EC2 Default User commited on
Commit
bcdcb27
1 Parent(s): 0c74655

Update spaCy pipeline

Browse files
README.md CHANGED
@@ -14,40 +14,41 @@ model-index:
14
  metrics:
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  - name: NER Precision
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  type: precision
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- value: 0.7643478261
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  - name: NER Recall
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  type: recall
20
- value: 0.7480851064
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  - name: NER F Score
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  type: f_score
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- value: 0.7561290323
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  - task:
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- name: SENTER
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  type: token-classification
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  metrics:
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- - name: SENTER Precision
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- type: precision
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- value: 0.8125
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- - name: SENTER Recall
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- type: recall
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- value: 0.6753246753
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- - name: SENTER F Score
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- type: f_score
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- value: 0.7375886525
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  - task:
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  name: UNLABELED_DEPENDENCIES
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  type: token-classification
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  metrics:
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- - name: Unlabeled Dependencies Accuracy
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- type: accuracy
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- value: 0.700686948
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  - task:
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  name: LABELED_DEPENDENCIES
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  type: token-classification
47
  metrics:
48
- - name: Labeled Dependencies Accuracy
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- type: accuracy
50
- value: 0.700686948
 
 
 
 
 
 
 
51
  ---
52
  ### Details: https://spacy.io/models/mk#mk_core_news_md
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@@ -56,8 +57,8 @@ Macedonian pipeline optimized for CPU. Components: tok2vec, morphologizer, parse
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  | Feature | Description |
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  | --- | --- |
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  | **Name** | `mk_core_news_md` |
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- | **Version** | `3.2.0` |
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- | **spaCy** | `>=3.2.0,<3.3.0` |
61
  | **Default Pipeline** | `morphologizer`, `parser`, `attribute_ruler`, `lemmatizer`, `ner` |
62
  | **Components** | `morphologizer`, `parser`, `senter`, `attribute_ruler`, `lemmatizer`, `ner` |
63
  | **Vectors** | 274587 keys, 20000 unique vectors (300 dimensions) |
@@ -69,13 +70,12 @@ Macedonian pipeline optimized for CPU. Components: tok2vec, morphologizer, parse
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70
  <details>
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- <summary>View label scheme (55 labels for 4 components)</summary>
73
 
74
  | Component | Labels |
75
  | --- | --- |
76
  | **`morphologizer`** | `POS=PROPN`, `POS=AUX`, `POS=ADJ`, `POS=NOUN`, `POS=ADP`, `POS=PUNCT`, `POS=CONJ`, `POS=NUM`, `POS=VERB`, `POS=PRON`, `POS=ADV`, `POS=SCONJ`, `POS=PART`, `POS=SYM`, `POS=X`, `_`, `POS=INTJ` |
77
  | **`parser`** | `ROOT`, `advmod`, `att`, `aux`, `cc`, `dep`, `det`, `dobj`, `iobj`, `neg`, `nsubj`, `pobj`, `poss`, `pozm`, `pozv`, `prep`, `punct`, `relcl` |
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- | **`senter`** | `I`, `S` |
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  | **`ner`** | `CARDINAL`, `DATE`, `EVENT`, `FAC`, `GPE`, `LANGUAGE`, `LAW`, `LOC`, `MONEY`, `NORP`, `ORDINAL`, `ORG`, `PERCENT`, `PERSON`, `PRODUCT`, `QUANTITY`, `TIME`, `WORK_OF_ART` |
80
 
81
  </details>
@@ -88,12 +88,12 @@ Macedonian pipeline optimized for CPU. Components: tok2vec, morphologizer, parse
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  | `TOKEN_P` | 100.00 |
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  | `TOKEN_R` | 100.00 |
90
  | `TOKEN_F` | 100.00 |
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- | `SENTS_P` | 81.25 |
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  | `SENTS_R` | 67.53 |
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- | `SENTS_F` | 73.76 |
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- | `DEP_UAS` | 70.07 |
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- | `DEP_LAS` | 53.19 |
96
- | `ENTS_P` | 76.43 |
97
- | `ENTS_R` | 74.81 |
98
- | `ENTS_F` | 75.61 |
99
- | `POS_ACC` | 93.07 |
 
