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zh_core_web_md

Token Classification
spaCy
Chinese
Eval Results (legacy)
Model card Files
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EC2 Default User commited on
Commit
c653306
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1 Parent(s): 39967f2

Update spaCy pipeline

Browse files
Files changed (14) hide show
  1. README.md +29 -36
  2. accuracy.json +197 -197
  3. attribute_ruler/patterns +0 -0
  4. config.cfg +8 -5
  5. meta.json +200 -204
  6. ner/model +2 -2
  7. parser/model +1 -1
  8. parser/moves +1 -1
  9. senter/model +2 -2
  10. tagger/cfg +1 -0
  11. tagger/model +2 -2
  12. tok2vec/model +2 -2
  13. vocab/strings.json +2 -2
  14. zh_core_web_md-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: 0.7220589964
18
  - name: NER Recall
19
  type: recall
20
- value: 0.6751648352
21
  - name: NER F Score
22
  type: f_score
23
- value: 0.6978249759
24
  - task:
25
- name: POS
26
  type: token-classification
27
  metrics:
28
- - name: POS Accuracy
29
  type: accuracy
30
- value: 0.9004973002
31
  - task:
32
- name: SENTER
33
  type: token-classification
34
  metrics:
35
- - name: SENTER Precision
36
- type: precision
37
- value: 0.7859447831
38
- - name: SENTER Recall
39
- type: recall
40
- value: 0.7298152156
41
- - name: SENTER F Score
42
  type: f_score
43
- value: 0.7568407423
44
  - task:
45
- name: UNLABELED_DEPENDENCIES
46
  type: token-classification
47
  metrics:
48
- - name: Unlabeled Dependencies Accuracy
49
- type: accuracy
50
- value: 0.7076909586
51
  - task:
52
- name: LABELED_DEPENDENCIES
53
  type: token-classification
54
  metrics:
55
- - name: Labeled Dependencies Accuracy
56
- type: accuracy
57
- value: 0.7076909586
58
  ---
59
  ### Details: https://spacy.io/models/zh#zh_core_web_md
60
 
@@ -63,8 +57,8 @@ Chinese pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter,
63
  | Feature | Description |
64
  | --- | --- |
65
  | **Name** | `zh_core_web_md` |
66
- | **Version** | `3.2.0` |
67
- | **spaCy** | `>=3.2.0,<3.3.0` |
68
  | **Default Pipeline** | `tok2vec`, `tagger`, `parser`, `attribute_ruler`, `ner` |
69
  | **Components** | `tok2vec`, `tagger`, `parser`, `senter`, `attribute_ruler`, `ner` |
70
  | **Vectors** | 500000 keys, 20000 unique vectors (300 dimensions) |
@@ -76,13 +70,12 @@ Chinese pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter,
76
 
77
  <details>
78
 
79
- <summary>View label scheme (101 labels for 4 components)</summary>
80
 
81
  | Component | Labels |
82
  | --- | --- |
83
  | **`tagger`** | `AD`, `AS`, `BA`, `CC`, `CD`, `CS`, `DEC`, `DEG`, `DER`, `DEV`, `DT`, `ETC`, `FW`, `IJ`, `INF`, `JJ`, `LB`, `LC`, `M`, `MSP`, `NN`, `NR`, `NT`, `OD`, `ON`, `P`, `PN`, `PU`, `SB`, `SP`, `URL`, `VA`, `VC`, `VE`, `VV`, `X` |
84
  | **`parser`** | `ROOT`, `acl`, `advcl:loc`, `advmod`, `advmod:dvp`, `advmod:loc`, `advmod:rcomp`, `amod`, `amod:ordmod`, `appos`, `aux:asp`, `aux:ba`, `aux:modal`, `aux:prtmod`, `auxpass`, `case`, `cc`, `ccomp`, `compound:nn`, `compound:vc`, `conj`, `cop`, `dep`, `det`, `discourse`, `dobj`, `etc`, `mark`, `mark:clf`, `name`, `neg`, `nmod`, `nmod:assmod`, `nmod:poss`, `nmod:prep`, `nmod:range`, `nmod:tmod`, `nmod:topic`, `nsubj`, `nsubj:xsubj`, `nsubjpass`, `nummod`, `parataxis:prnmod`, `punct`, `xcomp` |
85
- | **`senter`** | `I`, `S` |
86
  | **`ner`** | `CARDINAL`, `DATE`, `EVENT`, `FAC`, `GPE`, `LANGUAGE`, `LAW`, `LOC`, `MONEY`, `NORP`, `ORDINAL`, `ORG`, `PERCENT`, `PERSON`, `PRODUCT`, `QUANTITY`, `TIME`, `WORK_OF_ART` |
87
 
