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ja_core_news_lg

Token Classification
spaCy
Japanese
Eval Results
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osanseviero HF staff commited on
Commit
16a00d2
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1 Parent(s): 2f459d5

Update spaCy pipeline

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Files changed (18) hide show
  1. LICENSES_SOURCES +2 -2
  2. README.md +36 -28
  3. accuracy.json +161 -144
  4. attribute_ruler/patterns +0 -0
  5. config.cfg +47 -18
  6. ja_core_news_lg-any-py3-none-any.whl +2 -2
  7. meta.json +190 -150
  8. morphologizer/cfg +46 -0
  9. morphologizer/model +3 -0
  10. ner/model +0 -0
  11. ner/moves +1 -1
  12. parser/model +0 -0
  13. parser/moves +1 -1
  14. senter/cfg +1 -1
  15. senter/model +0 -0
  16. tok2vec/model +0 -0
  17. vocab/strings.json +2 -2
  18. vocab/vectors.cfg +3 -0
LICENSES_SOURCES CHANGED
@@ -1,4 +1,4 @@
1
- # UD Japanese GSD v2.6
2
 
3
  * Author: Omura, Mai; Miyao, Yusuke; Kanayama, Hiroshi; Matsuda, Hiroshi; Wakasa, Aya; Yamashita, Kayo; Asahara, Masayuki; Tanaka, Takaaki; Murawaki, Yugo; Matsumoto, Yuji; Mori, Shinsuke; Uematsu, Sumire; McDonald, Ryan; Nivre, Joakim; Zeman, Daniel
4
  * URL: https://github.com/UniversalDependencies/UD_Japanese-GSD
@@ -438,7 +438,7 @@ Creative Commons may be contacted at creativecommons.org.
438
 
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- # UD Japanese GSD v2.6 NER
442
 
443
  * Author: Megagon Labs Tokyo
444
  * URL: https://github.com/megagonlabs/UD_Japanese-GSD
 
1
+ # UD Japanese GSD v2.8
2
 
3
  * Author: Omura, Mai; Miyao, Yusuke; Kanayama, Hiroshi; Matsuda, Hiroshi; Wakasa, Aya; Yamashita, Kayo; Asahara, Masayuki; Tanaka, Takaaki; Murawaki, Yugo; Matsumoto, Yuji; Mori, Shinsuke; Uematsu, Sumire; McDonald, Ryan; Nivre, Joakim; Zeman, Daniel
4
  * URL: https://github.com/UniversalDependencies/UD_Japanese-GSD
 
438
 
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440
 
441
+ # UD Japanese GSD v2.8 NER
442
 
443
  * Author: Megagon Labs Tokyo
444
  * URL: https://github.com/megagonlabs/UD_Japanese-GSD
README.md CHANGED
@@ -4,7 +4,7 @@ tags:
4
  - token-classification
5
  language:
6
  - ja
7
- license: CC-BY-SA-4.0
8
  model-index:
9
  - name: ja_core_news_lg
10
  results:
@@ -14,61 +14,61 @@ model-index:
14
  metrics:
15
  - name: NER Precision
16
  type: precision
17
- value: 0.760989011
18
  - name: NER Recall
19
  type: recall
20
- value: 0.7075351213
21
  - name: NER F Score
22
  type: f_score
23
- value: 0.7332892124
24
  - task:
25
  name: POS
26
  type: token-classification
27
  metrics:
28
  - name: POS Accuracy
29
  type: accuracy
30
- value: 0.9721899386
31
  - task:
32
  name: SENTER
33
  type: token-classification
34
  metrics:
35
  - name: SENTER Precision
36
  type: precision
37
- value: 0.9860557769
38
  - name: SENTER Recall
39
  type: recall
40
- value: 0.9880239521
41
  - name: SENTER F Score
42
  type: f_score
43
- value: 0.9870388833
44
  - task:
45
  name: UNLABELED_DEPENDENCIES
46
  type: token-classification
47
  metrics:
48
  - name: Unlabeled Dependencies Accuracy
49
  type: accuracy
50
- value: 0.9181002928
51
  - task:
52
  name: LABELED_DEPENDENCIES
53
  type: token-classification
54
  metrics:
55
  - name: Labeled Dependencies Accuracy
56
  type: accuracy
57
- value: 0.9181002928
58
  ---
59
  ### Details: https://spacy.io/models/ja#ja_core_news_lg
60
 
