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spacy
/
ja_core_news_trf

Token Classification
spaCy
Japanese
Eval Results
Model card Files Community
adrianeboyd commited on
Commit
cbd9cb3
·
1 Parent(s): 49f366e

Update spaCy pipeline

Browse files
Files changed (11) hide show
  1. README.md +22 -22
  2. accuracy.json +122 -122
  3. config.cfg +1 -1
  4. ja_core_news_trf-any-py3-none-any.whl +2 -2
  5. meta.json +126 -126
  6. morphologizer/cfg +1 -0
  7. morphologizer/model +1 -1
  8. ner/model +1 -1
  9. parser/model +1 -1
  10. transformer/model +2 -2
  11. vocab/strings.json +2 -2
README.md CHANGED
@@ -14,27 +14,27 @@ model-index:
14
  metrics:
15
  - name: NER Precision
16
  type: precision
17
- value: 0.8298969072
18
  - name: NER Recall
19
  type: recall
20
- value: 0.8100628931
21
  - name: NER F Score
22
  type: f_score
23
- value: 0.8198599618
24
  - task:
25
  name: TAG
26
  type: token-classification
27
  metrics:
28
  - name: TAG (XPOS) Accuracy
29
  type: accuracy
30
- value: 0.9712488769
31
  - task:
32
  name: POS
33
  type: token-classification
34
  metrics:
35
  - name: POS (UPOS) Accuracy
36
  type: accuracy
37
- value: 0.9798207196
38
  - task:
39
  name: MORPH
40
  type: token-classification
@@ -55,21 +55,21 @@ model-index:
55
  metrics:
56
  - name: Unlabeled Attachment Score (UAS)
57
  type: f_score
58
- value: 0.9355666622
59
  - task:
60
  name: LABELED_DEPENDENCIES
61
  type: token-classification
62
  metrics:
63
  - name: Labeled Attachment Score (LAS)
64
  type: f_score
65
- value: 0.9241776538
66
  - task:
67
  name: SENTS
68
  type: token-classification
69
  metrics:
70
  - name: Sentences F-Score
71
  type: f_score
72
- value: 0.9755142018
73
  ---
74
  ### Details: https://spacy.io/models/ja#ja_core_news_trf
75
 
@@ -78,8 +78,8 @@ Japanese transformer pipeline (cl-tohoku/bert-base-japanese-char-v2). Components
78
  | Feature | Description |
79
  | --- | --- |
80
  | **Name** | `ja_core_news_trf` |
81
- | **Version** | `3.5.0` |
82
- | **spaCy** | `>=3.5.0,<3.6.0` |
83
  | **Default Pipeline** | `transformer`, `morphologizer`, `parser`, `attribute_ruler`, `ner` |
84
  | **Components** | `transformer`, `morphologizer`, `parser`, `attribute_ruler`, `ner` |
85
  | **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
@@ -111,16 +111,16 @@ Japanese transformer pipeline (cl-tohoku/bert-base-japanese-char-v2). Components
111
  | `TOKEN_F` | 97.77 |
112
  | `POS_ACC` | 97.98 |
113
  | `MORPH_ACC` | 0.00 |
114
- | `MORPH_MICRO_P` | 33.56 |
115
- | `MORPH_MICRO_R` | 96.08 |
116
- | `MORPH_MICRO_F` | 49.75 |
117
- | `SENTS_P` | 96.89 |
118
- | `SENTS_R` | 98.22 |
119
- | `SENTS_F` | 97.55 |
120
- | `DEP_UAS` | 93.56 |
121
- | `DEP_LAS` | 92.42 |
122
- | `TAG_ACC` | 97.12 |
123
  | `LEMMA_ACC` | 96.71 |
124
- | `ENTS_P` | 82.99 |
125
- | `ENTS_R` | 81.01 |
126
- | `ENTS_F` | 81.99 |
 
