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spacy
/
pl_core_news_lg

Token Classification
spaCy
Polish
Eval Results
Model card Files Community
adrianeboyd commited on
Commit
969b65f
•
1 Parent(s): 511428a

Update spaCy pipeline

Browse files
Files changed (14) hide show
  1. README.md +28 -28
  2. accuracy.json +287 -287
  3. config.cfg +7 -6
  4. lemmatizer/model +1 -1
  5. meta.json +290 -290
  6. morphologizer/model +1 -1
  7. ner/model +2 -2
  8. parser/model +1 -1
  9. pl_core_news_lg-any-py3-none-any.whl +2 -2
  10. senter/model +1 -1
  11. tagger/model +2 -2
  12. tok2vec/model +2 -2
  13. tokenizer +1 -1
  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: 0.8497109827
18
  - name: NER Recall
19
  type: recall
20
- value: 0.8432122371
21
  - name: NER F Score
22
  type: f_score
23
- value: 0.8464491363
24
  - task:
25
  name: TAG
26
  type: token-classification
27
  metrics:
28
  - name: TAG (XPOS) Accuracy
29
  type: accuracy
30
- value: 0.9835071032
31
  - task:
32
  name: POS
33
  type: token-classification
34
  metrics:
35
  - name: POS (UPOS) Accuracy
36
  type: accuracy
37
- value: 0.9782346706
38
  - task:
39
  name: MORPH
40
  type: token-classification
41
  metrics:
42
  - name: Morph (UFeats) Accuracy
43
  type: accuracy
44
- value: 0.9082463602
45
  - task:
46
  name: LEMMA
47
  type: token-classification
48
  metrics:
49
  - name: Lemma Accuracy
50
  type: accuracy
51
- value: 0.942641148
52
  - task:
53
  name: UNLABELED_DEPENDENCIES
54
  type: token-classification
55
  metrics:
56
  - name: Unlabeled Attachment Score (UAS)
57
  type: f_score
58
- value: 0.8928976035
59
  - task:
60
  name: LABELED_DEPENDENCIES
61
  type: token-classification
62
  metrics:
63
  - name: Labeled Attachment Score (LAS)
64
  type: f_score
65
- value: 0.8216924578
66
  - task:
67
  name: SENTS
68
  type: token-classification
69
  metrics:
70
  - name: Sentences F-Score
71
  type: f_score
72
- value: 0.965705201
73
  ---
74
  ### Details: https://spacy.io/models/pl#pl_core_news_lg
75
 
@@ -78,8 +78,8 @@ Polish pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, l
78
  | Feature | Description |
79
  | --- | --- |
80
  | **Name** | `pl_core_news_lg` |
81
- | **Version** | `3.4.0` |
82
- | **spaCy** | `>=3.4.0,<3.5.0` |
83
  | **Default Pipeline** | `tok2vec`, `morphologizer`, `parser`, `lemmatizer`, `tagger`, `attribute_ruler`, `ner` |
84
  | **Components** | `tok2vec`, `morphologizer`, `parser`, `lemmatizer`, `tagger`, `senter`, `attribute_ruler`, `ner` |
85
  | **Vectors** | 500000 keys, 500000 unique vectors (300 dimensions) |
@@ -106,22 +106,22 @@ Polish pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, l
106
 
107
  | Type | Score |
108
  | --- | --- |
109
- | `TOKEN_ACC` | 99.99 |
110
  | `TOKEN_P` | 99.63 |
111
  | `TOKEN_R` | 99.83 |
112
  | `TOKEN_F` | 99.73 |
113
- | `POS_ACC` | 97.82 |
114
- | `MORPH_ACC` | 90.82 |
115
- | `MORPH_MICRO_P` | 95.66 |
116
- | `MORPH_MICRO_R` | 95.57 |
117
- | `MORPH_MICRO_F` | 95.62 |
118
- | `SENTS_P` | 97.17 |
119
- | `SENTS_R` | 95.98 |
120
- | `SENTS_F` | 96.57 |
121
- | `DEP_UAS` | 89.29 |
122
- | `DEP_LAS` | 82.17 |
123
- | `LEMMA_ACC` | 94.26 |
124
- | `TAG_ACC` | 98.35 |
125
- | `ENTS_P` | 84.97 |
126
- | `ENTS_R` | 84.32 |
127
- | `ENTS_F` | 84.64 |
 