61
- Japanese pipeline optimized for CPU. Components: tok2vec, parser, senter, ner, attribute_ruler.
62
 
63
  | Feature | Description |
64
  | --- | --- |
65
  | **Name** | `ja_core_news_lg` |
66
- | **Version** | `3.1.0` |
67
- | **spaCy** | `>=3.1.0,<3.2.0` |
68
- | **Default Pipeline** | `tok2vec`, `parser`, `attribute_ruler`, `ner` |
69
- | **Components** | `tok2vec`, `parser`, `senter`, `attribute_ruler`, `ner` |
70
  | **Vectors** | 480443 keys, 480443 unique vectors (300 dimensions) |
71
- | **Sources** | [UD Japanese GSD v2.6](https://github.com/UniversalDependencies/UD_Japanese-GSD) (Omura, Mai; Miyao, Yusuke; Kanayama, Hiroshi; Matsuda, Hiroshi; Wakasa, Aya; Yamashita, Kayo; Asahara, Masayuki; Tanaka, Takaaki; Murawaki, Yugo; Matsumoto, Yuji; Mori, Shinsuke; Uematsu, Sumire; McDonald, Ryan; Nivre, Joakim; Zeman, Daniel)<br />[UD Japanese GSD v2.6 NER](https://github.com/megagonlabs/UD_Japanese-GSD) (Megagon Labs Tokyo)<br />[chiVe: Japanese Word Embedding with Sudachi & NWJC (chive-1.1-mc90-500k)](https://github.com/WorksApplications/chiVe) (Works Applications) |
72
  | **License** | `CC BY-SA 4.0` |
73
  | **Author** | [Explosion](https://explosion.ai) |
74
 
@@ -76,10 +76,11 @@ Japanese pipeline optimized for CPU. Components: tok2vec, parser, senter, ner, a
76
 
77
  <details>
78
 
79
- <summary>View label scheme (47 labels for 3 components)</summary>
80
 
81
  | Component | Labels |
82
  | --- | --- |
 
83
  | **`parser`** | `ROOT`, `acl`, `advcl`, `advmod`, `amod`, `aux`, `case`, `cc`, `ccomp`, `compound`, `cop`, `csubj`, `dep`, `det`, `dislocated`, `fixed`, `mark`, `nmod`, `nsubj`, `nummod`, `obj`, `obl`, `punct` |
84
  | **`senter`** | `I`, `S` |
85
  | **`ner`** | `CARDINAL`, `DATE`, `EVENT`, `FAC`, `GPE`, `LANGUAGE`, `LAW`, `LOC`, `MONEY`, `MOVEMENT`, `NORP`, `ORDINAL`, `ORG`, `PERCENT`, `PERSON`, `PET_NAME`, `PHONE`, `PRODUCT`, `QUANTITY`, `TIME`, `TITLE_AFFIX`, `WORK_OF_ART` |
@@ -91,14 +92,21 @@ Japanese pipeline optimized for CPU. Components: tok2vec, parser, senter, ner, a
91
  | Type | Score |
92
  | --- | --- |
93
  | `TOKEN_ACC` | 99.69 |
94
- | `TAG_ACC` | 97.22 |
95
- | `POS_ACC` | 96.40 |
96
- | `MORPH_ACC` | 0.00 |
97
- | `DEP_UAS` | 91.81 |
98
- | `DEP_LAS` | 89.98 |
99
- | `ENTS_P` | 76.10 |
100
- | `ENTS_R` | 70.75 |
101
- | `ENTS_F` | 73.33 |
102
- | `SENTS_P` | 98.61 |
103
- | `SENTS_R` | 98.80 |
104
- | `SENTS_F` | 98.70 |
 
