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spacy
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sv_core_news_lg

Token Classification
spaCy
Swedish
Eval Results
Model card Files Community
1
adrianeboyd commited on
Commit
639eb72
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1 Parent(s): 75d5e95

Update spaCy pipeline

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Files changed (17) hide show
  1. README.md +27 -27
  2. accuracy.json +182 -182
  3. config.cfg +2 -0
  4. lemmatizer/model +1 -1
  5. lemmatizer/trees +0 -0
  6. meta.json +185 -185
  7. morphologizer/cfg +1 -0
  8. morphologizer/model +1 -1
  9. ner/model +1 -1
  10. parser/model +1 -1
  11. senter/model +1 -1
  12. sv_core_news_lg-any-py3-none-any.whl +2 -2
  13. tagger/cfg +1 -0
  14. tagger/model +1 -1
  15. tok2vec/model +1 -1
  16. tokenizer +0 -0
  17. vocab/strings.json +2 -2
README.md CHANGED
@@ -14,62 +14,62 @@ model-index:
14
  metrics:
15
  - name: NER Precision
16
  type: precision
17
- value: 0.8602032409
18
  - name: NER Recall
19
  type: recall
20
- value: 0.7620437956
21
  - name: NER F Score
22
  type: f_score
23
- value: 0.8081537866
24
  - task:
25
  name: TAG
26
  type: token-classification
27
  metrics:
28
  - name: TAG (XPOS) Accuracy
29
  type: accuracy
30
- value: 0.9509033378
31
  - task:
32
  name: POS
33
  type: token-classification
34
  metrics:
35
  - name: POS (UPOS) Accuracy
36
  type: accuracy
37
- value: 0.9637644177
38
  - task:
39
  name: MORPH
40
  type: token-classification
41
  metrics:
42
  - name: Morph (UFeats) Accuracy
43
  type: accuracy
44
- value: 0.9584566704
45
  - task:
46
  name: LEMMA
47
  type: token-classification
48
  metrics:
49
  - name: Lemma Accuracy
50
  type: accuracy
51
- value: 0.9555986526
52
  - task:
53
  name: UNLABELED_DEPENDENCIES
54
  type: token-classification
55
  metrics:
56
  - name: Unlabeled Attachment Score (UAS)
57
  type: f_score
58
- value: 0.8362874455
59
  - task:
60
  name: LABELED_DEPENDENCIES
61
  type: token-classification
62
  metrics:
63
  - name: Labeled Attachment Score (LAS)
64
  type: f_score
65
- value: 0.7879268362
66
  - task:
67
  name: SENTS
68
  type: token-classification
69
  metrics:
70
  - name: Sentences F-Score
71
  type: f_score
72
- value: 0.9364613881
73
  ---
74
  ### Details: https://spacy.io/models/sv#sv_core_news_lg
75
 
@@ -78,8 +78,8 @@ Swedish pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer,
78
  | Feature | Description |
79
  | --- | --- |
80
  | **Name** | `sv_core_news_lg` |
81
- | **Version** | `3.5.0` |
82
- | **spaCy** | `>=3.5.0,<3.6.0` |
83
  | **Default Pipeline** | `tok2vec`, `tagger`, `morphologizer`, `parser`, `lemmatizer`, `attribute_ruler`, `ner` |
84
  | **Components** | `tok2vec`, `tagger`, `morphologizer`, `parser`, `lemmatizer`, `senter`, `attribute_ruler`, `ner` |
85
  | **Vectors** | floret (200000, 300) |
@@ -110,18 +110,18 @@ Swedish pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer,
110
  | `TOKEN_P` | 99.95 |
111
  | `TOKEN_R` | 99.96 |
112
  | `TOKEN_F` | 99.95 |
113
- | `TAG_ACC` | 95.09 |
114
- | `POS_ACC` | 96.38 |
115
- | `MORPH_ACC` | 95.85 |
116
- | `MORPH_MICRO_P` | 97.77 |
117
- | `MORPH_MICRO_R` | 97.39 |
118
- | `MORPH_MICRO_F` | 97.58 |
119
- | `SENTS_P` | 92.29 |
120
- | `SENTS_R` | 95.04 |
121
- | `SENTS_F` | 93.65 |
122
- | `DEP_UAS` | 83.63 |
123
- | `DEP_LAS` | 78.79 |
124
- | `LEMMA_ACC` | 95.56 |
125
- | `ENTS_P` | 86.02 |
126
- | `ENTS_R` | 76.20 |
127
- | `ENTS_F` | 80.82 |
 
