en_chat_xsum_ner_v2

axlesubash/en_chat_xsum_ner_v2

en_chat_xsum_ner_v2 is a token classification model from axlesubash. 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 pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attributeruler, lemmatizer.

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At a glance

Task: Token Classification
Library: spacy
License: mit
Access: Public
Created: Apr 12, 2024
Updated: Apr 15, 2024
SHA: 2ef022d6

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Model card

From the Hugging Face model README

English pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attribute_ruler, lemmatizer.

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

Developed by: Subash Pandey
Funded by [optional]: [More Information Needed]
Shared by [optional]: [More Information Needed]
Model type: [More Information Needed]
Language(s) (NLP): [More Information Needed]
License: [More Information Needed]

Feature	Description
Name	`en_chat_xsum_ner_v2`
Version	`3.7.1`
spaCy	`>=3.7.2,<3.8.0`
Default Pipeline	`tok2vec`, `tagger`, `parser`, `attribute_ruler`, `lemmatizer`, `ner`
Components	`tok2vec`, `tagger`, `parser`, `senter`, `attribute_ruler`, `lemmatizer`, `ner`
Vectors	514157 keys, 20000 unique vectors (300 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>Explosion Vectors (OSCAR 2109 + Wikipedia + OpenSubtitles + WMT News Crawl) (Explosion)
License	`MIT`
Author	Explosion

Label Scheme

<details> <summary>View label scheme (118 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`	`CARDINAL`, `DATE`, `Date`, `EVENT`, `Event`, `FAC`, `GPE`, `LANGUAGE`, `LAW`, `LOC`, `Location`, `MONEY`, `NORP`, `ORDINAL`, `ORG`, `PERCENT`, `PERSON`, `PRODUCT`, `Person`, `QUANTITY`, `TIME`, `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`	97.33
`SENTS_P`	92.21
`SENTS_R`	89.37
`SENTS_F`	90.77
`DEP_UAS`	92.05
`DEP_LAS`	90.23
`ENTS_P`	84.94
`ENTS_R`	85.49
`ENTS_F`	85.22

Task: Token Classification
Library: spacy
License: mit
Languages: en
Created: Apr 12, 2024
Updated: Apr 15, 2024