 
 
 
 
 
 
 
4
  - token-classification
5
  language:
6
  - ja
7
+ license: cc-by-sa-4.0
8
  model-index:
9
  - name: ja_core_news_lg
10
  results:
 
14
  metrics:
15
  - name: NER Precision
16
  type: precision
17
+ value: 0.7402422611
18
  - name: NER Recall
19
  type: recall
20
+ value: 0.6918238994
21
  - name: NER F Score
22
  type: f_score
23
+ value: 0.7152145644
24
  - task:
25
  name: POS
26
  type: token-classification
27
  metrics:
28
  - name: POS Accuracy
29
  type: accuracy
30
+ value: 0.9715755942
31
  - task:
32
  name: SENTER
33
  type: token-classification
34
  metrics:
35
  - name: SENTER Precision
36
  type: precision
37
+ value: 0.9862204724
38
  - name: SENTER Recall
39
  type: recall
40
+ value: 0.9881656805
41
  - name: SENTER F Score
42
  type: f_score
43
+ value: 0.9871921182
44
  - task:
45
  name: UNLABELED_DEPENDENCIES
46
  type: token-classification
47
  metrics:
48
  - name: Unlabeled Dependencies Accuracy
49
  type: accuracy
50
+ value: 0.9214150689
51
  - task:
52
  name: LABELED_DEPENDENCIES
53
  type: token-classification
54
  metrics:
55
  - name: Labeled Dependencies Accuracy
56
  type: accuracy
57
+ value: 0.9214150689
58
  ---
59
  ### Details: https://spacy.io/models/ja#ja_core_news_lg
60
 
61
+ Japanese pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute_ruler.
62
 
63
  | Feature | Description |
64
  | --- | --- |
65
  | **Name** | `ja_core_news_lg` |
66
+ | **Version** | `3.2.0` |
67
+ | **spaCy** | `>=3.2.0,<3.3.0` |
68
+ | **Default Pipeline** | `tok2vec`, `morphologizer`, `parser`, `attribute_ruler`, `ner` |
69
+ | **Components** | `tok2vec`, `morphologizer`, `parser`, `senter`, `attribute_ruler`, `ner` |
70
  | **Vectors** | 480443 keys, 480443 unique vectors (300 dimensions) |
71
+ | **Sources** | [UD Japanese GSD v2.8](https://github.com/UniversalDependencies/UD_Japanese-GSD) (Omura, Mai; Miyao, Yusuke; Kanayama, Hiroshi; Matsuda, Hiroshi; Wakasa, Aya; Yamashita, Kayo; Asahara, Masayuki; Tanaka, Takaaki; Murawaki, Yugo; Matsumoto, Yuji; Mori, Shinsuke; Uematsu, Sumire; McDonald, Ryan; Nivre, Joakim; Zeman, Daniel)<br />[UD Japanese GSD v2.8 NER](https://github.com/megagonlabs/UD_Japanese-GSD) (Megagon Labs Tokyo)<br />[chiVe: Japanese Word Embedding with Sudachi & NWJC (chive-1.1-mc90-500k)](https://github.com/WorksApplications/chiVe) (Works Applications) |
72
  | **License** | `CC BY-SA 4.0` |
73
  | **Author** | [Explosion](https://explosion.ai) |
74
 
