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en_core_web_sm

Token Classification
spaCy
English
Eval Results
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Adriane Boyd commited on
Commit
07168d0
·
1 Parent(s): e3b3969

Update spaCy pipeline

Browse files
Files changed (14) hide show
  1. README.md +20 -20
  2. accuracy.json +200 -200
  3. en_core_web_sm-any-py3-none-any.whl +2 -2
  4. meta.json +204 -203
  5. ner/model +1 -1
  6. ner/moves +1 -1
  7. parser/model +1 -1
  8. parser/moves +1 -2
  9. senter/model +1 -1
  10. tagger/cfg +1 -0
  11. tagger/model +2 -2
  12. tok2vec/model +1 -1
  13. tokenizer +0 -0
  14. vocab/strings.json +2 -2
README.md CHANGED
@@ -14,41 +14,41 @@ model-index:
14
  metrics:
15
  - name: NER Precision
16
  type: precision
17
- value: 0.8508041869
18
  - name: NER Recall
19
  type: recall
20
- value: 0.8344851763
21
  - name: NER F Score
22
  type: f_score
23
- value: 0.8425656714
24
  - task:
25
  name: TAG
26
  type: token-classification
27
  metrics:
28
  - name: TAG (XPOS) Accuracy
29
  type: accuracy
30
- value: 0.9726545475
31
  - task:
32
  name: UNLABELED_DEPENDENCIES
33
  type: token-classification
34
  metrics:
35
  - name: Unlabeled Attachment Score (UAS)
36
  type: f_score
37
- value: 0.9180803841
38
  - task:
39
  name: LABELED_DEPENDENCIES
40
  type: token-classification
41
  metrics:
42
  - name: Labeled Attachment Score (LAS)
43
  type: f_score
44
- value: 0.8996666011
45
  - task:
46
  name: SENTS
47
  type: token-classification
48
  metrics:
49
  - name: Sentences F-Score
50
  type: f_score
51
- value: 0.9060200669
52
  ---
53
  ### Details: https://spacy.io/models/en#en_core_web_sm
54
 
@@ -57,8 +57,8 @@ English pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter,
57
  | Feature | Description |
58
  | --- | --- |
59
  | **Name** | `en_core_web_sm` |
60
- | **Version** | `3.3.0` |
61
- | **spaCy** | `>=3.3.0.dev0,<3.4.0` |
62
  | **Default Pipeline** | `tok2vec`, `tagger`, `parser`, `attribute_ruler`, `lemmatizer`, `ner` |
63
  | **Components** | `tok2vec`, `tagger`, `parser`, `senter`, `attribute_ruler`, `lemmatizer`, `ner` |
64
  | **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
@@ -70,11 +70,11 @@ English pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter,
70
 
71
  <details>
72
 
73
- <summary>View label scheme (112 labels for 3 components)</summary>
74
 
75
  | Component | Labels |
76
  | --- | --- |
77
- | **`tagger`** | `$`, `''`, `,`, `-LRB-`, `-RRB-`, `.`, `:`, `ADD`, `AFX`, `CC`, `CD`, `DT`, `EX`, `FW`, `HYPH`, `IN`, `JJ`, `JJR`, `JJS`, `LS`, `MD`, `NFP`, `NN`, `NNP`, `NNPS`, `NNS`, `PDT`, `POS`, `PRP`, `PRP$`, `RB`, `RBR`, `RBS`, `RP`, `SYM`, `TO`, `UH`, `VB`, `VBD`, `VBG`, `VBN`, `VBP`, `VBZ`, `WDT`, `WP`, `WP$`, `WRB`, `XX`, ```` |
78
  | **`parser`** | `ROOT`, `acl`, `acomp`, `advcl`, `advmod`, `agent`, `amod`, `appos`, `attr`, `aux`, `auxpass`, `case`, `cc`, `ccomp`, `compound`, `conj`, `csubj`, `csubjpass`, `dative`, `dep`, `det`, `dobj`, `expl`, `intj`, `mark`, `meta`, `neg`, `nmod`, `npadvmod`, `nsubj`, `nsubjpass`, `nummod`, `oprd`, `parataxis`, `pcomp`, `pobj`, `poss`, `preconj`, `predet`, `prep`, `prt`, `punct`, `quantmod`, `relcl`, `xcomp` |
79
  | **`ner`** | `CARDINAL`, `DATE`, `EVENT`, `FAC`, `GPE`, `LANGUAGE`, `LAW`, `LOC`, `MONEY`, `NORP`, `ORDINAL`, `ORG`, `PERCENT`, `PERSON`, `PRODUCT`, `QUANTITY`, `TIME`, `WORK_OF_ART` |
80
 