14
  metrics:
15
  - name: NER Precision
16
  type: precision
17
+ value: 0.7543402778
18
  - name: NER Recall
19
  type: recall
20
+ value: 0.7395744681
21
  - name: NER F Score
22
  type: f_score
23
+ value: 0.7468844005
24
  - task:
25
+ name: POS
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  type: token-classification
27
  metrics:
28
+ - name: POS (UPOS) Accuracy
29
+ type: accuracy
30
+ value: 0.9309414621
 
 
 
 
 
 
31
  - task:
32
  name: UNLABELED_DEPENDENCIES
33
  type: token-classification
34
  metrics:
35
+ - name: Unlabeled Attachment Score (UAS)
36
+ type: f_score
37
+ value: 0.6916256158
38
  - task:
39
  name: LABELED_DEPENDENCIES
40
  type: token-classification
41
  metrics:
42
+ - name: Labeled Attachment Score (LAS)
43
+ type: f_score
44
+ value: 0.5300492611
45
+ - task:
46
+ name: SENTS
47
+ type: token-classification
48
+ metrics:
49
+ - name: Sentences F-Score
50
+ type: f_score
51
+ value: 0.7123287671
52
  ---
53
  ### Details: https://spacy.io/models/mk#mk_core_news_md
54
 
 
57
  | Feature | Description |
58
  | --- | --- |
59
  | **Name** | `mk_core_news_md` |
60
+ | **Version** | `3.3.0` |
61
+ | **spaCy** | `>=3.3.0.dev0,<3.4.0` |
62
  | **Default Pipeline** | `morphologizer`, `parser`, `attribute_ruler`, `lemmatizer`, `ner` |
63
  | **Components** | `morphologizer`, `parser`, `senter`, `attribute_ruler`, `lemmatizer`, `ner` |
64
  | **Vectors** | 274587 keys, 20000 unique vectors (300 dimensions) |
 
70
 
71
  <details>
72
 
73
+ <summary>View label scheme (53 labels for 3 components)</summary>
74
 
75
  | Component | Labels |
76
  | --- | --- |
77
  | **`morphologizer`** | `POS=PROPN`, `POS=AUX`, `POS=ADJ`, `POS=NOUN`, `POS=ADP`, `POS=PUNCT`, `POS=CONJ`, `POS=NUM`, `POS=VERB`, `POS=PRON`, `POS=ADV`, `POS=SCONJ`, `POS=PART`, `POS=SYM`, `POS=X`, `_`, `POS=INTJ` |
78
  | **`parser`** | `ROOT`, `advmod`, `att`, `aux`, `cc`, `dep`, `det`, `dobj`, `iobj`, `neg`, `nsubj`, `pobj`, `poss`, `pozm`, `pozv`, `prep`, `punct`, `relcl` |
 
