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spacy
/
sv_core_news_md

Token Classification
spaCy
Swedish
Eval Results
Model card Files Community
1
adrianeboyd commited on
Commit
c22469e
·
1 Parent(s): 69e1f38

Update spaCy pipeline

Browse files
Files changed (17) hide show
  1. README.md +27 -27
  2. accuracy.json +179 -179
  3. config.cfg +2 -0
  4. lemmatizer/model +1 -1
  5. lemmatizer/trees +0 -0
  6. meta.json +182 -182
  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_md-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: 0.8516666667
18
  - name: NER Recall
19
  type: recall
20
- value: 0.7459854015
21
  - name: NER F Score
22
  type: f_score
23
- value: 0.7953307393
24
  - task:
25
  name: TAG
26
  type: token-classification
27
  metrics:
28
  - name: TAG (XPOS) Accuracy
29
  type: accuracy
30
- value: 0.9482494641
31
  - task:
32
  name: POS
33
  type: token-classification
34
  metrics:
35
  - name: POS (UPOS) Accuracy
36
  type: accuracy
37
- value: 0.9606001837
38
  - task:
39
  name: MORPH
40
  type: token-classification
41
  metrics:
42
  - name: Morph (UFeats) Accuracy
43
  type: accuracy
44
- value: 0.9541696438
45
  - task:
46
  name: LEMMA
47
  type: token-classification
48
  metrics:
49
  - name: Lemma Accuracy
50
  type: accuracy
51
- value: 0.9557007247
52
  - task:
53
  name: UNLABELED_DEPENDENCIES
54
  type: token-classification
55
  metrics:
56
  - name: Unlabeled Attachment Score (UAS)
57
  type: f_score
58
- value: 0.8339750849
59
  - task:
60
  name: LABELED_DEPENDENCIES
61
  type: token-classification
62
  metrics:
63
  - name: Labeled Attachment Score (LAS)
64
  type: f_score
65
- value: 0.7849377123
66
  - task:
67
  name: SENTS
68
  type: token-classification
69
  metrics:
70
  - name: Sentences F-Score
71
  type: f_score
72
- value: 0.9126213592
73
  ---
74
  ### Details: https://spacy.io/models/sv#sv_core_news_md
75
 
@@ -78,8 +78,8 @@ Swedish pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer,
78
  | Feature | Description |
79
  | --- | --- |
80
  | **Name** | `sv_core_news_md` |
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 (50000, 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` | 94.82 |
114
- | `POS_ACC` | 96.06 |
115
- | `MORPH_ACC` | 95.42 |
116
- | `MORPH_MICRO_P` | 97.28 |
117
- | `MORPH_MICRO_R` | 97.17 |
118
- | `MORPH_MICRO_F` | 97.23 |
119
- | `SENTS_P` | 89.35 |
120
- | `SENTS_R` | 93.25 |
121
- | `SENTS_F` | 91.26 |
122
- | `DEP_UAS` | 83.40 |
123
- | `DEP_LAS` | 78.49 |
124
- | `LEMMA_ACC` | 95.57 |
125
- | `ENTS_P` | 85.17 |
126
- | `ENTS_R` | 74.60 |
127
- | `ENTS_F` | 79.53 |
 
14
  metrics:
15
  - name: NER Precision
16
  type: precision
17
+ value: 0.8421624559
18
  - name: NER Recall
19
  type: recall
20
+ value: 0.7542579075
21
  - name: NER F Score
22
  type: f_score
23
+ value: 0.7957900141
24
  - task:
25
  name: TAG
26
  type: token-classification
27
  metrics:
28
  - name: TAG (XPOS) Accuracy
29
  type: accuracy
30
+ value: 0.9514136981
31
  - task:
32
  name: POS
33
  type: token-classification
34
  metrics:
35
  - name: POS (UPOS) Accuracy
36
  type: accuracy
37
+ value: 0.9628457691
38
  - task:
39
  name: MORPH
40
  type: token-classification
41
  metrics:
42
  - name: Morph (UFeats) Accuracy
43
  type: accuracy
44
+ value: 0.9560069409
45
  - task:
46
  name: LEMMA
47
  type: token-classification
48
  metrics:
49
  - name: Lemma Accuracy
50
  type: accuracy
51
+ value: 0.9550882923
52
  - task:
53
  name: UNLABELED_DEPENDENCIES
54
  type: token-classification
55
  metrics:
56
  - name: Unlabeled Attachment Score (UAS)
57
  type: f_score
58
+ value: 0.8323804132
59
  - task:
60
  name: LABELED_DEPENDENCIES
61
  type: token-classification
62
  metrics:
63
  - name: Labeled Attachment Score (LAS)
64
  type: f_score
65
+ value: 0.783130484
66
  - task:
67
  name: SENTS
68
  type: token-classification
69
  metrics:
70
  - name: Sentences F-Score
71
  type: f_score
72
+ value: 0.9269717624
73
  ---
74
  ### Details: https://spacy.io/models/sv#sv_core_news_md
75
 
 
78
  | Feature | Description |
79
  | --- | --- |
80
  | **Name** | `sv_core_news_md` |
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 (50000, 300) |
 
110
  | `TOKEN_P` | 99.95 |
111
  | `TOKEN_R` | 99.96 |
112
  | `TOKEN_F` | 99.95 |
113
+ | `TAG_ACC` | 95.14 |
114
+ | `POS_ACC` | 96.28 |
115
+ | `MORPH_ACC` | 95.60 |
116
+ | `MORPH_MICRO_P` | 97.44 |
117
+ | `MORPH_MICRO_R` | 97.40 |
118
+ | `MORPH_MICRO_F` | 97.42 |
119
+ | `SENTS_P` | 91.01 |
120
+ | `SENTS_R` | 94.44 |
121
+ | `SENTS_F` | 92.70 |
122
+ | `DEP_UAS` | 83.24 |
123
+ | `DEP_LAS` | 78.31 |
124
+ | `LEMMA_ACC` | 95.51 |
125
+ | `ENTS_P` | 84.22 |
126
+ | `ENTS_R` | 75.43 |
127
+ | `ENTS_F` | 79.58 |
accuracy.json CHANGED
@@ -3,32 +3,32 @@
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  "token_f": 0.9995406992,
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- "morph_acc": 0.9541696438,
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@@ -36,211 +36,211 @@
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@@ -283,51 +283,51 @@
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