en_egw_entity_extractor

mjr27/en_egw_entity_extractor

en_egw_entity_extractor is a token classification model from mjr27. Use it when you need labels on individual words, such as names. It is set up for spacy.

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2.2 GB

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Hugging Face

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At a glance

Task: Token Classification
Library: spacy
Access: Public
Created: Nov 24, 2023
Updated: Nov 24, 2023
SHA: 3cd85bee

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Model card

From the Hugging Face model README

Feature	Description
Name	`en_egw_entity_extractor`
Version	`1.0.0`
spaCy	`>=3.7.2,<3.8.0`
Default Pipeline	`transformer`, `tagger`, `parser`, `ner`
Components	`transformer`, `tagger`, `parser`, `ner`
Vectors	514157 keys, 514157 unique vectors (300 dimensions)
Sources	n/a
License	n/a
Author	n/a

Label Scheme

<details> <summary>View label scheme (100 labels for 3 components)</summary>

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`
`ner`	`DATE`, `GPE`, `LOC`, `PERSON`, `REFERENCE`

</details>

Accuracy

Type	Score
`TAG_ACC`	97.31
`DEP_UAS`	93.84
`DEP_LAS`	92.26
`SENTS_P`	95.17
`SENTS_R`	94.77
`SENTS_F`	94.97
`ENTS_F`	89.38
`ENTS_P`	89.25
`ENTS_R`	89.52
`TRANSFORMER_LOSS`	10291319.95
`TAGGER_LOSS`	1200598.50
`PARSER_LOSS`	5348475.42
`NER_LOSS`	297327.96

Task: Token Classification
Library: spacy
Languages: en
Created: Nov 24, 2023
Updated: Nov 24, 2023