adrianeboyd commited on
Commit
0ff593c
1 Parent(s): 1f12b7f

Update spaCy pipeline

Browse files
README.md CHANGED
@@ -14,62 +14,62 @@ model-index:
14
  metrics:
15
  - name: NER Precision
16
  type: precision
17
- value: 0.7507507508
18
  - name: NER Recall
19
  type: recall
20
- value: 0.7683442182
21
  - name: NER F Score
22
  type: f_score
23
- value: 0.7594456047
24
  - task:
25
  name: TAG
26
  type: token-classification
27
  metrics:
28
  - name: TAG (XPOS) Accuracy
29
  type: accuracy
30
- value: 0.9665464567
31
  - task:
32
  name: POS
33
  type: token-classification
34
  metrics:
35
  - name: POS (UPOS) Accuracy
36
  type: accuracy
37
- value: 0.9392520821
38
  - task:
39
  name: MORPH
40
  type: token-classification
41
  metrics:
42
  - name: Morph (UFeats) Accuracy
43
  type: accuracy
44
- value: 0.9511244026
45
  - task:
46
  name: LEMMA
47
  type: token-classification
48
  metrics:
49
  - name: Lemma Accuracy
50
  type: accuracy
51
- value: 0.9583369983
52
  - task:
53
  name: UNLABELED_DEPENDENCIES
54
  type: token-classification
55
  metrics:
56
  - name: Unlabeled Attachment Score (UAS)
57
  type: f_score
58
- value: 0.8896207773
59
  - task:
60
  name: LABELED_DEPENDENCIES
61
  type: token-classification
62
  metrics:
63
  - name: Labeled Attachment Score (LAS)
64
  type: f_score
65
- value: 0.8376148803
66
  - task:
67
  name: SENTS
68
  type: token-classification
69
  metrics:
70
  - name: Sentences F-Score
71
  type: f_score
72
- value: 0.9686457638
73
  ---
74
  ### Details: https://spacy.io/models/ro#ro_core_news_lg
75
 
@@ -78,8 +78,8 @@ Romanian pipeline optimized for CPU. Components: tok2vec, tagger, parser, lemmat
78
  | Feature | Description |
79
  | --- | --- |
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  | **Name** | `ro_core_news_lg` |
81
- | **Version** | `3.4.0` |
82
- | **spaCy** | `>=3.4.0,<3.5.0` |
83
  | **Default Pipeline** | `tok2vec`, `tagger`, `parser`, `lemmatizer`, `attribute_ruler`, `ner` |
84
  | **Components** | `tok2vec`, `tagger`, `parser`, `lemmatizer`, `senter`, `attribute_ruler`, `ner` |
85
  | **Vectors** | 500000 keys, 500000 unique vectors (300 dimensions) |
@@ -105,22 +105,22 @@ Romanian pipeline optimized for CPU. Components: tok2vec, tagger, parser, lemmat
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106
  | Type | Score |
107
  | --- | --- |
108
- | `TOKEN_ACC` | 99.90 |
109
  | `TOKEN_P` | 99.67 |
110
  | `TOKEN_R` | 99.57 |
111
  | `TOKEN_F` | 99.59 |
112
- | `TAG_ACC` | 96.65 |
113
- | `SENTS_P` | 97.19 |
114
- | `SENTS_R` | 96.54 |
115
- | `SENTS_F` | 96.86 |
116
- | `DEP_UAS` | 88.96 |
117
- | `DEP_LAS` | 83.76 |
118
- | `LEMMA_ACC` | 95.83 |
119
- | `POS_ACC` | 93.93 |
120
- | `MORPH_ACC` | 95.11 |
121
- | `MORPH_MICRO_P` | 99.00 |
122
- | `MORPH_MICRO_R` | 95.81 |
123
- | `MORPH_MICRO_F` | 97.08 |
124
- | `ENTS_P` | 75.08 |
125
- | `ENTS_R` | 76.83 |
126
- | `ENTS_F` | 75.94 |
 