@@ -88,12 +88,12 @@ English pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter,
88
  | `TOKEN_P` | 99.57 |
89
  | `TOKEN_R` | 99.58 |
90
  | `TOKEN_F` | 99.57 |
91
- | `TAG_ACC` | 97.27 |
92
- | `SENTS_P` | 91.89 |
93
- | `SENTS_R` | 89.35 |
94
- | `SENTS_F` | 90.60 |
95
- | `DEP_UAS` | 91.81 |
96
- | `DEP_LAS` | 89.97 |
97
- | `ENTS_P` | 85.08 |
98
- | `ENTS_R` | 83.45 |
99
- | `ENTS_F` | 84.26 |
 
14
  metrics:
15
  - name: NER Precision
16
  type: precision
17
+ value: 0.8565043157
18
  - name: NER Recall
19
  type: recall
20
+ value: 0.8348858173
21
  - name: NER F Score
22
  type: f_score
23
+ value: 0.8455569081
24
  - task:
25
  name: TAG
26
  type: token-classification
27
  metrics:
28
  - name: TAG (XPOS) Accuracy
29
  type: accuracy
30
+ value: 0.9726250474
31
  - task:
32
  name: UNLABELED_DEPENDENCIES
33
  type: token-classification
34
  metrics:
35
  - name: Unlabeled Attachment Score (UAS)
36
  type: f_score
37
+ value: 0.9165718428
38
  - task:
39
  name: LABELED_DEPENDENCIES
40
  type: token-classification
41
  metrics:
42
  - name: Labeled Attachment Score (LAS)
43
  type: f_score
44
+ value: 0.8978441095
45
  - task:
46
  name: SENTS
47
  type: token-classification
48
  metrics:
49
  - name: Sentences F-Score
50
  type: f_score
51
+ value: 0.9038596962
52
  ---
53
  ### Details: https://spacy.io/models/en#en_core_web_sm
54
 
 
57
  | Feature | Description |
58
  | --- | --- |
59
  | **Name** | `en_core_web_sm` |
60
+ | **Version** | `3.4.0` |
61
+ | **spaCy** | `>=3.4.0,<3.5.0` |
62
  | **Default Pipeline** | `tok2vec`, `tagger`, `parser`, `attribute_ruler`, `lemmatizer`, `ner` |
63
  | **Components** | `tok2vec`, `tagger`, `parser`, `senter`, `attribute_ruler`, `lemmatizer`, `ner` |
64
  | **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
 
70
 
71
  <details>
72
 
73
+ <summary>View label scheme (113 labels for 3 components)</summary>
74
 
75
  | Component | Labels |
76
  | --- | --- |
77
+ | **`tagger`** | `$`, `''`, `,`, `-LRB-`, `-RRB-`, `.`, `:`, `ADD`, `AFX`, `CC`, `CD`, `DT`, `EX`, `FW`, `HYPH`, `IN`, `JJ`, `JJR`, `JJS`, `LS`, `MD`, `NFP`, `NN`, `NNP`, `NNPS`, `NNS`, `PDT`, `POS`, `PRP`, `PRP$`, `RB`, `RBR`, `RBS`, `RP`, `SYM`, `TO`, `UH`, `VB`, `VBD`, `VBG`, `VBN`, `VBP`, `VBZ`, `WDT`, `WP`, `WP$`, `WRB`, `XX`, `_SP`, ```` |
78
  | **`parser`** | `ROOT`, `acl`, `acomp`, `advcl`, `advmod`, `agent`, `amod`, `appos`, `attr`, `aux`, `auxpass`, `case`, `cc`, `ccomp`, `compound`, `conj`, `csubj`, `csubjpass`, `dative`, `dep`, `det`, `dobj`, `expl`, `intj`, `mark`, `meta`, `neg`, `nmod`, `npadvmod`, `nsubj`, `nsubjpass`, `nummod`, `oprd`, `parataxis`, `pcomp`, `pobj`, `poss`, `preconj`, `predet`, `prep`, `prt`, `punct`, `quantmod`, `relcl`, `xcomp` |
79
  | **`ner`** | `CARDINAL`, `DATE`, `EVENT`, `FAC`, `GPE`, `LANGUAGE`, `LAW`, `LOC`, `MONEY`, `NORP`, `ORDINAL`, `ORG`, `PERCENT`, `PERSON`, `PRODUCT`, `QUANTITY`, `TIME`, `WORK_OF_ART` |
80
 