88
  </details>
@@ -95,12 +88,12 @@ Chinese pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter,
95
  | `TOKEN_P` | 94.58 |
96
  | `TOKEN_R` | 91.36 |
97
  | `TOKEN_F` | 92.94 |
98
- | `TAG_ACC` | 90.05 |
99
- | `SENTS_P` | 78.59 |
100
- | `SENTS_R` | 72.98 |
101
- | `SENTS_F` | 75.68 |
102
- | `DEP_UAS` | 70.77 |
103
- | `DEP_LAS` | 65.52 |
104
- | `ENTS_P` | 72.21 |
105
- | `ENTS_R` | 67.52 |
106
- | `ENTS_F` | 69.78 |
 
14
  metrics:
15
  - name: NER Precision
16
  type: precision
17
+ value: 0.7214151828
18
  - name: NER Recall
19
  type: recall
20
+ value: 0.6767032967
21
  - name: NER F Score
22
  type: f_score
23
+ value: 0.6983442958
24
  - task:
25
+ name: TAG
26
  type: token-classification
27
  metrics:
28
+ - name: TAG (XPOS) Accuracy
29
  type: accuracy
30
+ value: 0.8993969506
31
  - task:
32
+ name: UNLABELED_DEPENDENCIES
33
  type: token-classification
34
  metrics:
35
+ - name: Unlabeled Attachment Score (UAS)
 
 
 
 
 
 
36
  type: f_score
37
+ value: 0.7039979835
38
  - task:
39
+ name: LABELED_DEPENDENCIES
40
  type: token-classification
41
  metrics:
42
+ - name: Labeled Attachment Score (LAS)
43
+ type: f_score
44
+ value: 0.6522146335
45
  - task:
46
+ name: SENTS
47
  type: token-classification
48
  metrics:
49
+ - name: Sentences F-Score
50
+ type: f_score
51
+ value: 0.7534931861
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** | `3.3.0` |
61
+ | **spaCy** | `>=3.3.0.dev0,<3.4.0` |
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) |
 
70
 
71
  <details>
72
 
73
+ <summary>View label scheme (99 labels for 3 components)</summary>
74
 
75
  | Component | Labels |
76
  | --- | --- |
77
  | **`tagger`** | `AD`, `AS`, `BA`, `CC`, `CD`, `CS`, `DEC`, `DEG`, `DER`, `DEV`, `DT`, `ETC`, `FW`, `IJ`, `INF`, `JJ`, `LB`, `LC`, `M`, `MSP`, `NN`, `NR`, `NT`, `OD`, `ON`, `P`, `PN`, `PU`, `SB`, `SP`, `URL`, `VA`, `VC`, `VE`, `VV`, `X` |
78
  | **`parser`** | `ROOT`, `acl`, `advcl:loc`, `advmod`, `advmod:dvp`, `advmod:loc`, `advmod:rcomp`, `amod`, `amod:ordmod`, `appos`, `aux:asp`, `aux:ba`, `aux:modal`, `aux:prtmod`, `auxpass`, `case`, `cc`, `ccomp`, `compound:nn`, `compound:vc`, `conj`, `cop`, `dep`, `det`, `discourse`, `dobj`, `etc`, `mark`, `mark:clf`, `name`, `neg`, `nmod`, `nmod:assmod`, `nmod:poss`, `nmod:prep`, `nmod:range`, `nmod:tmod`, `nmod:topic`, `nsubj`, `nsubj:xsubj`, `nsubjpass`, `nummod`, `parataxis:prnmod`, `punct`, `xcomp` |
 
