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pl_core_news_md

Token Classification
spaCy
Polish
Eval Results
Model card Files
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adrianeboyd commited on
Commit
6cd4e04
·
1 Parent(s): ab2ca28

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_md-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: 0.838028169
18
  - name: NER Recall
19
  type: recall
20
- value: 0.8342893563
21
  - name: NER F Score
22
  type: f_score
23
- value: 0.8361545832
24
  - task:
25
  name: TAG
26
  type: token-classification
27
  metrics:
28
  - name: TAG (XPOS) Accuracy
29
  type: accuracy
30
- value: 0.9824705811
31
  - task:
32
  name: POS
33
  type: token-classification
34
  metrics:
35
  - name: POS (UPOS) Accuracy
36
  type: accuracy
37
- value: 0.9767367565
38
  - task:
39
  name: MORPH
40
  type: token-classification
41
  metrics:
42
  - name: Morph (UFeats) Accuracy
43
  type: accuracy
44
- value: 0.8970513509
45
  - task:
46
  name: LEMMA
47
  type: token-classification
48
  metrics:
49
  - name: Lemma Accuracy
50
  type: accuracy
51
- value: 0.9366919628
52
  - task:
53
  name: UNLABELED_DEPENDENCIES
54
  type: token-classification
55
  metrics:
56
  - name: Unlabeled Attachment Score (UAS)
57
  type: f_score
58
- value: 0.8919060234
59
  - task:
60
  name: LABELED_DEPENDENCIES
61
  type: token-classification
62
  metrics:
63
  - name: Labeled Attachment Score (LAS)
64
  type: f_score
65
- value: 0.8162603893
66
  - task:
67
  name: SENTS
68
  type: token-classification
69
  metrics:
70
  - name: Sentences F-Score
71
  type: f_score
72
- value: 0.9624604251
73
  ---
74
  ### Details: https://spacy.io/models/pl#pl_core_news_md
75
 
@@ -78,8 +78,8 @@ Polish pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, l
78
  | Feature | Description |
79
  | --- | --- |
80
  | **Name** | `pl_core_news_md` |
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, 20000 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` | 97.67 |
114
- | `MORPH_ACC` | 89.71 |
115
- | `MORPH_MICRO_P` | 94.96 |
116
- | `MORPH_MICRO_R` | 94.78 |
117
- | `MORPH_MICRO_F` | 94.87 |
118
- | `SENTS_P` | 96.42 |
119
- | `SENTS_R` | 96.07 |
120
- | `SENTS_F` | 96.25 |
121
- | `DEP_UAS` | 89.19 |
122
- | `DEP_LAS` | 81.63 |
123
- | `LEMMA_ACC` | 93.67 |
124
- | `TAG_ACC` | 98.25 |
125
- | `ENTS_P` | 83.80 |
126
- | `ENTS_R` | 83.43 |
127
- | `ENTS_F` | 83.62 |
 
14
  metrics:
15
  - name: NER Precision
16
  type: precision
17
+ value: 0.828425096
18
  - name: NER Recall
19
  type: recall
20
+ value: 0.8247291268
21
  - name: NER F Score
22
  type: f_score
23
+ value: 0.8265729799
24
  - task:
25
  name: TAG
26
  type: token-classification
27
  metrics:
28
  - name: TAG (XPOS) Accuracy
29
  type: accuracy
30
+ value: 0.9816474605
31
  - task:
32
  name: POS
33
  type: token-classification
34
  metrics:
35
  - name: POS (UPOS) Accuracy
36
  type: accuracy
37
+ value: 0.9763909862
38
  - task:
39
  name: MORPH
40
  type: token-classification
41
  metrics:
42
  - name: Morph (UFeats) Accuracy
43
  type: accuracy
44
+ value: 0.8950409384
45
  - task:
46
  name: LEMMA
47
  type: token-classification
48
  metrics:
49
  - name: Lemma Accuracy
50
  type: accuracy
51
+ value: 0.9361695953
52
  - task:
53
  name: UNLABELED_DEPENDENCIES
54
  type: token-classification
55
  metrics:
56
  - name: Unlabeled Attachment Score (UAS)
57
  type: f_score
58
+ value: 0.8889276191
59
  - task:
60
  name: LABELED_DEPENDENCIES
61
  type: token-classification
62
  metrics:
63
  - name: Labeled Attachment Score (LAS)
64
  type: f_score
65
+ value: 0.8139397108
66
  - task:
67
  name: SENTS
68
  type: token-classification
69
  metrics:
70
  - name: Sentences F-Score
71
  type: f_score
72
+ value: 0.9633152174
73
  ---
74
  ### Details: https://spacy.io/models/pl#pl_core_news_md
75
 