 
88
  | `TOKEN_P` | 99.57 |
89
  | `TOKEN_R` | 99.58 |
90
  | `TOKEN_F` | 99.57 |
91
+ | `TAG_ACC` | 97.26 |
92
+ | `SENTS_P` | 91.92 |
93
+ | `SENTS_R` | 88.90 |
94
+ | `SENTS_F` | 90.39 |
95
+ | `DEP_UAS` | 91.66 |
96
+ | `DEP_LAS` | 89.78 |
97
+ | `ENTS_P` | 85.65 |
98
+ | `ENTS_R` | 83.49 |
99
+ | `ENTS_F` | 84.56 |
accuracy.json CHANGED
@@ -3,328 +3,328 @@
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  "token_p": 0.9956819193,
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  "token_r": 0.9957659295,
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  "token_f": 0.9957239226,
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- "tag_acc": 0.9726545475,
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- "sents_p": 0.9188657486,
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- "sents_r": 0.8935285969,
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- "sents_f": 0.9060200669,
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- "dep_uas": 0.9180803841,
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- "dep_las": 0.8996666011,
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  "dep_las_per_type": {
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  "prep": {
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- "p": 0.8545638793,
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- "r": 0.8639347372,
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- "f": 0.8592237589
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  },
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  "det": {
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  "nsubj": {
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- "f": 0.9520642959
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- "p": 0.8575166709,
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- "f": 0.8561075296
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  },
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- "r": 0.8899663566,
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  },
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- "p": 0.8755679832,
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- "r": 0.8991385499,
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- "f": 0.8871967416
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  },
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- "p": 0.9161676647,
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- "r": 0.9119533528,
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- "f": 0.9140556512
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  },
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  },
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  "poss": {
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- "p": 0.9729241877,
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- "r": 0.9764492754,
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- "f": 0.9746835443
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  },
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  "ccomp": {
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- "r": 0.8409368635,
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- "f": 0.8050302203
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  },
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  "attr": {
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- "p": 0.9093147312,
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- "r": 0.9318755257,
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  },
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  "case": {
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- "f": 0.9848447205
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  },
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  "mark": {
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- "f": 0.905026455
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  "advcl": {
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  "cc": {
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  "neg": {
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  },
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  "conj": {
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- "p": 0.7654719087,
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  "nsubjpass": {
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  "npadvmod": {
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  "pcomp": {
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  "expl": {
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- "p": 0.983014862,
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  "r": 0.9914346895,
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  },
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  "acl": {
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  },
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  "agent": {
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  },
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  "dative": {
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  },
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  "acomp": {
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- "p": 0.9046746104,
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  },
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  "dep": {
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- "p": 0.4151624549,
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- "f": 0.2575587906
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  },
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  "csubj": {
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- "p": 0.6476683938,
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- "r": 0.7396449704,
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  },
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  "quantmod": {
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- "p": 0.8682310469,
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- "r": 0.7814784728,
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- "f": 0.8225737495
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  },
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  "nmod": {
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- "p": 0.741078208,
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- "r": 0.5947592931,
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- "f": 0.6599053414
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  },
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  "appos": {
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- "p": 0.7215189873,
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- "r": 0.6676789588,
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- "f": 0.6935556557
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  },
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  "predet": {
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- "p": 0.8395061728,
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- "f": 0.8571428571
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  },
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  "preconj": {
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- "p": 0.5544554455,
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- "r": 0.6511627907,
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  },
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  "oprd": {
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- "p": 0.8205980066,
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- "r": 0.7373134328,
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- "f": 0.7767295597
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  },
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  "parataxis": {
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- "p": 0.6121883657,
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- "r": 0.4793926247,
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- "f": 0.5377128954
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  },
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  "meta": {
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- "p": 0.7407407407,
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- "r": 0.3846153846,
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- "f": 0.5063291139
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  },
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  "csubjpass": {
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- "p": 0.7142857143,
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- "r": 0.8333333333,
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- "f": 0.7692307692
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  }
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  },
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- "ents_p": 0.8508041869,
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- "ents_r": 0.8344851763,
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- "ents_f": 0.8425656714,
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  "ents_per_type": {
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  "DATE": {
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- "p": 0.8732394366,
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- "r": 0.866031746,
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- "f": 0.8696206567
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  },
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  "GPE": {
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- "p": 0.9154443486,
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- "f": 0.90144435
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  },
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  "ORDINAL": {
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- "p": 0.7927927928,
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- "r": 0.8198757764,
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- "f": 0.8061068702
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- },
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- "FAC": {
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- "p": 0.4049586777,
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- "f": 0.390438247
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  },
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  "ORG": {
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- "p": 0.8038601982,
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  },
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- "CARDINAL": {
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- "p": 0.8222477064,
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  },
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- "LOC": {
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- "p": 0.714801444,
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- "r": 0.6305732484,
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- "f": 0.6700507614
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  },
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  "PERSON": {
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- "p": 0.8572793883,
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- "r": 0.8782637076,
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- "f": 0.8676446881
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  },
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  "NORP": {
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- "p": 0.918652424,
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- "r": 0.8944,
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- "f": 0.9063640049
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  },
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  "TIME": {
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- "p": 0.7436708861,
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- "r": 0.6871345029,
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- "f": 0.7142857143
 