14
  metrics:
15
  - name: NER Precision
16
  type: precision
17
+ value: 0.847446671
18
  - name: NER Recall
19
  type: recall
20
+ value: 0.8355640535
21
  - name: NER F Score
22
  type: f_score
23
+ value: 0.8414634146
24
  - task:
25
  name: TAG
26
  type: token-classification
27
  metrics:
28
  - name: TAG (XPOS) Accuracy
29
  type: accuracy
30
+ value: 0.9828973843
31
  - task:
32
  name: POS
33
  type: token-classification
34
  metrics:
35
  - name: POS (UPOS) Accuracy
36
  type: accuracy
37
+ value: 0.9781017658
38
  - task:
39
  name: MORPH
40
  type: token-classification
41
  metrics:
42
  - name: Morph (UFeats) Accuracy
43
  type: accuracy
44
+ value: 0.9098299967
45
  - task:
46
  name: LEMMA
47
  type: token-classification
48
  metrics:
49
  - name: Lemma Accuracy
50
  type: accuracy
51
+ value: 0.9424670256
52
  - task:
53
  name: UNLABELED_DEPENDENCIES
54
  type: token-classification
55
  metrics:
56
  - name: Unlabeled Attachment Score (UAS)
57
  type: f_score
58
+ value: 0.894969847
59
  - task:
60
  name: LABELED_DEPENDENCIES
61
  type: token-classification
62
  metrics:
63
  - name: Labeled Attachment Score (LAS)
64
  type: f_score
65
+ value: 0.8237918475
66
  - task:
67
  name: SENTS
68
  type: token-classification
69
  metrics:
70
  - name: Sentences F-Score
71
  type: f_score
72
+ value: 0.9631305135
73
  ---
74
  ### Details: https://spacy.io/models/pl#pl_core_news_lg
75
 
 
78
  | Feature | Description |
79
  | --- | --- |
80
  | **Name** | `pl_core_news_lg` |
81
+ | **Version** | `3.5.0` |
82
+ | **spaCy** | `>=3.5.0,<3.6.0` |
83
  | **Default Pipeline** | `tok2vec`, `morphologizer`, `parser`, `lemmatizer`, `tagger`, `attribute_ruler`, `ner` |
84
  | **Components** | `tok2vec`, `morphologizer`, `parser`, `lemmatizer`, `tagger`, `senter`, `attribute_ruler`, `ner` |
85
  | **Vectors** | 500000 keys, 500000 unique vectors (300 dimensions) |
 
106
 
107
  | Type | Score |
108
  | --- | --- |
109
+ | `TOKEN_ACC` | 99.98 |
110
  | `TOKEN_P` | 99.63 |
111
  | `TOKEN_R` | 99.83 |
112
  | `TOKEN_F` | 99.73 |
113
+ | `POS_ACC` | 97.81 |
114
+ | `MORPH_ACC` | 90.98 |
115
+ | `MORPH_MICRO_P` | 95.74 |
116
+ | `MORPH_MICRO_R` | 95.61 |
117
+ | `MORPH_MICRO_F` | 95.67 |
118
+ | `SENTS_P` | 96.51 |
119
+ | `SENTS_R` | 96.12 |
120
+ | `SENTS_F` | 96.31 |
121
+ | `DEP_UAS` | 89.50 |
122
+ | `DEP_LAS` | 82.38 |
123
+ | `LEMMA_ACC` | 94.25 |
124
+ | `TAG_ACC` | 98.29 |
125
+ | `ENTS_P` | 84.74 |
126
+ | `ENTS_R` | 83.56 |
127
+ | `ENTS_F` | 84.15 |
accuracy.json CHANGED
@@ -1,28 +1,28 @@
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  {
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- "token_acc": 0.9998840378,
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  "token_p": 0.99628964,
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  "token_r": 0.9982863284,
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  "token_f": 0.9972869848,
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- "pos_acc": 0.9782346706,
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- "morph_acc": 0.9082463602,
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- "morph_micro_p": 0.9566133721,
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- "morph_micro_r": 0.9556931008,
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- "morph_micro_f": 0.956153015,
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  "morph_per_feat": {
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  "Case": {
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- "p": 0.9214166773,
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- "r": 0.9216527867,
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  "Gender": {
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  "AdpType": {
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  "p": 0.9994665244,
@@ -30,39 +30,39 @@
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  "f": 0.9990667911
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@@ -70,99 +70,99 @@
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@@ -175,276 +175,271 @@
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@@ -466,63 +466,63 @@
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131
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166
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167
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175
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177
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178
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363
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@@ -81,8 +81,8 @@ nO = null
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83
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88
  [components.ner.model.tok2vec.encode]
@@ -158,8 +158,8 @@ normalize = false
158
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159
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163
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165
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@@ -178,8 +178,8 @@ factory = "tok2vec"
178
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179
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180
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181
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182
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185
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@@ -221,6 +221,7 @@ eval_frequency = 1000
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2189
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  {
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  "lang":"pl",
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  "name":"core_news_lg",
4
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5
  "description":"Polish pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, lemmatizer (trainable_lemmatizer), tagger, senter, ner.",
6
  "author":"Explosion",
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  "email":"[email protected]",
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1780
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1912
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1947
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1948
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2144
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2250
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