 
78
  | Feature | Description |
79
  | --- | --- |
80
  | **Name** | `pl_core_news_md` |
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, 20000 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` | 97.64 |
114
+ | `MORPH_ACC` | 89.50 |
115
+ | `MORPH_MICRO_P` | 94.87 |
116
+ | `MORPH_MICRO_R` | 94.74 |
117
+ | `MORPH_MICRO_F` | 94.81 |
118
+ | `SENTS_P` | 96.64 |
119
+ | `SENTS_R` | 96.03 |
120
+ | `SENTS_F` | 96.33 |
121
+ | `DEP_UAS` | 88.89 |
122
+ | `DEP_LAS` | 81.39 |
123
+ | `LEMMA_ACC` | 93.62 |
124
+ | `TAG_ACC` | 98.16 |
125
+ | `ENTS_P` | 82.84 |
126
+ | `ENTS_R` | 82.47 |
127
+ | `ENTS_F` | 82.66 |
accuracy.json CHANGED
@@ -1,330 +1,315 @@
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167
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298
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313
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config.cfg CHANGED
@@ -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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160
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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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182
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183
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185
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@@ -221,6 +221,7 @@ eval_frequency = 1000
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2245
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2246
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2247
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2248
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2249
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2250
  "xcomp:subj":{
2251
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@@ -2257,55 +2247,65 @@
2257
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2258
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2259
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2260
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2261
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2262
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2263
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2264
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2265
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2266
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2267
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2268
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2269
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2270
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2271
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2272
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2273
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2274
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2275
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2276
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2277
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2278
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2279
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2280
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2281
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2282
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2283
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2284
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2285
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2286
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2291
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2292
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2293
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2296
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2298
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2300
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2301
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2302
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2303
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2304
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2305
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2306
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2307
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2308
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2309
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2310
  "sources":[
2311
  {
 
1
  {
2
  "lang":"pl",
3
  "name":"core_news_md",
4
+ "version":"3.5.0",
5
  "description":"Polish pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, lemmatizer (trainable_lemmatizer), tagger, senter, ner.",
6
  "author":"Explosion",
7
  "email":"[email protected]",
8
  "url":"https://explosion.ai",
9
  "license":"GNU GPL 3.0",
10
+ "spacy_version":">=3.5.0,<3.6.0",
11
+ "spacy_git_version":"9e0322de1",
12
  "vectors":{
13
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14
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1780
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1781
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1782
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1783
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1784
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1785
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1786
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1787
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1788
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1789
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1790
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1791
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1792
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1793
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1794
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1795
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1796
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1797
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1798
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1799
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1800
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1801
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1802
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1803
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1804
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1805
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1806
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1807
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1808
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1809
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1810
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1811
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1812
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1813
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1814
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1815
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1816
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1817
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1818
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1819
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1820
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1821
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1822
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1823
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1824
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1825
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1826
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1827
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1828
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1830
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1831
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1832
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1833
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1835
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1836
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1837
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1838
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1840
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1842
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1843
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1845
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1847
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1848
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1850
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1851
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1852
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1853
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1857
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1858
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1860
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1862
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1863
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1864
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1867
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1868
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1870
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1872
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1873
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1877
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1878
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1880
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1881
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1882
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1883
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1887
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1888
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1890
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1891
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1892
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1893
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1894
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1895
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1896
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1897
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1898
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1899
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1900
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1901
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1902
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1903
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1904
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1905
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1907
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1908
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1910
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1911
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1912
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1913
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1914
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1915
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1916
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1917
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1918
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1919
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1920
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1921
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1922
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1923
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1924
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1925
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1926
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1927
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1928
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1929
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1930
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1931
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1932
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1933
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1935
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1936
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1937
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1938
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1942
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1943
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1944
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1945
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1946
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1947
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1948
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1949
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1950
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1951
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1952
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1953
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1954
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1955
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1956
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1957
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1958
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1959
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1960
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1961
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1962
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1963
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1964
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1966
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1967
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1969
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1970
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1971
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1972
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1973
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1974
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1975
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1976
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1977
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1978
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1979
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1980
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1981
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1982
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1983
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1984
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1985
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1986
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1987
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1988
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1989
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1990
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1991
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1992
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1993
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1994
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1995
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1996
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1997
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1998
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1999
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2000
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2001
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2002
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2003
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2004
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2005
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2007
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2008
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2009
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2010
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2011
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2012
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2013
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2014
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2015
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2016
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2017
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2018
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2019
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2020
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2021
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2022
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2023
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2024
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2025
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2026
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2027
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2028
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2029
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2030
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2031
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2032
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2033
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2034
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2035
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2036
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2037
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2038
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2039
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2040
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2041
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2042
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2043
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2044
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2045
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2046
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2047
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2048
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2049
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2050
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2051
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2052
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2053
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2054
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2055
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2056
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2057
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2058
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2059
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2060
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2061
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2062
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2063
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2064
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2065
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2066
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2067
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2068
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2069
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2070
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2071
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2072
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2073
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2074
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2075
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2076
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2077
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2078
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2079
  },
2080
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2081
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2082
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2083
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2084
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2085
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2086
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2087
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2088
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2089
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2090
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2091
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2092
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2093
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2094
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2095
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2096
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2103
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2104
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2105
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2106
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2107
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2108
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2109
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2110
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2111
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2112
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2113
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2114
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2115
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2116
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2117
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2118
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2119
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2120
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2121
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2122
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2123
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2124
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2125
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2126
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2127
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2128
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2129
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2130
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2131
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2132
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2133
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2134
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2135
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2136
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2137
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2138
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2139
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2140
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2141
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2142
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2143
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2144
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2145
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2146
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2147
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2148
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2149
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2150
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2151
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2152
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2153
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2154
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2157
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2158
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2159
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2160
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2161
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2162
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2164
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2165
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2166
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2167
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2168
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2169
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2170
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2172
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2173
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2174
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2175
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2177
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2178
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2179
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2180
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2182
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2183
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2184
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2185
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2186
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2187
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2188
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2189
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2190
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2191
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2193
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2194
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2195
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2196
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2197
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2198
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2199
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2200
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2201
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2202
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2204
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2205
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2209
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2210
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2220
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2221
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2223
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2224
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2225
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2228
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2229
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2230
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2231
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2234
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2235
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2239
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2240
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2247
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2250
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2252
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2253
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2254
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2255
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2256
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2257
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2258
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2259
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2260
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2264
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2270
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2301
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