79
  | **`ner`** | `CARDINAL`, `DATE`, `EVENT`, `FAC`, `GPE`, `LANGUAGE`, `LAW`, `LOC`, `MONEY`, `NORP`, `ORDINAL`, `ORG`, `PERCENT`, `PERSON`, `PRODUCT`, `QUANTITY`, `TIME`, `WORK_OF_ART` |
80
 
81
  </details>
 
88
  | `TOKEN_P` | 94.58 |
89
  | `TOKEN_R` | 91.36 |
90
  | `TOKEN_F` | 92.94 |
91
+ | `TAG_ACC` | 89.94 |
92
+ | `SENTS_P` | 78.18 |
93
+ | `SENTS_R` | 72.72 |
94
+ | `SENTS_F` | 75.35 |
95
+ | `DEP_UAS` | 70.40 |
96
+ | `DEP_LAS` | 65.22 |
97
+ | `ENTS_P` | 72.14 |
98
+ | `ENTS_R` | 67.67 |
99
+ | `ENTS_F` | 69.83 |
accuracy.json CHANGED
@@ -3,212 +3,207 @@
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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": 0.9004973002,
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- "sents_p": 0.7859447831,
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- "sents_r": 0.7298152156,
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- "sents_f": 0.7568407423,
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- "dep_uas": 0.7076909586,
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- "dep_las": 0.6551856356,
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  "dep_las_per_type": {
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  "dep": {
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- "p": 0.4941927991,
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- "r": 0.3439426089,
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- "f": 0.4056002383
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  },
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  "case": {
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  },
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  "nmod:tmod": {
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  "nummod": {
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- "p": 0.8324715615,
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- "f": 0.652350081
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  },
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  "mark:clf": {
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- "p": 0.923958962,
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  },
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- "f": 0.8864864865
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  },
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  "nsubj": {
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- "p": 0.7838943894,
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- "r": 0.7293944233,
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- "f": 0.7556630186
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  },
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  "acl": {
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- "p": 0.7085714286,
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- "r": 0.5501941209,
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- "f": 0.6194192944
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  },
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  "advmod": {
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- "p": 0.8221938776,
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- "r": 0.7306733167,
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- "f": 0.7737366463
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  },
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  "mark": {
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- "p": 0.7447306792,
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- "r": 0.6967572305,
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- "f": 0.7199456645
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  },
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  "xcomp": {
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- "p": 0.7822878229,
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- "f": 0.7335640138
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- "p": 0.7571008815,
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- "f": 0.7390057361
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  "det": {
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  },
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- "p": 0.7567811935,
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- "f": 0.7036569987
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  },
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  "nmod:prep": {
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- "p": 0.9058823529,
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- "r": 0.825,
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- "f": 0.8635514019
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  "compound:nn": {
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- "p": 0.802248996,
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  "ccomp": {
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  "advmod:rcomp": {
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- "p": 0.8196202532,
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- "r": 0.7174515235,
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  },
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  "nmod:topic": {
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  "cop": {
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- "p": 0.7555739059,
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- "r": 0.5888030888,
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- "f": 0.6618444846
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  },
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  "discourse": {
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- "p": 0.5577797998,
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  "neg": {
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  "advmod:loc": {
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- "p": 0.6130268199,
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  },
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  "aux:asp": {
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- "p": 0.9095435685,
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  },
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  "conj": {
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  },
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  "nsubjpass": {
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- "p": 0.8292682927,
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- "r": 0.68,
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- "f": 0.7472527473
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  },
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  "compound:vc": {
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- "p": 0.4486486486,
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  },
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  "advcl:loc": {
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- "p": 0.5945945946,
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  "r": 0.4714285714,
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- "f": 0.5258964143
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  },
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  "cc": {
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- "p": 0.7013108614,
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- "r": 0.6645962733,
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  "advmod:dvp": {
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- "p": 0.8045112782,
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  "r": 0.6645962733,
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  },
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  "appos": {
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- "p": 0.8658536585,
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- "r": 0.816091954,
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- "f": 0.8402366864
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- },
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- "name": {
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- "p": 0.5625,
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- "r": 0.4666666667,
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- "f": 0.5101214575
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- },
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- "parataxis:prnmod": {
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- "p": 0.5,
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- "r": 0.1278195489,
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- "f": 0.2035928144
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  },
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  "nmod:poss": {
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- "p": 0.6352941176,
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- "r": 0.4,
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- "f": 0.4909090909
 