 
76
 
77
  <details>
78
 
79
+ <summary>View label scheme (66 labels for 4 components)</summary>
80
 
81
  | Component | Labels |
82
  | --- | --- |
83
+ | **`morphologizer`** | `POS=NOUN`, `POS=ADP`, `POS=VERB`, `POS=SCONJ`, `POS=AUX`, `POS=PUNCT`, `POS=PART`, `POS=DET`, `POS=NUM`, `POS=ADV`, `POS=PRON`, `POS=ADJ`, `POS=PROPN`, `POS=CCONJ`, `POS=SYM`, `POS=NOUN\|Polarity=Neg`, `POS=AUX\|Polarity=Neg`, `POS=INTJ`, `POS=SCONJ\|Polarity=Neg` |
84
  | **`parser`** | `ROOT`, `acl`, `advcl`, `advmod`, `amod`, `aux`, `case`, `cc`, `ccomp`, `compound`, `cop`, `csubj`, `dep`, `det`, `dislocated`, `fixed`, `mark`, `nmod`, `nsubj`, `nummod`, `obj`, `obl`, `punct` |
85
  | **`senter`** | `I`, `S` |
86
  | **`ner`** | `CARDINAL`, `DATE`, `EVENT`, `FAC`, `GPE`, `LANGUAGE`, `LAW`, `LOC`, `MONEY`, `MOVEMENT`, `NORP`, `ORDINAL`, `ORG`, `PERCENT`, `PERSON`, `PET_NAME`, `PHONE`, `PRODUCT`, `QUANTITY`, `TIME`, `TITLE_AFFIX`, `WORK_OF_ART` |
 
92
  | Type | Score |
93
  | --- | --- |
94
  | `TOKEN_ACC` | 99.69 |
95
+ | `TOKEN_P` | 97.65 |
96
+ | `TOKEN_R` | 97.90 |
97
+ | `TOKEN_F` | 97.77 |
98
+ | `POS_ACC` | 97.36 |
99
+ | `MORPH_ACC` | 0.40 |
100
+ | `MORPH_MICRO_P` | 34.01 |
101
+ | `MORPH_MICRO_R` | 98.04 |
102
+ | `MORPH_MICRO_F` | 50.51 |
103
+ | `SENTS_P` | 98.62 |
104
+ | `SENTS_R` | 98.82 |
105
+ | `SENTS_F` | 98.72 |
106
+ | `DEP_UAS` | 92.14 |
107
+ | `DEP_LAS` | 90.81 |
108
+ | `TAG_ACC` | 97.16 |
109
+ | `LEMMA_ACC` | 96.59 |
110
+ | `ENTS_P` | 74.02 |
111
+ | `ENTS_R` | 69.18 |
112
+ | `ENTS_F` | 71.52 |
accuracy.json CHANGED
@@ -1,222 +1,243 @@
1
  {
2
- "token_acc": 0.9968965945,
3
- "tag_acc": 0.9721899386,
4
- "pos_acc": 0.9639755682,
5
- "morph_acc": 0.0,
6
- "dep_uas": 0.9181002928,
7
- "dep_las": 0.8998080263,
8
- "ents_p": 0.760989011,
9
- "ents_r": 0.7075351213,
10
- "ents_f": 0.7332892124,
11
- "sents_p": 0.9860557769,
12
- "sents_r": 0.9880239521,
13
- "sents_f": 0.9870388833,
14
- "speed": 11674.7452722222,
15
  "morph_per_feat": {
16
  "Polarity": {
 
 
 
 
 
 
 
 
 
 
17
  "p": 0.0,
18
  "r": 0.0,
19
  "f": 0.0
20
  }
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  },
 
 
 
 
 
22
  "dep_las_per_type": {
23
  "cc": {
24
- "p": 0.7872340426,
25
- "r": 0.8043478261,
26
- "f": 0.7956989247
27
- },
28
- "nummod": {
29
- "p": 0.9770114943,
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- "r": 0.8762886598,
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- "f": 0.9239130435
32
  },
33
  "compound": {
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- "p": 0.9397972117,
35
- "r": 0.9205462446,
36
- "f": 0.9300721229
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  },
38
  "obl": {
39
- "p": 0.7827102804,
40
- "r": 0.8052884615,
41
- "f": 0.7938388626
42
  },
43
  "case": {
44
- "p": 0.986533282,
45
- "r": 0.9789996182,
46
- "f": 0.9827520123
47
  },
48
  "dislocated": {
49
  "p": 0.5,
50
- "r": 0.2105263158,
51
- "f": 0.2962962963
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- },
53
- "nmod": {
54
- "p": 0.8676092545,
55
- "r": 0.813253012,
56
- "f": 0.8395522388
57
  },
58
  "nsubj": {
59
- "p": 0.7950819672,
60
- "r": 0.8083333333,
61
- "f": 0.8016528926
 