14
  metrics:
15
  - name: NER Precision
16
  type: precision
17
+ value: 0.8356510746
18
  - name: NER Recall
19
  type: recall
20
+ value: 0.8314465409
21
  - name: NER F Score
22
  type: f_score
23
+ value: 0.8335435057
24
  - task:
25
  name: TAG
26
  type: token-classification
27
  metrics:
28
  - name: TAG (XPOS) Accuracy
29
  type: accuracy
30
+ value: 0.9713305562
31
  - task:
32
  name: POS
33
  type: token-classification
34
  metrics:
35
  - name: POS (UPOS) Accuracy
36
  type: accuracy
37
+ value: 0.9798190675
38
  - task:
39
  name: MORPH
40
  type: token-classification
 
55
  metrics:
56
  - name: Unlabeled Attachment Score (UAS)
57
  type: f_score
58
+ value: 0.9311959654
59
  - task:
60
  name: LABELED_DEPENDENCIES
61
  type: token-classification
62
  metrics:
63
  - name: Labeled Attachment Score (LAS)
64
  type: f_score
65
+ value: 0.9202934425
66
  - task:
67
  name: SENTS
68
  type: token-classification
69
  metrics:
70
  - name: Sentences F-Score
71
  type: f_score
72
+ value: 0.9639727361
73
  ---
74
  ### Details: https://spacy.io/models/ja#ja_core_news_trf
75
 
 
78
  | Feature | Description |
79
  | --- | --- |
80
  | **Name** | `ja_core_news_trf` |
81
+ | **Version** | `3.6.1` |
82
+ | **spaCy** | `>=3.6.0,<3.7.0` |
83
  | **Default Pipeline** | `transformer`, `morphologizer`, `parser`, `attribute_ruler`, `ner` |
84
  | **Components** | `transformer`, `morphologizer`, `parser`, `attribute_ruler`, `ner` |
85
  | **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
 
111
  | `TOKEN_F` | 97.77 |
112
  | `POS_ACC` | 97.98 |
113
  | `MORPH_ACC` | 0.00 |
114
+ | `MORPH_MICRO_P` | 34.01 |
115
+ | `MORPH_MICRO_R` | 98.04 |
116
+ | `MORPH_MICRO_F` | 50.51 |
117
+ | `SENTS_P` | 95.19 |
118
+ | `SENTS_R` | 97.63 |
119
+ | `SENTS_F` | 96.40 |
120
+ | `DEP_UAS` | 93.12 |
121
+ | `DEP_LAS` | 92.03 |
122
+ | `TAG_ACC` | 97.13 |
123
  | `LEMMA_ACC` | 96.71 |
124
+ | `ENTS_P` | 83.57 |
125
+ | `ENTS_R` | 83.14 |
126
+ | `ENTS_F` | 83.35 |
accuracy.json CHANGED
@@ -3,16 +3,16 @@
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  "token_p": 0.9764591282,
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  "token_r": 0.9790021974,
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  "token_f": 0.9777290092,
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- "pos_acc": 0.9798207196,
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  "morph_acc": 0.0,
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  "morph_per_feat": {
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  "Polarity": {
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  "p": 1.0,
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- "r": 0.9607843137,
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  },
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  "Inflection": {
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  "p": 0.0,
@@ -25,106 +25,106 @@
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  "f": 0.0
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  }
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  },
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- "sents_p": 0.9688715953,
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- "sents_r": 0.9822485207,
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- "sents_f": 0.9755142018,
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- "dep_uas": 0.9355666622,
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- "dep_las": 0.9241776538,
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  "dep_las_per_type": {
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  "cc": {
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  "compound": {
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@@ -132,76 +132,76 @@
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  "DATE": {
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  "CARDINAL": {
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  "p": 1.0,
@@ -209,29 +209,29 @@
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  "f": 0.6666666667
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  "LOC": {
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@@ -249,5 +249,5 @@
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  "f": 1.0
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  }
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- "speed": 3501.8234367587
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