14
  metrics:
15
  - name: NER Precision
16
  type: precision
17
+ value: 0.7502799552
18
  - name: NER Recall
19
  type: recall
20
+ value: 0.7721859393
21
  - name: NER F Score
22
  type: f_score
23
+ value: 0.7610753502
24
  - task:
25
  name: TAG
26
  type: token-classification
27
  metrics:
28
  - name: TAG (XPOS) Accuracy
29
  type: accuracy
30
+ value: 0.9657255109
31
  - task:
32
  name: POS
33
  type: token-classification
34
  metrics:
35
  - name: POS (UPOS) Accuracy
36
  type: accuracy
37
+ value: 0.9395242502
38
  - task:
39
  name: MORPH
40
  type: token-classification
41
  metrics:
42
  - name: Morph (UFeats) Accuracy
43
  type: accuracy
44
+ value: 0.9499516228
45
  - task:
46
  name: LEMMA
47
  type: token-classification
48
  metrics:
49
  - name: Lemma Accuracy
50
  type: accuracy
51
+ value: 0.9575746914
52
  - task:
53
  name: UNLABELED_DEPENDENCIES
54
  type: token-classification
55
  metrics:
56
  - name: Unlabeled Attachment Score (UAS)
57
  type: f_score
58
+ value: 0.8875784191
59
  - task:
60
  name: LABELED_DEPENDENCIES
61
  type: token-classification
62
  metrics:
63
  - name: Labeled Attachment Score (LAS)
64
  type: f_score
65
+ value: 0.8359473024
66
  - task:
67
  name: SENTS
68
  type: token-classification
69
  metrics:
70
  - name: Sentences F-Score
71
  type: f_score
72
+ value: 0.9699799867
73
  ---
74
  ### Details: https://spacy.io/models/ro#ro_core_news_lg
75
 
 
78
  | Feature | Description |
79
  | --- | --- |
80
  | **Name** | `ro_core_news_lg` |
81
+ | **Version** | `3.5.0` |
82
+ | **spaCy** | `>=3.5.0,<3.6.0` |
83
  | **Default Pipeline** | `tok2vec`, `tagger`, `parser`, `lemmatizer`, `attribute_ruler`, `ner` |
84
  | **Components** | `tok2vec`, `tagger`, `parser`, `lemmatizer`, `senter`, `attribute_ruler`, `ner` |
85
  | **Vectors** | 500000 keys, 500000 unique vectors (300 dimensions) |
 
105
 
106
  | Type | Score |
107
  | --- | --- |
108
+ | `TOKEN_ACC` | 99.80 |
109
  | `TOKEN_P` | 99.67 |
110
  | `TOKEN_R` | 99.57 |
111
  | `TOKEN_F` | 99.59 |
112
+ | `TAG_ACC` | 96.57 |
113
+ | `SENTS_P` | 97.32 |
114
+ | `SENTS_R` | 96.68 |
115
+ | `SENTS_F` | 97.00 |
116
+ | `DEP_UAS` | 88.76 |
117
+ | `DEP_LAS` | 83.59 |
118
+ | `LEMMA_ACC` | 95.76 |
119
+ | `POS_ACC` | 93.95 |
120
+ | `MORPH_ACC` | 95.00 |
121
+ | `MORPH_MICRO_P` | 99.05 |
122
+ | `MORPH_MICRO_R` | 95.76 |
123
+ | `MORPH_MICRO_F` | 97.04 |
124
+ | `ENTS_P` | 75.03 |
125
+ | `ENTS_R` | 77.22 |
126
+ | `ENTS_F` | 76.11 |
accuracy.json CHANGED
@@ -1,129 +1,129 @@
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  "token_p": 0.9967350492,
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  "token_r": 0.9957244934,
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- "tag_acc": 0.9665464567,
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- "sents_p": 0.9718875502,
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- "sents_r": 0.9654255319,
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- "sents_f": 0.9686457638,
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- "dep_uas": 0.8896207773,
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- "dep_las": 0.8376148803,
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  "nsubj:pass": {
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  "fixed": {
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  "nmod:agent": {
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  "p": 0.0,
@@ -131,9 +131,9 @@
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@@ -141,74 +141,69 @@
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  "obl:agent": {
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  "obl:pmod": {
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  "expl:pv": {
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  "f": 0.8
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- "compound": {
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  "expl": {
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  "r": 0.8148148148,
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- "ccomp:pmod": {
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- "p": 0.0,
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  "expl:poss": {
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- "p": 1.0,
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  "r": 0.9032258065,
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  "goeswith": {
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- "xcomp": {
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- "dep": {
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  "orphan": {
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  "p": 0.0,
@@ -216,15 +211,20 @@
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  "f": 0.0
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  "expl:impers": {
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  "list": {
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  "csubj:pass": {
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@@ -236,67 +236,67 @@
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  "f": 0.0
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- "lemma_acc": 0.9583369983,
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- "pos_acc": 0.9392520821,
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- "morph_acc": 0.9511244026,
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- "morph_micro_p": 0.9899534372,
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  "morph_per_feat": {
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  "Case": {
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  "Gender": {
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  "Number": {
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@@ -304,14 +304,14 @@
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  "Reflex": {
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@@ -319,19 +319,19 @@
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  "Tense": {
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  "Variant": {
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@@ -339,9 +339,9 @@
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  "f": 0.9702970297
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  "Position": {
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  "Number[psor]": {
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  "p": 1.0,
@@ -359,49 +359,49 @@
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  "f": 0.0
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  },
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- "ents_p": 0.7507507508,
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  "ents_per_type": {
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  "NUMERIC_VALUE": {
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@@ -409,40 +409,40 @@
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