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spacy
/
nl_core_news_md

Token Classification
spaCy
Dutch
Eval Results
Model card Files Community
adrianeboyd commited on
Commit
c7180d3
·
1 Parent(s): c9eb0f5

Update spaCy pipeline

Browse files
Files changed (14) hide show
  1. README.md +28 -28
  2. accuracy.json +162 -162
  3. config.cfg +5 -4
  4. lemmatizer/model +1 -1
  5. meta.json +165 -165
  6. morphologizer/model +1 -1
  7. ner/model +2 -2
  8. nl_core_news_md-any-py3-none-any.whl +2 -2
  9. parser/model +1 -1
  10. senter/model +1 -1
  11. tagger/model +1 -1
  12. tok2vec/model +2 -2
  13. tokenizer +0 -0
  14. 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: 0.7590618337
18
  - name: NER Recall
19
  type: recall
20
- value: 0.7385892116
21
  - name: NER F Score
22
  type: f_score
23
- value: 0.7486855941
24
  - task:
25
  name: TAG
26
  type: token-classification
27
  metrics:
28
  - name: TAG (XPOS) Accuracy
29
  type: accuracy
30
- value: 0.9509934353
31
  - task:
32
  name: POS
33
  type: token-classification
34
  metrics:
35
  - name: POS (UPOS) Accuracy
36
  type: accuracy
37
- value: 0.9646673937
38
  - task:
39
  name: MORPH
40
  type: token-classification
41
  metrics:
42
  - name: Morph (UFeats) Accuracy
43
  type: accuracy
44
- value: 0.9621114625
45
  - task:
46
  name: LEMMA
47
  type: token-classification
48
  metrics:
49
  - name: Lemma Accuracy
50
  type: accuracy
51
- value: 0.9528316288
52
  - task:
53
  name: UNLABELED_DEPENDENCIES
54
  type: token-classification
55
  metrics:
56
  - name: Unlabeled Attachment Score (UAS)
57
  type: f_score
58
- value: 0.8662459129
59
  - task:
60
  name: LABELED_DEPENDENCIES
61
  type: token-classification
62
  metrics:
63
  - name: Labeled Attachment Score (LAS)
64
  type: f_score
65
- value: 0.8195574481
66
  - task:
67
  name: SENTS
68
  type: token-classification
69
  metrics:
70
  - name: Sentences F-Score
71
  type: f_score
72
- value: 0.8678321678
73
  ---
74
  ### Details: https://spacy.io/models/nl#nl_core_news_md
75
 
@@ -78,8 +78,8 @@ Dutch pipeline optimized for CPU. Components: tok2vec, morphologizer, tagger, pa
78
  | Feature | Description |
79
  | --- | --- |
80
  | **Name** | `nl_core_news_md` |
81
- | **Version** | `3.4.0` |
82
- | **spaCy** | `>=3.4.0,<3.5.0` |
83
  | **Default Pipeline** | `tok2vec`, `morphologizer`, `tagger`, `parser`, `lemmatizer`, `attribute_ruler`, `ner` |
84
  | **Components** | `tok2vec`, `morphologizer`, `tagger`, `parser`, `lemmatizer`, `senter`, `attribute_ruler`, `ner` |
85
  | **Vectors** | 500000 keys, 20000 unique vectors (300 dimensions) |
@@ -106,22 +106,22 @@ Dutch pipeline optimized for CPU. Components: tok2vec, morphologizer, tagger, pa
106
 
107
  | Type | Score |
108
  | --- | --- |
109
- | `TAG_ACC` | 95.10 |
110
- | `SENTS_P` | 84.65 |
111
- | `SENTS_R` | 89.02 |
112
- | `SENTS_F` | 86.78 |
113
- | `DEP_UAS` | 86.62 |
114
- | `DEP_LAS` | 81.96 |
115
- | `ENTS_P` | 75.91 |
116
- | `ENTS_R` | 73.86 |
117
- | `ENTS_F` | 74.87 |
118
- | `TOKEN_ACC` | 99.97 |
119
  | `TOKEN_P` | 99.74 |
120
  | `TOKEN_R` | 99.76 |
121
  | `TOKEN_F` | 99.75 |
122
- | `POS_ACC` | 96.47 |
123
- | `MORPH_ACC` | 96.21 |
124
- | `MORPH_MICRO_P` | 97.17 |
125
- | `MORPH_MICRO_R` | 95.31 |
126
- | `MORPH_MICRO_F` | 96.23 |
127
- | `LEMMA_ACC` | 95.28 |
 