 
 
 
 
212
  },
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  "nsubj:xsubj": {
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  "p": 0.0,
@@ -216,14 +211,19 @@
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  "f": 0.0
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  },
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  "nmod:range": {
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- "p": 0.7346153846,
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- "r": 0.6409395973,
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- "f": 0.6845878136
 
 
 
 
 
222
  },
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  "amod:ordmod": {
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- "p": 0.6181818182,
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- "r": 0.53125,
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- "f": 0.5714285714
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  },
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  "erased": {
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  "p": 0.0,
@@ -231,94 +231,94 @@
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  "f": 0.0
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  },
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  "etc": {
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- "p": 0.9268292683,
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- "r": 0.9047619048,
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- "f": 0.9156626506
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  }
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  },
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- "ents_p": 0.7220589964,
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- "ents_r": 0.6751648352,
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- "ents_f": 0.6978249759,
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  "ents_per_type": {
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  "DATE": {
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- "p": 0.758780037,
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- "f": 0.7852702056
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  },
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  "GPE": {
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- "p": 0.7517889088,
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- "r": 0.8216031281,
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- "f": 0.7851471275
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  },
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  "ORDINAL": {
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- "p": 0.8720930233,
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- "r": 0.7894736842,
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- "f": 0.8287292818
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  },
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  "FAC": {
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- "p": 0.5076923077,
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- "r": 0.3548387097,
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- "f": 0.417721519
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- },
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- "PERSON": {
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- "p": 0.7917511832,
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- "r": 0.7545103093,
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- "f": 0.7726822831
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  },
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  "ORG": {
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- "p": 0.6896831844,
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- "r": 0.6461187215,
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- "f": 0.6671905697
 
 
 
 
 
272
  },
273
  "QUANTITY": {
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- "p": 0.7706422018,
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- "r": 0.6222222222,
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- "f": 0.6885245902
 
 
 
 
 
277
  },
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  "CARDINAL": {
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- "p": 0.6181818182,
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- "r": 0.5141129032,
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- "f": 0.5613648872
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  },
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- "LOC": {
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- "p": 0.5247148289,
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- "r": 0.3709677419,
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- "f": 0.4346456693
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  },
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  "NORP": {
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- "p": 0.6646153846,
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- "r": 0.4537815126,
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- "f": 0.5393258427
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  },
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  "WORK_OF_ART": {
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- "p": 0.5733333333,
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- "r": 0.2866666667,
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- "f": 0.3822222222
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- },
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- "TIME": {
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- "p": 0.7209302326,
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- "r": 0.7524271845,
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- "f": 0.7363420428
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- },
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- "PRODUCT": {
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- "p": 0.2,
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- "r": 0.0612244898,
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- "f": 0.09375
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  },
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  "MONEY": {
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- "p": 0.9230769231,
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- "r": 0.8,
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- "f": 0.8571428571
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  },
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  "PERCENT": {
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- "p": 0.7613636364,
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- "r": 0.8072289157,
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- "f": 0.783625731
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  },
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  "EVENT": {
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- "p": 0.5688073394,
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- "r": 0.4558823529,
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- "f": 0.506122449
 