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` | 100.00 |
89
  | `TOKEN_R` | 100.00 |
90
  | `TOKEN_F` | 100.00 |
91
+ | `SENTS_P` | 75.36 |
92
  | `SENTS_R` | 67.53 |
93
+ | `SENTS_F` | 71.23 |
94
+ | `DEP_UAS` | 69.16 |
95
+ | `DEP_LAS` | 53.00 |
96
+ | `ENTS_P` | 75.43 |
97
+ | `ENTS_R` | 73.96 |
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+ | `ENTS_F` | 74.69 |
99
+ | `POS_ACC` | 93.09 |
accuracy.json CHANGED
@@ -3,36 +3,36 @@
3
  "token_p": 1.0,
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  "token_r": 1.0,
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  "token_f": 1.0,
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- "sents_p": 0.8125,
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  "sents_r": 0.6753246753,
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- "sents_f": 0.7375886525,
9
- "dep_uas": 0.700686948,
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- "dep_las": 0.5318940137,
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  "dep_las_per_type": {
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  "nsubj": {
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- "p": 0.7297297297,
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- "r": 0.7105263158,
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- "f": 0.72
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  },
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  "root": {
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- "p": 0.8125,
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- "r": 0.7428571429,
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- "f": 0.776119403
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  },
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  "cc": {
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- "p": 0.8181818182,
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- "r": 0.6428571429,
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- "f": 0.72
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  },
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  "relcl": {
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- "p": 0.4074074074,
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- "r": 0.4230769231,
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- "f": 0.4150943396
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  },
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  "pozm": {
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- "p": 0.8333333333,
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- "r": 0.4545454545,
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- "f": 0.5882352941
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  },
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  "poss": {
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  "p": 0.0,
@@ -40,14 +40,14 @@
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  "f": 0.0
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  },
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  "aux": {
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- "p": 0.6111111111,
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- "r": 0.6666666667,
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- "f": 0.6376811594
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  },
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  "prep": {
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- "p": 0.6875,
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- "r": 0.7333333333,
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- "f": 0.7096774194
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  },
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  "iobj": {
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  "p": 0.0,
@@ -55,9 +55,9 @@
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  "f": 0.0
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  },
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  "pozv": {
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- "p": 0.2,
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- "r": 0.1333333333,
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- "f": 0.16
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  },
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  "quantmod": {
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  "p": 0.0,
@@ -65,9 +65,9 @@
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  "f": 0.0
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  },
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  "att": {
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- "p": 0.693877551,
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- "r": 0.6538461538,
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- "f": 0.6732673267
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  },
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  "det": {
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  "p": 0.0,
@@ -80,29 +80,29 @@
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  "f": 0.0
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  },
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  "dep": {
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- "p": 0.0,
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- "r": 0.0,
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- "f": 0.0
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  },
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  "dobj": {
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- "p": 0.40625,
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- "r": 0.4333333333,
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- "f": 0.4193548387
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  },
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  "ppdo": {
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- "p": 0.625,
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- "r": 0.3333333333,
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- "f": 0.4347826087
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  },
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  "neg": {
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- "p": 0.6363636364,
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- "r": 0.6363636364,
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- "f": 0.6363636364
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  },
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  "pobj": {
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- "p": 0.425,
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- "r": 0.53125,
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- "f": 0.4722222222
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  },
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  "mwe": {
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  "p": 0.0,
@@ -114,16 +114,16 @@
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  "r": 0.0,
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  "f": 0.0
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  },
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- "appos": {
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- "p": 0.0,
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- "f": 0.0
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- },
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  "advmod": {
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  "p": 0.3333333333,
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  "r": 0.5,
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  "f": 0.4
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  },
 
 
 
 
 
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  "advcl": {
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  "p": 0.0,
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  "r": 0.0,
@@ -160,90 +160,90 @@
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  "f": 0.0
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  }
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  },
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- "speed": 2294.6132988248,
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- "ents_p": 0.7643478261,
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- "ents_r": 0.7480851064,
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- "ents_f": 0.7561290323,
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  "ents_per_type": {
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- "ORG": {
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- "p": 0.5409836066,
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- "r": 0.6734693878,
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- "f": 0.6
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- },
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  "GPE": {
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- "p": 0.8718535469,
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- "r": 0.8985849057,
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- "f": 0.8850174216
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  },
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  "LOC": {
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- "p": 0.8305084746,
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- "r": 0.5632183908,
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- "f": 0.6712328767
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  },
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  "QUANTITY": {
 
 
 
 
 
184
  "p": 0.7142857143,
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- "r": 0.6097560976,
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- "f": 0.6578947368
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  },
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  "CARDINAL": {
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- "p": 0.7040816327,
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- "r": 0.7340425532,
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- "f": 0.71875
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  },
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  "NORP": {
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- "p": 0.4666666667,
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- "r": 0.4307692308,
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- },
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- "DATE": {
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- "p": 0.7887323944,
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  },
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  "PERSON": {
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- "p": 0.8255033557,
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- "r": 0.82,
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- "f": 0.8227424749
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  },
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- "FAC": {
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- "p": 0.1666666667,
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- "r": 0.1,
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- "f": 0.125
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  },
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  "MONEY": {
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- "p": 0.6666666667,
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- "r": 1.0,
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- "f": 0.8
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  },
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  "ORDINAL": {
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- "p": 0.5,
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- "r": 0.5454545455,
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- "f": 0.5217391304
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  },
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  "PERCENT": {
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- "p": 1.0,
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  "r": 1.0,
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- "f": 1.0
 