14
  metrics:
15
  - name: NER Precision
16
  type: precision
17
+ value: 0.7668579627
18
  - name: NER Recall
19
  type: recall
20
+ value: 0.7392807746
21
  - name: NER F Score
22
  type: f_score
23
+ value: 0.7528169014
24
  - task:
25
  name: TAG
26
  type: token-classification
27
  metrics:
28
  - name: TAG (XPOS) Accuracy
29
  type: accuracy
30
+ value: 0.9498800436
31
  - task:
32
  name: POS
33
  type: token-classification
34
  metrics:
35
  - name: POS (UPOS) Accuracy
36
  type: accuracy
37
+ value: 0.9639258451
38
  - task:
39
  name: MORPH
40
  type: token-classification
41
  metrics:
42
  - name: Morph (UFeats) Accuracy
43
  type: accuracy
44
+ value: 0.9606282037
45
  - task:
46
  name: LEMMA
47
  type: token-classification
48
  metrics:
49
  - name: Lemma Accuracy
50
  type: accuracy
51
+ value: 0.9542272717
52
  - task:
53
  name: UNLABELED_DEPENDENCIES
54
  type: token-classification
55
  metrics:
56
  - name: Unlabeled Attachment Score (UAS)
57
  type: f_score
58
+ value: 0.8682209833
59
  - task:
60
  name: LABELED_DEPENDENCIES
61
  type: token-classification
62
  metrics:
63
  - name: Labeled Attachment Score (LAS)
64
  type: f_score
65
+ value: 0.8213380639
66
  - task:
67
  name: SENTS
68
  type: token-classification
69
  metrics:
70
  - name: Sentences F-Score
71
  type: f_score
72
+ value: 0.8684303351
73
  ---
74
  ### Details: https://spacy.io/models/nl#nl_core_news_md
75
 
 
78
  | Feature | Description |
79
  | --- | --- |
80
  | **Name** | `nl_core_news_md` |
81
+ | **Version** | `3.5.0` |
82
+ | **spaCy** | `>=3.5.0,<3.6.0` |
83
  | **Default Pipeline** | `tok2vec`, `morphologizer`, `tagger`, `parser`, `lemmatizer`, `attribute_ruler`, `ner` |
84
  | **Components** | `tok2vec`, `morphologizer`, `tagger`, `parser`, `lemmatizer`, `senter`, `attribute_ruler`, `ner` |
85
  | **Vectors** | 500000 keys, 20000 unique vectors (300 dimensions) |
 
106
 
107
  | Type | Score |
108
  | --- | --- |
109
+ | `TAG_ACC` | 94.99 |
110
+ | `SENTS_P` | 85.43 |
111
+ | `SENTS_R` | 88.31 |
112
+ | `SENTS_F` | 86.84 |
113
+ | `DEP_UAS` | 86.82 |
114
+ | `DEP_LAS` | 82.13 |
115
+ | `ENTS_P` | 76.69 |
116
+ | `ENTS_R` | 73.93 |
117
+ | `ENTS_F` | 75.28 |
118
+ | `TOKEN_ACC` | 99.94 |
119
  | `TOKEN_P` | 99.74 |
120
  | `TOKEN_R` | 99.76 |
121
  | `TOKEN_F` | 99.75 |
122
+ | `POS_ACC` | 96.39 |
123
+ | `MORPH_ACC` | 96.06 |
124
+ | `MORPH_MICRO_P` | 96.97 |
125
+ | `MORPH_MICRO_R` | 95.06 |
126
+ | `MORPH_MICRO_F` | 96.01 |
127
+ | `LEMMA_ACC` | 95.42 |
accuracy.json CHANGED
@@ -1,190 +1,190 @@
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  "expl": {
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  "orphan": {
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- "ents_p": 0.7590618337,
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  "ents_per_type": {
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@@ -231,27 +231,27 @@
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- "lemma_acc": 0.9528316288
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