en_data_dev_spacy_trf_1

sigtica/en_data_dev_spacy_trf_1

en_data_dev_spacy_trf_1 is a token classification model from sigtica. Use it when you need labels on individual words, such as names. It is set up for spacy. The card lists the license as mit.

English spacy model, based on the spacy stock encorewebtrf transformer-based model, used for regular day to day data engineering.

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At a glance

Task: Token Classification
Library: spacy
License: mit
Access: Public
Created: Jul 19, 2024
Updated: Jul 19, 2024
SHA: ca2d8956

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Model card

From the Hugging Face model README

en_data_dev_spacy_trf_1

English spacy model, based on the spacy stock en_core_web_trf transformer-based model, used for regular day to day data engineering.

English transformer pipeline (Transformer(name='roberta-base', piece_encoder='byte-bpe', stride=104, type='roberta', width=768, window=144, vocab_size=50265)). Components: transformer, tagger, parser, ner, attribute_ruler, lemmatizer.

Feature	Description
Name	`en_data_dev_spacy_trf_1`
Version	`3.7.3`
spaCy	`>=3.7.2,<3.8.0`
Default Pipeline	`transformer`, `tagger`, `parser`, `attribute_ruler`, `lemmatizer`, `ner`
Components	`transformer`, `tagger`, `parser`, `attribute_ruler`, `lemmatizer`, `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)<br>ClearNLP Constituent-to-Dependency Conversion (Emory University)<br>WordNet 3.0 (Princeton University)<br>roberta-base (Yinhan Liu and Myle Ott and Naman Goyal and Jingfei Du and Mandar Joshi and Danqi Chen and Omer Levy and Mike Lewis and Luke Zettlemoyer and Veselin Stoyanov)
License	`MIT`
Author	Explosion

Label Scheme

<details> <summary>View label scheme (112 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`, ````
`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`	`CARDINAL`, `DATE`, `EVENT`, `FAC`, `GPE`, `LANGUAGE`, `LAW`, `LOC`, `MONEY`, `NORP`, `ORDINAL`, `ORG`, `PERCENT`, `PERSON`, `PRODUCT`, `QUANTITY`, `TIME`, `WORK_OF_ART`

</details>

Accuracy

Type	Score
`TOKEN_ACC`	99.86
`TOKEN_P`	99.57
`TOKEN_R`	99.58
`TOKEN_F`	99.57
`TAG_ACC`	98.13
`SENTS_P`	94.89
`SENTS_R`	85.79
`SENTS_F`	90.11
`DEP_UAS`	95.26
`DEP_LAS`	93.91
`ENTS_P`	90.08
`ENTS_R`	90.30
`ENTS_F`	90.19

Task: Token Classification
Library: spacy
License: mit
Languages: en
Created: Jul 19, 2024
Updated: Jul 19, 2024