 
 
 
 
322
  },
323
  "LAW": {
324
  "p": 0.4814814815,
@@ -326,10 +326,10 @@
326
  "f": 0.2988505747
327
  },
328
  "LANGUAGE": {
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- "p": 0.6363636364,
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  "r": 0.7777777778,
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- "f": 0.7
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  }
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  },
334
- "speed": 7391.1713592242
335
  }
 
3
  "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": 0.8993969506,
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+ "sents_p": 0.7818149275,
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+ "sents_r": 0.7271516564,
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+ "sents_f": 0.7534931861,
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+ "dep_uas": 0.7039979835,
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+ "dep_las": 0.6522146335,
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  "dep_las_per_type": {
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  "dep": {
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+ "p": 0.479214123,
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+ "r": 0.3401030615,
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+ "f": 0.3978488269
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  },
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  "case": {
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+ "p": 0.81099213,
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+ "r": 0.7621241513,
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+ "f": 0.7857991124
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  },
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  "nmod:tmod": {
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108
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115
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116
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119
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124
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128
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129
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132
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134
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139
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140
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141
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142
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143
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144
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146
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148
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149
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150
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151
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152
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153
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154
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159
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161
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189
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  "appos": {
194
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195
+ "r": 0.8068965517,
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197
  },
198
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231
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234
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259
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262
  },
263
  "ORG": {
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283
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293
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  "WORK_OF_ART": {
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+ "f": 0.4502164502
 
 
 
 
 
 
 
 
 
 
302
  },
303
  "MONEY": {
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+ "p": 0.9565217391,
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313
  "EVENT": {
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318
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  "LAW": {
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326
  "f": 0.2988505747
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328
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329
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331
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332
  }
333
  },
334
+ "speed": 7209.1977233502
335
  }
attribute_ruler/patterns CHANGED
Binary files a/attribute_ruler/patterns and b/attribute_ruler/patterns differ
 
config.cfg CHANGED
@@ -51,7 +51,7 @@ nO = null
51
  @architectures = "spacy.MultiHashEmbed.v2"
52
  width = 96
53
  attrs = ["NORM","PREFIX","SUFFIX","SHAPE"]
54
- rows = [5000,2500,2500,2500]
55
  include_static_vectors = true
56
 
57
  [components.ner.model.tok2vec.encode]
@@ -89,8 +89,9 @@ overwrite = false
89
  scorer = {"@scorers":"spacy.senter_scorer.v1"}
90
 
91
  [components.senter.model]
92
- @architectures = "spacy.Tagger.v1"
93
  nO = null
 
94
 
95
  [components.senter.model.tok2vec]
96
  @architectures = "spacy.Tok2Vec.v2"
@@ -111,12 +112,14 @@ maxout_pieces = 2
111
 
112
  [components.tagger]
113
  factory = "tagger"
 
114
  overwrite = false
115
  scorer = {"@scorers":"spacy.tagger_scorer.v1"}
116
 
117
  [components.tagger.model]
118
- @architectures = "spacy.Tagger.v1"
119
  nO = null
 
120
 
121
  [components.tagger.model.tok2vec]
122
  @architectures = "spacy.Tok2VecListener.v1"
@@ -133,7 +136,7 @@ factory = "tok2vec"
133
  @architectures = "spacy.MultiHashEmbed.v2"
134
  width = ${components.tok2vec.model.encode:width}
135
  attrs = ["NORM","PREFIX","SUFFIX","SHAPE"]
136
- rows = [5000,2500,2500,2500]
137
  include_static_vectors = true
138
 