 
 
 
 
287
  },
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  "QUANTITY": {
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- "p": 0.8308823529,
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- "r": 0.6208791209,
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- "f": 0.7106918239
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  },
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  "EVENT": {
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- "p": 0.5533980583,
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- "r": 0.3275862069,
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- "f": 0.4115523466
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  },
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  "WORK_OF_ART": {
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- "p": 0.4926470588,
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- "r": 0.3453608247,
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- "f": 0.4060606061
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  },
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  "LAW": {
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- "p": 0.58,
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  "r": 0.453125,
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- "f": 0.5087719298
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  },
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  "MONEY": {
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- "p": 0.9198564593,
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- "r": 0.9079102715,
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- "f": 0.9138443256
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  },
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  "PERCENT": {
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- "p": 0.9153354633,
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- "r": 0.8774885145,
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- "f": 0.8960125098
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- },
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- "LANGUAGE": {
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- "p": 0.7857142857,
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- "r": 0.6875,
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- "f": 0.7333333333
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  },
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  "PRODUCT": {
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- "p": 0.5795454545,
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- "r": 0.2417061611,
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- "f": 0.3411371237
 
 
 
 
 
327
  }
328
  },
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- "speed": 9738.3022066337
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  }
 
3
  "token_p": 0.9956819193,
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  "token_r": 0.9957659295,
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  "token_f": 0.9957239226,
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+ "tag_acc": 0.9726250474,
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+ "sents_p": 0.9191788296,
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+ "sents_r": 0.8890428129,
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+ "sents_f": 0.9038596962,
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+ "dep_uas": 0.9165718428,
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+ "dep_las": 0.8978441095,
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  "dep_las_per_type": {
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  "prep": {
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+ "p": 0.8546376267,
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+ "r": 0.8635553026,
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+ "f": 0.8590733226
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  },
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  "det": {
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+ "p": 0.9768701389,
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+ "r": 0.9781048683,
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+ "f": 0.9774871137
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  },
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  "pobj": {
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+ "p": 0.9605592002,
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+ "r": 0.9659532692,
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+ "f": 0.9632486833
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  },
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  "nsubj": {
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+ "p": 0.9545715675,
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+ "r": 0.9463745893,
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+ "f": 0.9504554055
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  },
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The diff for this file is too large to render. See raw diff
 
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