A custom spaCy model for ICIS entity recognition.
Feature | Description |
---|---|
Name | en_icis_model |
Version | 1.0.7 |
spaCy | >=3.7.5,<3.8.0 |
Default Pipeline | tok2vec , tagger , parser , attribute_ruler , lemmatizer , entity_ruler , ner |
Components | tok2vec , tagger , parser , senter , attribute_ruler , lemmatizer , entity_ruler , ner |
Vectors | 0 keys, 0 unique vectors (0 dimensions) |
Sources | OntoNotes 5 (Ralph Weischedel, Martha Palmer, Mitchell Marcus, Eduard Hovy, Sameer Pradhan, Lance Ramshaw, Nianwen Xue, Ann Taylor, Jeff Kaufman, Michelle Franchini, Mohammed El-Bachouti, Robert Belvin, Ann Houston) ClearNLP Constituent-to-Dependency Conversion (Emory University) WordNet 3.0 (Princeton University) |
License | MIT |
Author | James Melvin |
Label Scheme
View label scheme (115 labels for 4 components)
Component | Labels |
---|---|
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 , ```` |
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 |
entity_ruler |
COMMODITY , LOCATION |
ner |
CARDINAL , DATE , EVENT , FAC , GPE , LANGUAGE , LAW , LOC , MONEY , NORP , ORDINAL , ORG , PERCENT , PERSON , PRODUCT , QUANTITY , TIME , WORK_OF_ART |
Accuracy
Type | Score |
---|---|
TOKEN_ACC |
99.86 |
TOKEN_P |
99.57 |
TOKEN_R |
99.58 |
TOKEN_F |
99.57 |
TAG_ACC |
97.25 |
SENTS_P |
92.02 |
SENTS_R |
89.21 |
SENTS_F |
90.59 |
DEP_UAS |
91.75 |
DEP_LAS |
89.87 |
ENTS_P |
84.55 |
ENTS_R |
84.57 |
ENTS_F |
84.56 |
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Evaluation results
- NER Precisionself-reported0.845
- NER Recallself-reported0.846
- NER F Scoreself-reported0.846
- TAG (XPOS) Accuracyself-reported0.972
- Unlabeled Attachment Score (UAS)self-reported0.918
- Labeled Attachment Score (LAS)self-reported0.899
- Sentences F-Scoreself-reported0.906