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spacy
/
nl_core_news_sm

Token Classification spaCy Dutch Eval Results
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adrianeboyd commited on
Commit
be1c49b
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1 Parent(s): e75164d

Update spaCy pipeline

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Files changed (14) hide show
  1. README.md +28 -28
  2. accuracy.json +168 -168
  3. config.cfg +5 -4
  4. lemmatizer/model +1 -1
  5. meta.json +171 -171
  6. morphologizer/model +1 -1
  7. ner/model +2 -2
  8. nl_core_news_sm-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: 0.7371060172
18
  - name: NER Recall
19
  type: recall
20
- value: 0.7116182573
21
  - name: NER F Score
22
  type: f_score
23
- value: 0.724137931
24
  - task:
25
  name: TAG
26
  type: token-classification
27
  metrics:
28
  - name: TAG (XPOS) Accuracy
29
  type: accuracy
30
- value: 0.9416383146
31
  - task:
32
  name: POS
33
  type: token-classification
34
  metrics:
35
  - name: POS (UPOS) Accuracy
36
  type: accuracy
37
- value: 0.9587577152
38
  - task:
39
  name: MORPH
40
  type: token-classification
41
  metrics:
42
  - name: Morph (UFeats) Accuracy
43
  type: accuracy
44
- value: 0.952426655
45
  - task:
46
  name: LEMMA
47
  type: token-classification
48
  metrics:
49
  - name: Lemma Accuracy
50
  type: accuracy
51
- value: 0.9511743027
52
  - task:
53
  name: UNLABELED_DEPENDENCIES
54
  type: token-classification
55
  metrics:
56
  - name: Unlabeled Attachment Score (UAS)
57
  type: f_score
58
- value: 0.8520452126
59
  - task:
60
  name: LABELED_DEPENDENCIES
61
  type: token-classification
62
  metrics:
63
  - name: Labeled Attachment Score (LAS)
64
  type: f_score
65
- value: 0.8008008515
66
  - task:
67
  name: SENTS
68
  type: token-classification
69
  metrics:
70
  - name: Sentences F-Score
71
  type: f_score
72
- value: 0.8551385479
73
  ---
74
  ### Details: https://spacy.io/models/nl#nl_core_news_sm
75
 
@@ -78,8 +78,8 @@ Dutch pipeline optimized for CPU. Components: tok2vec, morphologizer, tagger, pa
78
  | Feature | Description |
79
  | --- | --- |
80
  | **Name** | `nl_core_news_sm` |
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** | 0 keys, 0 unique vectors (0 dimensions) |
@@ -106,22 +106,22 @@ Dutch pipeline optimized for CPU. Components: tok2vec, morphologizer, tagger, pa
106
 
107
  | Type | Score |
108
  | --- | --- |
109
- | `TAG_ACC` | 94.16 |
110
- | `SENTS_P` | 83.67 |
111
- | `SENTS_R` | 87.45 |
112
- | `SENTS_F` | 85.51 |
113
- | `DEP_UAS` | 85.20 |
114
- | `DEP_LAS` | 80.08 |
115
- | `ENTS_P` | 73.71 |
116
- | `ENTS_R` | 71.16 |
117
- | `ENTS_F` | 72.41 |
118
- | `TOKEN_ACC` | 99.97 |
119
  | `TOKEN_P` | 99.74 |
120
  | `TOKEN_R` | 99.76 |
121
  | `TOKEN_F` | 99.75 |
122
- | `POS_ACC` | 95.88 |
123
- | `MORPH_ACC` | 95.24 |
124
- | `MORPH_MICRO_P` | 95.96 |
125
- | `MORPH_MICRO_R` | 93.99 |
126
- | `MORPH_MICRO_F` | 94.96 |
127
- | `LEMMA_ACC` | 95.12 |
14
  metrics:
15
  - name: NER Precision
16
  type: precision
17
+ value: 0.7496350365
18
  - name: NER Recall
19
  type: recall
20
+ value: 0.7102351314
21
  - name: NER F Score
22
  type: f_score
23
+ value: 0.7294034091
24
  - task:
25
  name: TAG
26
  type: token-classification
27
  metrics:
28
  - name: TAG (XPOS) Accuracy
29
  type: accuracy
30
+ value: 0.939195673
31
  - task:
32
  name: POS
33
  type: token-classification
34
  metrics:
35
  - name: POS (UPOS) Accuracy
36
  type: accuracy
37
+ value: 0.9572746505
38
  - task:
39
  name: MORPH
40
  type: token-classification
41
  metrics:
42
  - name: Morph (UFeats) Accuracy
43
  type: accuracy
44
+ value: 0.9504635184
45
  - task:
46
  name: LEMMA
47
  type: token-classification
48
  metrics:
49
  - name: Lemma Accuracy
50
  type: accuracy
51
+ value: 0.9504328674
52
  - task:
53
  name: UNLABELED_DEPENDENCIES
54
  type: token-classification
55
  metrics:
56
  - name: Unlabeled Attachment Score (UAS)
57
  type: f_score
58
+ value: 0.8535422689
59
  - task:
60
  name: LABELED_DEPENDENCIES
61
  type: token-classification
62
  metrics:
63
  - name: Labeled Attachment Score (LAS)
64
  type: f_score
65
+ value: 0.80149091
66
  - task:
67
  name: SENTS
68
  type: token-classification
69
  metrics:
70
  - name: Sentences F-Score
71
  type: f_score
72
+ value: 0.8658922914
73
  ---
74
  ### Details: https://spacy.io/models/nl#nl_core_news_sm
75
 
