en_engagement_LSTM_f2

egumasa/en_engagement_LSTM_f2

en_engagement_LSTM_f2 is a token classification model from egumasa. Use it when you need labels on individual words, such as names. It is set up for spacy.

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1.9 GB

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Hugging Face

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At a glance

Task: Token Classification
Library: spacy
Access: Public
Created: Feb 4, 2023
Updated: Feb 4, 2023
SHA: c4ef1b4d

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Model card

From the Hugging Face model README

Feature	Description
Name	`en_engagement_LSTM_f2`
Version	`1.0.0`
spaCy	`>=3.4.4,<3.5.0`
Default Pipeline	`transformer`, `parser`, `tagger`, `ner`, `attribute_ruler`, `lemmatizer`, `trainable_transformer`, `spancat`
Components	`transformer`, `parser`, `tagger`, `ner`, `attribute_ruler`, `lemmatizer`, `trainable_transformer`, `spancat`
Vectors	0 keys, 0 unique vectors (0 dimensions)
Sources	n/a
License	n/a
Author	n/a

Label Scheme

<details> <summary>View label scheme (122 labels for 4 components)</summary>

Component	Labels
`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`
`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`, ````
`ner`	`CARDINAL`, `DATE`, `EVENT`, `FAC`, `GPE`, `LANGUAGE`, `LAW`, `LOC`, `MONEY`, `NORP`, `ORDINAL`, `ORG`, `PERCENT`, `PERSON`, `PRODUCT`, `QUANTITY`, `TIME`, `WORK_OF_ART`
`spancat`	`ATTRIBUTION`, `COUNTER`, `SOURCES`, `CITATION`, `DENY`, `ENTERTAIN`, `MONOGLOSS`, `PROCLAIM`, `ENDOPHORIC`, `JUSTIFYING`

</details>

Accuracy

Type	Score
`DEP_UAS`	0.00
`DEP_LAS`	0.00
`DEP_LAS_PER_TYPE`	0.00
`SENTS_P`	91.43
`SENTS_R`	93.16
`SENTS_F`	92.29
`TAG_ACC`	0.00
`ENTS_F`	0.00
`ENTS_P`	0.00
`ENTS_R`	0.00
`LEMMA_ACC`	0.00
`SPANS_SC_F`	74.80
`SPANS_SC_P`	75.85
`SPANS_SC_R`	73.77
`TRAINABLE_TRANSFORMER_LOSS`	226.60
`SPANCAT_LOSS`	81318.13

Task: Token Classification
Library: spacy
Languages: en
Created: Feb 4, 2023
Updated: Feb 4, 2023