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zh_core_web_trf

Token Classification spaCy Chinese Eval Results
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adrianeboyd commited on
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1 Parent(s): 3270d9e

Update spaCy pipeline

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Files changed (11) hide show
  1. .gitattributes +1 -0
  2. README.md +18 -18
  3. accuracy.json +195 -195
  4. meta.json +198 -198
  5. ner/model +1 -1
  6. parser/model +1 -1
  7. tagger/model +1 -1
  8. tokenizer/pkuseg_processors +0 -0
  9. transformer/model +2 -2
  10. vocab/strings.json +2 -2
  11. zh_core_web_trf-any-py3-none-any.whl +2 -2
.gitattributes CHANGED
@@ -19,3 +19,4 @@
19
  *strings.json filter=lfs diff=lfs merge=lfs -text
20
  vectors filter=lfs diff=lfs merge=lfs -text
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  model filter=lfs diff=lfs merge=lfs -text
 
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  *strings.json filter=lfs diff=lfs merge=lfs -text
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  vectors filter=lfs diff=lfs merge=lfs -text
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  model filter=lfs diff=lfs merge=lfs -text
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+ tokenizer/pkuseg_processors filter=lfs diff=lfs merge=lfs -text
README.md CHANGED
@@ -14,41 +14,41 @@ model-index:
14
  metrics:
15
  - name: NER Precision
16
  type: precision
17
- value: 0.6942860106
18
  - name: NER Recall
19
  type: recall
20
- value: 0.7357142857
21
  - name: NER F Score
22
  type: f_score
23
- value: 0.7144000427
24
  - task:
25
  name: TAG
26
  type: token-classification
27
  metrics:
28
  - name: TAG (XPOS) Accuracy
29
  type: accuracy
30
- value: 0.9235746441
31
  - task:
32
  name: UNLABELED_DEPENDENCIES
33
  type: token-classification
34
  metrics:
35
  - name: Unlabeled Attachment Score (UAS)
36
  type: f_score
37
- value: 0.7606435996
38
  - task:
39
  name: LABELED_DEPENDENCIES
40
  type: token-classification
41
  metrics:
42
  - name: Labeled Attachment Score (LAS)
43
  type: f_score
44
- value: 0.7210395809
45
  - task:
46
  name: SENTS
47
  type: token-classification
48
  metrics:
49
  - name: Sentences F-Score
50
  type: f_score
51
- value: 0.6541033435
52
  ---
53
  ### Details: https://spacy.io/models/zh#zh_core_web_trf
54
 
@@ -57,8 +57,8 @@ Chinese transformer pipeline (bert-base-chinese). Components: transformer, tagge
57
  | Feature | Description |
58
  | --- | --- |
59
  | **Name** | `zh_core_web_trf` |
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- | **Version** | `3.3.0` |
61
- | **spaCy** | `>=3.3.0.dev0,<3.4.0` |
62
  | **Default Pipeline** | `transformer`, `tagger`, `parser`, `attribute_ruler`, `ner` |
63
  | **Components** | `transformer`, `tagger`, `parser`, `attribute_ruler`, `ner` |
64
  | **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
@@ -88,12 +88,12 @@ Chinese transformer pipeline (bert-base-chinese). Components: transformer, tagge
88
  | `TOKEN_P` | 94.58 |
89
  | `TOKEN_R` | 91.36 |
90
  | `TOKEN_F` | 92.94 |
91
- | `TAG_ACC` | 92.36 |
92
- | `SENTS_P` | 68.37 |
93
- | `SENTS_R` | 62.69 |
94
- | `SENTS_F` | 65.41 |
95
- | `DEP_UAS` | 76.06 |
96
- | `DEP_LAS` | 72.10 |
97
- | `ENTS_P` | 69.43 |
98
- | `ENTS_R` | 73.57 |
99
- | `ENTS_F` | 71.44 |
14
  metrics:
15
  - name: NER Precision
16
  type: precision
17
+ value: 0.6807715613
18
  - name: NER Recall
19
  type: recall
20
+ value: 0.7407692308
21
  - name: NER F Score
22
  type: f_score
23
+ value: 0.7095042627
24
  - task:
25
  name: TAG
26
  type: token-classification
27
  metrics:
28
  - name: TAG (XPOS) Accuracy
29
  type: accuracy
30
+ value: 0.9237103231
31
  - task:
32
  name: UNLABELED_DEPENDENCIES
33
  type: token-classification
34
  metrics:
35
  - name: Unlabeled Attachment Score (UAS)
36
  type: f_score
37
+ value: 0.7641386414
38
  - task:
39
  name: LABELED_DEPENDENCIES
40
  type: token-classification
41
  metrics:
42
  - name: Labeled Attachment Score (LAS)
43
  type: f_score
44
+ value: 0.724427042
45
  - task:
46
  name: SENTS
47
  type: token-classification
48
  metrics:
49
  - name: Sentences F-Score
50
  type: f_score
51
+ value: 0.6377692376
52
  ---
53
  ### Details: https://spacy.io/models/zh#zh_core_web_trf
54
 
57
  | Feature | Description |
58
  | --- | --- |
59
  | **Name** | `zh_core_web_trf` |
60
+ | **Version** | `3.4.0` |
61
+ | **spaCy** | `>=3.4.0,<3.5.0` |
62
  | **Default Pipeline** | `transformer`, `tagger`, `parser`, `attribute_ruler`, `ner` |
63
  | **Components** | `transformer`, `tagger`, `parser`, `attribute_ruler`, `ner` |
64
  | **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
88
  | `TOKEN_P` | 94.58 |
89
  | `TOKEN_R` | 91.36 |
90
  | `TOKEN_F` | 92.94 |
91
+ | `TAG_ACC` | 92.37 |
92
+ | `SENTS_P` | 68.85 |
93
+ | `SENTS_R` | 59.40 |
94
+ | `SENTS_F` | 63.78 |
95
+ | `DEP_UAS` | 76.41 |
96
+ | `DEP_LAS` | 72.44 |
97
+ | `ENTS_P` | 68.08 |
98
+ | `ENTS_R` | 74.08 |
99
+ | `ENTS_F` | 70.95 |
accuracy.json CHANGED
@@ -3,212 +3,212 @@
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