",
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",
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