78
  | Feature | Description |
79
  | --- | --- |
80
  | **Name** | `nl_core_news_sm` |
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** | 0 keys, 0 unique vectors (0 dimensions) |
106
 
107
  | Type | Score |
108
  | --- | --- |
109
+ | `TAG_ACC` | 93.92 |
110
+ | `SENTS_P` | 85.00 |
111
+ | `SENTS_R` | 88.24 |
112
+ | `SENTS_F` | 86.59 |
113
+ | `DEP_UAS` | 85.35 |
114
+ | `DEP_LAS` | 80.15 |
115
+ | `ENTS_P` | 74.96 |
116
+ | `ENTS_R` | 71.02 |
117
+ | `ENTS_F` | 72.94 |
118
+ | `TOKEN_ACC` | 99.94 |
119
  | `TOKEN_P` | 99.74 |
120
  | `TOKEN_R` | 99.76 |
121
  | `TOKEN_F` | 99.75 |
122
+ | `POS_ACC` | 95.73 |
123
+ | `MORPH_ACC` | 95.05 |
124
+ | `MORPH_MICRO_P` | 95.82 |
125
+ | `MORPH_MICRO_R` | 93.63 |
126
+ | `MORPH_MICRO_F` | 94.71 |
127
+ | `LEMMA_ACC` | 95.04 |
accuracy.json CHANGED
@@ -1,192 +1,192 @@
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  "fixed": {
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- "iobj": {
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  "obl:agent": {
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- "dep": {
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- "ents_p": 0.7371060172,
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- "ents_r": 0.7116182573,
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- "ents_f": 0.724137931,
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  "ents_per_type": {
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- "WORK_OF_ART": {
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@@ -201,37 +201,37 @@
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  },
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- "CARDINAL": {
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- "PERSON": {
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- "GPE": {
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- "ORG": {
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- "EVENT": {
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- "DATE": {
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- "ORDINAL": {
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@@ -241,32 +241,32 @@
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- "TIME": {
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- "LAW": {
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- "FAC": {
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- "MONEY": {
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- "PERCENT": {
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@@ -277,66 +277,66 @@
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  "f": 0.0
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  }
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- "speed": 12614.5409600293,
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- "token_acc": 0.9997165842,
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  "token_p": 0.9974281853,
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  "token_r": 0.9975586363,
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  "token_f": 0.9974934066,
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- "pos_acc": 0.9587577152,
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- "morph_acc": 0.952426655,
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- "morph_micro_p": 0.959603942,
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- "morph_micro_r": 0.9398989209,
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- "morph_micro_f": 0.9496492237,
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  "morph_per_feat": {
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  "Person": {
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  "Poss": {
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  "PronType": {
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  "Gender": {
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  "Tense": {
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  "Degree": {
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  "Definite": {
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  "Reflex": {
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  "p": 1.0,
@@ -344,10 +344,10 @@
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  "f": 1.0
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  },
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  "Abbr": {
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  }
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  },
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- "lemma_acc": 0.9511743027
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  }
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  "dep_las_per_type": {
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  "nsubj": {
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  "ccomp": {
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