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spacy
/
sv_core_news_sm

Token Classification
spaCy
Swedish
Eval Results (legacy)
Model card Files
xet
 Community
1
adrianeboyd commited on
Commit
982b70a
·
1 Parent(s): de0692f

Update spaCy pipeline

Browse files
Files changed (17) hide show
  1. README.md +27 -27
  2. accuracy.json +171 -171
  3. config.cfg +2 -0
  4. lemmatizer/model +1 -1
  5. lemmatizer/trees +0 -0
  6. meta.json +174 -174
  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_sm-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: 0.798119469
18
  - name: NER Recall
19
  type: recall
20
- value: 0.702189781
21
  - name: NER F Score
22
  type: f_score
23
- value: 0.7470877556
24
  - task:
25
  name: TAG
26
  type: token-classification
27
  metrics:
28
  - name: TAG (XPOS) Accuracy
29
  type: accuracy
30
- value: 0.9309992855
31
  - task:
32
  name: POS
33
  type: token-classification
34
  metrics:
35
  - name: POS (UPOS) Accuracy
36
  type: accuracy
37
- value: 0.9474328876
38
  - task:
39
  name: MORPH
40
  type: token-classification
41
  metrics:
42
  - name: Morph (UFeats) Accuracy
43
  type: accuracy
44
- value: 0.9386546902
45
  - task:
46
  name: LEMMA
47
  type: token-classification
48
  metrics:
49
  - name: Lemma Accuracy
50
  type: accuracy
51
- value: 0.9479432479
52
  - task:
53
  name: UNLABELED_DEPENDENCIES
54
  type: token-classification
55
  metrics:
56
  - name: Unlabeled Attachment Score (UAS)
57
  type: f_score
58
- value: 0.8139719203
59
  - task:
60
  name: LABELED_DEPENDENCIES
61
  type: token-classification
62
  metrics:
63
  - name: Labeled Attachment Score (LAS)
64
  type: f_score
65
- value: 0.759057971
66
  - task:
67
  name: SENTS
68
  type: token-classification
69
  metrics:
70
  - name: Sentences F-Score
71
  type: f_score
72
- value: 0.9218900675
73
  ---
74
  ### Details: https://spacy.io/models/sv#sv_core_news_sm
75
 
@@ -78,8 +78,8 @@ Swedish pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer,
78
  | Feature | Description |
79
  | --- | --- |
80
  | **Name** | `sv_core_news_sm` |
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** | 0 keys, 0 unique vectors (0 dimensions) |
@@ -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` | 93.10 |
114
- | `POS_ACC` | 94.74 |
115
- | `MORPH_ACC` | 93.87 |
116
- | `MORPH_MICRO_P` | 95.68 |
117
- | `MORPH_MICRO_R` | 95.59 |
118
- | `MORPH_MICRO_F` | 95.64 |
119
- | `SENTS_P` | 89.68 |
120
- | `SENTS_R` | 94.84 |
121
- | `SENTS_F` | 92.19 |
122
- | `DEP_UAS` | 81.40 |
123
- | `DEP_LAS` | 75.91 |
124
- | `LEMMA_ACC` | 94.79 |
125
- | `ENTS_P` | 79.81 |
126
- | `ENTS_R` | 70.22 |
127
- | `ENTS_F` | 74.71 |
 
14
  metrics:
15
  - name: NER Precision
16
  type: precision
17
+ value: 0.8002766252
18
  - name: NER Recall
19
  type: recall
20
+ value: 0.703892944
21
  - name: NER F Score
22
  type: f_score
23
+ value: 0.7489967638
24
  - task:
25
  name: TAG
26
  type: token-classification
27
  metrics:
28
  - name: TAG (XPOS) Accuracy
29
  type: accuracy
30
+ value: 0.9351842401
31
  - task:
32
  name: POS
33
  type: token-classification
34
  metrics:
35
  - name: POS (UPOS) Accuracy
36
  type: accuracy
37
+ value: 0.9511074819
38
  - task:
39
  name: MORPH
40
  type: token-classification
41
  metrics:
42
  - name: Morph (UFeats) Accuracy
43
  type: accuracy
44
+ value: 0.9406961315
45
  - task:
46
  name: LEMMA
47
  type: token-classification
48
  metrics:
49
  - name: Lemma Accuracy
50
  type: accuracy
51
+ value: 0.9489639686
52
  - task:
53
  name: UNLABELED_DEPENDENCIES
54
  type: token-classification
55
  metrics:
56
  - name: Unlabeled Attachment Score (UAS)
57
  type: f_score
58
+ value: 0.818598856
59
  - task:
60
  name: LABELED_DEPENDENCIES
61
  type: token-classification
62
  metrics:
63
  - name: Labeled Attachment Score (LAS)
64
  type: f_score
65
+ value: 0.7672877612
66
  - task:
67
  name: SENTS
68
  type: token-classification
69
  metrics:
70
  - name: Sentences F-Score
71
  type: f_score
72
+ value: 0.9368318756
73
  ---
74
  ### Details: https://spacy.io/models/sv#sv_core_news_sm
75
 
 
78
  | Feature | Description |
79
  | --- | --- |
80
  | **Name** | `sv_core_news_sm` |
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** | 0 keys, 0 unique vectors (0 dimensions) |
 
110
  | `TOKEN_P` | 99.95 |
111
  | `TOKEN_R` | 99.96 |
112
  | `TOKEN_F` | 99.95 |
113
+ | `TAG_ACC` | 93.52 |
114
+ | `POS_ACC` | 95.11 |
115
+ | `MORPH_ACC` | 94.07 |
116
+ | `MORPH_MICRO_P` | 96.02 |
117
+ | `MORPH_MICRO_R` | 95.75 |
118
+ | `MORPH_MICRO_F` | 95.88 |
119
+ | `SENTS_P` | 91.81 |
120
+ | `SENTS_R` | 95.63 |
121
+ | `SENTS_F` | 93.68 |
122
+ | `DEP_UAS` | 81.86 |
123
+ | `DEP_LAS` | 76.73 |
124
+ | `LEMMA_ACC` | 94.90 |
125
+ | `ENTS_P` | 80.03 |
126
+ | `ENTS_R` | 70.39 |
127
+ | `ENTS_F` | 74.90 |
accuracy.json CHANGED
@@ -3,199 +3,199 @@
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  "token_p": 0.9994896918,
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  "token_r": 0.9995917117,
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  "token_f": 0.9995406992,
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- "tag_acc": 0.9309992855,
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- "pos_acc": 0.9474328876,
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- "morph_acc": 0.9386546902,
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- "morph_micro_p": 0.956841589,
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- "morph_micro_f": 0.956393859,
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  "morph_per_feat": {
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@@ -203,39 +203,39 @@
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@@ -248,19 +248,19 @@
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@@ -283,51 +283,51 @@
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