 
 
 
 
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  },
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  "LANGUAGE": {
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  "p": 0.0,
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  "f": 0.0
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  },
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- "WORK_OF_ART": {
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- "p": 0.5625,
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- "r": 0.4390243902,
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- "f": 0.4931506849
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  },
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  "TIME": {
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- "p": 0.5714285714,
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- "r": 0.6666666667,
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- "f": 0.6153846154
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  },
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  "EVENT": {
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- "p": 0.5789473684,
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  "r": 0.6470588235,
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- "f": 0.6111111111
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  },
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  "LAW": {
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  "p": 0.0,
@@ -256,5 +256,5 @@
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  "f": 0.0
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  }
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  },
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- "pos_acc": 0.9306717022
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  }
 
3
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  "token_f": 1.0,
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+ "sents_p": 0.7536231884,
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  "sents_r": 0.6753246753,
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+ "sents_f": 0.7123287671,
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+ "dep_uas": 0.6916256158,
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+ "dep_las": 0.5300492611,
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  "dep_las_per_type": {
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  "nsubj": {
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  "root": {
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  },
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  "cc": {
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  },
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  "relcl": {
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  "pozm": {
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+ "r": 0.3636363636,
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  },
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  "poss": {
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  "p": 0.0,
 
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  "f": 0.0
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  },
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  "aux": {
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+ "f": 0.6176470588
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  "prep": {
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  },
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  "iobj": {
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  "p": 0.0,
 
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  "f": 0.0
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  },
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  "pozv": {
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  "quantmod": {
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  "att": {
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  "det": {
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  "advmod": {
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+ "appos": {
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  "advcl": {
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  "ents_per_type": {
 
 
 
 
 
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  "GPE": {
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  "LOC": {
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  "QUANTITY": {
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+ "DATE": {
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  "p": 0.7142857143,
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  },
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  "CARDINAL": {
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  },
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  "NORP": {
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+ "r": 0.4153846154,
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+ "f": 0.4615384615
 
 
 
 
 
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  },
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  "PERSON": {
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+ "p": 0.7756410256,
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  "MONEY": {
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  },
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  "ORDINAL": {
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  },
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  "PERCENT": {
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  "r": 1.0,
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+ "WORK_OF_ART": {
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  },
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  "LANGUAGE": {
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+ "FAC": {
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  "TIME": {
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  "EVENT": {
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  },
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  "LAW": {
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  "p": 0.0,
 
256
  "f": 0.0
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  }
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  },
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+ "pos_acc": 0.9309414621
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  }
attribute_ruler/patterns CHANGED
Binary files a/attribute_ruler/patterns and b/attribute_ruler/patterns differ
 
config.cfg CHANGED
@@ -39,8 +39,9 @@ overwrite = true
39
  scorer = {"@scorers":"spacy.morphologizer_scorer.v1"}
40
 
41
  [components.morphologizer.model]
42
- @architectures = "spacy.Tagger.v1"
43
  nO = null
 
44
 
45
  [components.morphologizer.model.tok2vec]
46
  @architectures = "spacy.Tok2Vec.v2"
@@ -49,7 +50,7 @@ nO = null
49
  @architectures = "spacy.MultiHashEmbed.v2"
50
  width = ${components.morphologizer.model.tok2vec.encode:width}
51
  attrs = ["NORM","PREFIX","SUFFIX","SHAPE","SPACY"]
52
- rows = [5000,2500,2500,2500,100]
53
  include_static_vectors = true
54
 