139
  [components.tok2vec.model.encode]
@@ -170,7 +173,7 @@ dropout = 0.1
170
  accumulate_gradient = 1
171
  patience = 5000
172
  max_epochs = 0
173
- max_steps = 0
174
  eval_frequency = 1000
175
  frozen_components = []
176
  before_to_disk = null
 
51
  @architectures = "spacy.MultiHashEmbed.v2"
52
  width = 96
53
  attrs = ["NORM","PREFIX","SUFFIX","SHAPE"]
54
+ rows = [5000,1000,2500,2500]
55
  include_static_vectors = true
56
 
57
  [components.ner.model.tok2vec.encode]
 
89
  scorer = {"@scorers":"spacy.senter_scorer.v1"}
90
 
91
  [components.senter.model]
92
+ @architectures = "spacy.Tagger.v2"
93
  nO = null
94
+ normalize = false
95
 
96
  [components.senter.model.tok2vec]
97
  @architectures = "spacy.Tok2Vec.v2"
 
112
 
113
  [components.tagger]
114
  factory = "tagger"
115
+ neg_prefix = "!"
116
  overwrite = false
117
  scorer = {"@scorers":"spacy.tagger_scorer.v1"}
118
 
119
  [components.tagger.model]
120
+ @architectures = "spacy.Tagger.v2"
121
  nO = null
122
+ normalize = false
123
 
124
  [components.tagger.model.tok2vec]
125
  @architectures = "spacy.Tok2VecListener.v1"
 
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]
 
173
  accumulate_gradient = 1
174
  patience = 5000
175
  max_epochs = 0
176
+ max_steps = 100000
177
  eval_frequency = 1000
178
  frozen_components = []
179
  before_to_disk = null
meta.json CHANGED
@@ -1,14 +1,14 @@
1
  {
2
  "lang":"zh",
3
  "name":"core_web_md",
4
- "version":"3.2.0",
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":">=3.2.0,<3.3.0",
11
- "spacy_git_version":"bb26550e2",
12
  "vectors":{
13
  "width":300,
14
  "vectors":20000,
@@ -104,10 +104,6 @@
104
  "punct",
105
  "xcomp"
106
  ],
107
- "senter":[
108
- "I",
109
- "S"
110
- ],
111
  "attribute_ruler":[
112
 
113
  ],
@@ -155,212 +151,207 @@
155
  "token_p":0.9458325855,
156
  "token_r":0.9136060443,
157
  "token_f":0.9294400505,
158
- "tag_acc":0.9004973002,
159
- "sents_p":0.7859447831,
160
- "sents_r":0.7298152156,
161
- "sents_f":0.7568407423,
162
- "dep_uas":0.7076909586,
163
- "dep_las":0.6551856356,
164
  "dep_las_per_type":{
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  "dep":{
166
- "p":0.4941927991,
167
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168
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170
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171
- "p":0.815348957,
172
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173
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174
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175
  "nmod:tmod":{
176
- "p":0.7291941876,
177
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178
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179
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180
  "nummod":{
181
- "p":0.8324715615,
182
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183
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184
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185
  "mark:clf":{
186
- "p":0.923958962,
187
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188
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190
  "auxpass":{
191
- "p":0.8864864865,
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193
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194
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195
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196
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198
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200
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201
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203
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204
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205
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206
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207
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210
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211
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212
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213
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215
  "xcomp":{
216
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217
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218
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219
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220
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221
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225
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226
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227
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230
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231
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232
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233
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235
  "nmod:prep":{
236
- "p":0.6989096025,
237
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239
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240
  "root":{
241
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242
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243
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245
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- "p":0.9058823529,
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248
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- "p":0.7339595888,
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  "dobj":{
256
- "p":0.802248996,
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259
  },
260
  "ccomp":{
261
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263
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266
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  },
270
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271
- "p":0.3596059113,
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276
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281
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286
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