14
  metrics:
15
  - name: NER Precision
16
  type: precision
17
+ value: 0.8605993951
18
  - name: NER Recall
19
  type: recall
20
+ value: 0.7615571776
21
  - name: NER F Score
22
  type: f_score
23
+ value: 0.8080547309
24
  - task:
25
  name: TAG
26
  type: token-classification
27
  metrics:
28
  - name: TAG (XPOS) Accuracy
29
  type: accuracy
30
+ value: 0.9534551393
31
  - task:
32
  name: POS
33
  type: token-classification
34
  metrics:
35
  - name: POS (UPOS) Accuracy
36
  type: accuracy
37
+ value: 0.9635602736
38
  - task:
39
  name: MORPH
40
  type: token-classification
41
  metrics:
42
  - name: Morph (UFeats) Accuracy
43
  type: accuracy
44
+ value: 0.9586608145
45
  - task:
46
  name: LEMMA
47
  type: token-classification
48
  metrics:
49
  - name: Lemma Accuracy
50
  type: accuracy
51
+ value: 0.9559048688
52
  - task:
53
  name: UNLABELED_DEPENDENCIES
54
  type: token-classification
55
  metrics:
56
  - name: Unlabeled Attachment Score (UAS)
57
  type: f_score
58
+ value: 0.8310026609
59
  - task:
60
  name: LABELED_DEPENDENCIES
61
  type: token-classification
62
  metrics:
63
  - name: Labeled Attachment Score (LAS)
64
  type: f_score
65
+ value: 0.7857102417
66
  - task:
67
  name: SENTS
68
  type: token-classification
69
  metrics:
70
  - name: Sentences F-Score
71
  type: f_score
72
+ value: 0.9398058252
73
  ---
74
  ### Details: https://spacy.io/models/sv#sv_core_news_lg
75
 
 
78
  | Feature | Description |
79
  | --- | --- |
80
  | **Name** | `sv_core_news_lg` |
81
+ | **Version** | `3.6.0` |
82
+ | **spaCy** | `>=3.6.0,<3.7.0` |
83
  | **Default Pipeline** | `tok2vec`, `tagger`, `morphologizer`, `parser`, `lemmatizer`, `attribute_ruler`, `ner` |
84
  | **Components** | `tok2vec`, `tagger`, `morphologizer`, `parser`, `lemmatizer`, `senter`, `attribute_ruler`, `ner` |
85
  | **Vectors** | floret (200000, 300) |
 
110
  | `TOKEN_P` | 99.95 |
111
  | `TOKEN_R` | 99.96 |
112
  | `TOKEN_F` | 99.95 |
113
+ | `TAG_ACC` | 95.35 |
114
+ | `POS_ACC` | 96.36 |
115
+ | `MORPH_ACC` | 95.87 |
116
+ | `MORPH_MICRO_P` | 97.83 |
117
+ | `MORPH_MICRO_R` | 97.45 |
118
+ | `MORPH_MICRO_F` | 97.64 |
119
+ | `SENTS_P` | 92.02 |
120
+ | `SENTS_R` | 96.03 |
121
+ | `SENTS_F` | 93.98 |
122
+ | `DEP_UAS` | 83.10 |
123
+ | `DEP_LAS` | 78.57 |
124
+ | `LEMMA_ACC` | 95.59 |
125
+ | `ENTS_P` | 86.06 |
126
+ | `ENTS_R` | 76.16 |
127
+ | `ENTS_F` | 80.81 |
accuracy.json CHANGED
@@ -3,264 +3,264 @@
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@@ -283,51 +283,51 @@
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