 
 
 
 
62
  },
63
  "root": {
64
- "p": 0.9717171717,
65
- "r": 0.9600798403,
66
- "f": 0.9658634538
67
  },
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  "aux": {
69
- "p": 0.9625090123,
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- "r": 0.9673913043,
71
- "f": 0.9649439827
72
  },
73
  "advcl": {
74
- "p": 0.6802884615,
75
- "r": 0.6596736597,
76
- "f": 0.6698224852
77
  },
78
  "mark": {
79
- "p": 0.956,
80
- "r": 0.9409448819,
81
- "f": 0.9484126984
 
 
 
 
 
82
  },
83
  "acl": {
84
- "p": 0.7887931034,
85
- "r": 0.8061674009,
86
- "f": 0.7973856209
87
  },
88
  "obj": {
89
- "p": 0.950617284,
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- "r": 0.9390243902,
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- "f": 0.9447852761
92
  },
93
- "fixed": {
94
- "p": 0.9421052632,
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- "r": 0.9835164835,
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- "f": 0.9623655914
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  },
98
  "advmod": {
99
- "p": 0.7045454545,
100
- "r": 0.4920634921,
101
- "f": 0.5794392523
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  },
103
  "amod": {
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- "p": 0.8888888889,
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- "r": 0.6,
106
- "f": 0.7164179104
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  },
108
  "cop": {
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- "p": 0.9664804469,
110
- "r": 0.9505494505,
111
- "f": 0.9584487535
112
  },
113
  "ccomp": {
114
- "p": 0.9,
115
  "r": 0.8181818182,
116
- "f": 0.8571428571
 
 
 
 
 
117
  },
118
  "det": {
119
- "p": 0.9803921569,
120
- "r": 0.9803921569,
121
- "f": 0.9803921569
122
  },
123
  "dep": {
124
- "p": 0.0,
125
- "r": 0.0,
126
- "f": 0.0
127
- },
128
- "csubj": {
129
- "p": 0.8333333333,
130
- "r": 0.7692307692,
131
- "f": 0.8
132
  }
133
  },
 
 
 
 
 
134
  "ents_per_type": {
135
  "DATE": {
136
- "p": 0.9626168224,
137
- "r": 0.9537037037,
138
- "f": 0.9581395349
139
  },
140
  "ORG": {
141
- "p": 0.6637931034,
142
- "r": 0.5877862595,
143
- "f": 0.6234817814
144
  },
145
  "PERSON": {
146
- "p": 0.780141844,
147
- "r": 0.7913669065,
148
- "f": 0.7857142857
149
  },
150
  "GPE": {
151
- "p": 0.6956521739,
152
- "r": 0.6808510638,
153
- "f": 0.688172043
154
- },
155
- "EVENT": {
156
- "p": 0.6666666667,
157
- "r": 0.6153846154,
158
- "f": 0.64
159
  },
160
- "PRODUCT": {
161
- "p": 0.5666666667,
162
- "r": 0.4146341463,
163
- "f": 0.4788732394
164
  },
165
  "TIME": {
166
  "p": 0.6666666667,
167
  "r": 1.0,
168
  "f": 0.8
169
  },
170
- "QUANTITY": {
171
- "p": 0.8970588235,
172
- "r": 0.9242424242,
173
- "f": 0.9104477612
174
- },
175
  "NORP": {
176
- "p": 0.7037037037,
177
- "r": 0.59375,
178
- "f": 0.6440677966
179
- },
180
- "TITLE_AFFIX": {
181
- "p": 0.8571428571,
182
- "r": 0.6,
183
- "f": 0.7058823529
184
  },
185
  "ORDINAL": {
186
- "p": 0.65,
187
- "r": 0.6842105263,
188
- "f": 0.6666666667
 
