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zh_core_web_md

Token Classification
spaCy
Chinese
Eval Results
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adrianeboyd commited on
Commit
d156ba1
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1 Parent(s): caef619

Update spaCy pipeline

Browse files
Files changed (11) hide show
  1. README.md +19 -19
  2. accuracy.json +199 -199
  3. attribute_ruler/patterns +0 -0
  4. config.cfg +3 -2
  5. meta.json +202 -202
  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_md-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: 0.7221830158
18
  - name: NER Recall
19
  type: recall
20
- value: 0.6747252747
21
  - name: NER F Score
22
  type: f_score
23
- value: 0.6976479945
24
  - task:
25
  name: TAG
26
  type: token-classification
27
  metrics:
28
  - name: TAG (XPOS) Accuracy
29
  type: accuracy
30
- value: 0.9008590446
31
  - task:
32
  name: UNLABELED_DEPENDENCIES
33
  type: token-classification
34
  metrics:
35
  - name: Unlabeled Attachment Score (UAS)
36
  type: f_score
37
- value: 0.7039779048
38
  - task:
39
  name: LABELED_DEPENDENCIES
40
  type: token-classification
41
  metrics:
42
  - name: Labeled Attachment Score (LAS)
43
  type: f_score
44
- value: 0.6517336371
45
  - task:
46
  name: SENTS
47
  type: token-classification
48
  metrics:
49
  - name: Sentences F-Score
50
  type: f_score
51
- value: 0.7535180869
52
  ---
53
  ### Details: https://spacy.io/models/zh#zh_core_web_md
54
 
@@ -57,8 +57,8 @@ Chinese pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter,
57
  | Feature | Description |
58
  | --- | --- |
59
  | **Name** | `zh_core_web_md` |
60
- | **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, 20000 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` | 90.09 |
92
- | `SENTS_P` | 78.26 |
93
- | `SENTS_R` | 72.65 |
94
- | `SENTS_F` | 75.35 |
95
- | `DEP_UAS` | 70.40 |
96
- | `DEP_LAS` | 65.17 |
97
- | `ENTS_P` | 72.22 |
98
- | `ENTS_R` | 67.47 |
99
- | `ENTS_F` | 69.76 |
 
14
  metrics:
15
  - name: NER Precision
16
  type: precision
17
+ value: 0.7188227082
18
  - name: NER Recall
19
  type: recall
20
+ value: 0.679010989
21
  - name: NER F Score
22
  type: f_score
23
+ value: 0.6983499096
24
  - task:
25
  name: TAG
26
  type: token-classification
27
  metrics:
28
  - name: TAG (XPOS) Accuracy
29
  type: accuracy
30
+ value: 0.9003849582
31
  - task:
32
  name: UNLABELED_DEPENDENCIES
33
  type: token-classification
34
  metrics:
35
  - name: Unlabeled Attachment Score (UAS)
36
  type: f_score
37
+ value: 0.7049895344
38
  - task:
39
  name: LABELED_DEPENDENCIES
40
  type: token-classification
41
  metrics:
42
  - name: Labeled Attachment Score (LAS)
43
  type: f_score
44
+ value: 0.652241773
45
  - task:
46
  name: SENTS
47
  type: token-classification
48
  metrics:
49
  - name: Sentences F-Score
50
  type: f_score
51
+ value: 0.7572294372
52
  ---
53
  ### Details: https://spacy.io/models/zh#zh_core_web_md
54
 
 
57
  | Feature | Description |
58
  | --- | --- |
59
  | **Name** | `zh_core_web_md` |
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, 20000 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` | 90.04 |
92
+ | `SENTS_P` | 78.89 |
93
+ | `SENTS_R` | 72.80 |
94
+ | `SENTS_F` | 75.72 |
95
+ | `DEP_UAS` | 70.50 |
96
+ | `DEP_LAS` | 65.22 |
97
+ | `ENTS_P` | 71.88 |
98
+ | `ENTS_R` | 67.90 |
99
+ | `ENTS_F` | 69.83 |
accuracy.json CHANGED
@@ -1,229 +1,229 @@
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@@ -231,105 +231,105 @@
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107
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121
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124
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134
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139
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142
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143
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144
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203
  "nmod:poss": {
204
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205
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206
+ "f": 0.5454545455
 
 
 
 
 
207
  },
208
  "nsubj:xsubj": {
209
  "p": 0.0,
210
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213
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  "parataxis:prnmod": {
219
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220
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221
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223
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224
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226
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228
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229
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231
  "f": 0.0
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233
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234
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235
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237
  }
238
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239
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249
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253
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254
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258
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268
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271
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273
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278
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283
  "CARDINAL": {
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285
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286
+ "f": 0.5689839572
 
 
 
 
 
287
  },
288
  "NORP": {
289
+ "p": 0.6955128205,
290
+ "r": 0.4558823529,
291
+ "f": 0.5507614213
 
 
 
 
 
292
  },
293
  "TIME": {
294
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295
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313
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318
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319
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323
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331
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332
  }
333
  },
334
+ "speed": 7528.4602636561
335
  }
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_md",
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":"[email protected]",
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":20000,
@@ -148,231 +148,231 @@
148
  "senter"
149
  ],
150
  "performance":{
151
- "token_acc":0.9788303388,
152
  "token_p":0.9458325855,
153
  "token_r":0.9136060443,
154
  "token_f":0.9294400505,
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- "tag_acc":0.9008590446,
156
- "sents_p":0.7826398852,
157
- "sents_r":0.7264857666,
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- "sents_f":0.7535180869,
159
- "dep_uas":0.7039779048,
160
- "dep_las":0.6517336371,
161
  "dep_las_per_type":{
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  "dep":{
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- "p":0.4830434783,
164
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167
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177
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182
  "mark:clf":{
183
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184
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187
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192
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- "p":0.7779833488,
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252
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277
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279
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- "p":0.8775,
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- "p":0.6804123711,
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- "p":0.6454545455,
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- "f":0.5795918367
361
  },
362
  "nsubj:xsubj":{
363
  "p":0.0,
364
  "r":0.0,
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  "f":0.0
366
  },
 
 
 
 
 
367
  "parataxis:prnmod":{
368
- "p":0.4444444444,
369
- "r":0.1203007519,
370
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371
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  "amod:ordmod":{
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  "erased":{
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  "p":0.0,
@@ -380,107 +380,107 @@
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  "f":0.0
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382
  "etc":{
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386
  }
387
  },
388
- "ents_p":0.7221830158,
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390
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391
  "ents_per_type":{
392
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393
- "p":0.7737983035,
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397
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  },
402
  "ORDINAL":{
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- "p":0.8531073446,
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  },
407
  "FAC":{
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- "p":0.5428571429,
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- "r":0.3064516129,
410
- "f":0.3917525773
 
 
 
 
 
411
  },
412
  "ORG":{
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- "p":0.6842961758,
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- "r":0.6400304414,
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- "f":0.6614235155
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  },
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- "p":0.5247148289,
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  },
427
  "CARDINAL":{
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- "p":0.6025345622,
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- },
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- "PERSON":{
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- "MONEY":{
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  },
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  "EVENT":{
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466
  },
467
  "PRODUCT":{
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