55
  [components.morphologizer.model.tok2vec.encode]
@@ -82,7 +83,7 @@ nO = null
82
  @architectures = "spacy.MultiHashEmbed.v2"
83
  width = 96
84
  attrs = ["NORM","PREFIX","SUFFIX","SHAPE","SPACY"]
85
- rows = [5000,2500,2500,2500,100]
86
  include_static_vectors = true
87
 
88
  [components.ner.model.tok2vec.encode]
@@ -116,7 +117,7 @@ nO = null
116
  @architectures = "spacy.MultiHashEmbed.v2"
117
  width = 96
118
  attrs = ["NORM","PREFIX","SUFFIX","SHAPE","SPACY"]
119
- rows = [5000,2500,2500,2500,100]
120
  include_static_vectors = true
121
 
122
  [components.parser.model.tok2vec.encode]
@@ -132,8 +133,9 @@ overwrite = false
132
  scorer = {"@scorers":"spacy.senter_scorer.v1"}
133
 
134
  [components.senter.model]
135
- @architectures = "spacy.Tagger.v1"
136
  nO = null
 
137
 
138
  [components.senter.model.tok2vec]
139
  @architectures = "spacy.Tok2Vec.v2"
@@ -179,7 +181,7 @@ dropout = 0.1
179
  accumulate_gradient = 1
180
  patience = 5000
181
  max_epochs = 0
182
- max_steps = 0
183
  eval_frequency = 1000
184
  frozen_components = []
185
  before_to_disk = null
 
39
  scorer = {"@scorers":"spacy.morphologizer_scorer.v1"}
40
 
41
  [components.morphologizer.model]
42
+ @architectures = "spacy.Tagger.v2"
43
  nO = null
44
+ normalize = false
45
 
46
  [components.morphologizer.model.tok2vec]
47
  @architectures = "spacy.Tok2Vec.v2"
 
50
  @architectures = "spacy.MultiHashEmbed.v2"
51
  width = ${components.morphologizer.model.tok2vec.encode:width}
52
  attrs = ["NORM","PREFIX","SUFFIX","SHAPE","SPACY"]
53
+ rows = [5000,1000,2500,2500,50]
54
  include_static_vectors = true
55
 
56
  [components.morphologizer.model.tok2vec.encode]
 
83
  @architectures = "spacy.MultiHashEmbed.v2"
84
  width = 96
85
  attrs = ["NORM","PREFIX","SUFFIX","SHAPE","SPACY"]
86
+ rows = [5000,1000,2500,2500,50]
87
  include_static_vectors = true
88
 
89
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117
  @architectures = "spacy.MultiHashEmbed.v2"
118
  width = 96
119
  attrs = ["NORM","PREFIX","SUFFIX","SHAPE","SPACY"]
120
+ rows = [5000,1000,2500,2500,50]
121
  include_static_vectors = true
122
 
123
  [components.parser.model.tok2vec.encode]
 
133
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134
 
135
  [components.senter.model]
136
+ @architectures = "spacy.Tagger.v2"
137
  nO = null
138
+ normalize = false
139
 
140
  [components.senter.model.tok2vec]
141
  @architectures = "spacy.Tok2Vec.v2"
 
181
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182
  patience = 5000
183
  max_epochs = 0
184
+ max_steps = 100000
185
  eval_frequency = 1000
186
  frozen_components = []
187
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meta.json CHANGED
@@ -1,14 +1,14 @@
1
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2
  "lang":"mk",
3
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4
- "version":"3.2.0",
5
  "description":"Macedonian pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute_ruler, lemmatizer.",
6
  "author":"Explosion",
7
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8
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13
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14
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@@ -55,10 +55,6 @@
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57
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58
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59
- "I",
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61
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62
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63
 
64
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@@ -109,36 +105,36 @@
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@@ -146,14 +142,14 @@
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@@ -161,9 +157,9 @@
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@@ -171,9 +167,9 @@
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@@ -186,29 +182,29 @@
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214
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@@ -220,16 +216,16 @@
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@@ -266,90 +262,90 @@
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287
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289
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290
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291
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334
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368
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3
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7
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11
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14
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56
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57
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60
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