 
 
 
 
189
  },
190
  "WORK_OF_ART": {
191
- "p": 0.6875,
192
- "r": 0.6470588235,
193
- "f": 0.6666666667
194
  },
195
- "FAC": {
196
- "p": 0.5769230769,
197
- "r": 0.4054054054,
198
- "f": 0.4761904762
199
  },
200
  "PERCENT": {
201
  "p": 1.0,
202
- "r": 0.4285714286,
203
- "f": 0.6
 
 
 
 
 
 
 
 
 
 
204
  },
205
  "LOC": {
206
- "p": 0.6,
207
- "r": 0.9,
208
- "f": 0.72
209
  },
210
  "MOVEMENT": {
211
- "p": 0.3333333333,
212
- "r": 0.2,
213
- "f": 0.25
214
- },
215
- "LAW": {
216
  "p": 0.0,
217
  "r": 0.0,
218
  "f": 0.0
219
  },
 
 
 
 
 
 
 
 
 
 
220
  "MONEY": {
221
  "p": 1.0,
222
  "r": 1.0,
@@ -226,11 +247,7 @@
226
  "p": 1.0,
227
  "r": 1.0,
228
  "f": 1.0
229
- },
230
- "CARDINAL": {
231
- "p": 0.0,
232
- "r": 0.0,
233
- "f": 0.0
234
  }
235
- }
 
236
  }
 
1
  {
2
+ "token_acc": 0.9968649485,
3
+ "token_p": 0.9764591282,
4
+ "token_r": 0.9790021974,
5
+ "token_f": 0.9777290092,
6
+ "pos_acc": 0.9736163946,
7
+ "morph_acc": 0.0040005162,
8
+ "morph_micro_p": 0.3401360544,
9
+ "morph_micro_r": 0.9803921569,
10
+ "morph_micro_f": 0.5050505051,
 
 
 
 
11
  "morph_per_feat": {
12
  "Polarity": {
13
+ "p": 1.0,
14
+ "r": 0.9803921569,
15
+ "f": 0.9900990099
16
+ },
17
+ "Inflection": {
18
+ "p": 0.0,
19
+ "r": 0.0,
20
+ "f": 0.0
21
+ },
22
+ "Reading": {
23
  "p": 0.0,
24
  "r": 0.0,
25
  "f": 0.0
26
  }
27
  },
28
+ "sents_p": 0.9862204724,
29
+ "sents_r": 0.9881656805,
30
+ "sents_f": 0.9871921182,
31
+ "dep_uas": 0.9214150689,
32
+ "dep_las": 0.9080930316,
33
  "dep_las_per_type": {
34
  "cc": {
35
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attribute_ruler/patterns CHANGED
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config.cfg CHANGED
@@ -1,10 +1,8 @@
1
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2
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3
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4
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13
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14
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15
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16
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18
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28
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29
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30
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31
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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33
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34
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66
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67
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68
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69
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70
 
71
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84
 
85
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86
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87
 
88
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89
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@@ -130,17 +148,17 @@ maxout_pieces = 3
130
 
131
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144
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205
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336
  "author":"Omura, Mai; Miyao, Yusuke; Kanayama, Hiroshi; Matsuda, Hiroshi; Wakasa, Aya; Yamashita, Kayo; Asahara, Masayuki; Tanaka, Takaaki; Murawaki, Yugo; Matsumoto, Yuji; Mori, Shinsuke; Uematsu, Sumire; McDonald, Ryan; Nivre, Joakim; Zeman, Daniel"
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