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en_core_web_lg
neerajp/en_core_web_lg
en_core_web_lg is a token classification model from neerajp. 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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Repo size
1.2 GB
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1
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Repo makeup
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Other638 MB · 52%
At a glance
- Task
- Token Classification
- Library
- spacy
- License
- mit
- Access
- Public
- Created
- Nov 4, 2022
- Updated
- Nov 4, 2022
- SHA
- 3f017d47
Try a prompt
Model card
From the Hugging Face model README
Details: https://spacy.io/models/en#en_core_web_lg
English pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attribute_ruler, lemmatizer.
| Feature | Description |
|---|---|
| Name | en_core_web_lg |
| Version | 3.4.1 |
| spaCy | >=3.4.0,<3.5.0 |
| Default Pipeline | tok2vec, tagger, parser, attribute_ruler, lemmatizer, ner |
| Components | tok2vec, tagger, parser, senter, attribute_ruler, lemmatizer, ner |
| Vectors | 514157 keys, 514157 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 (113 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, EVENT, FAC, GPE, LANGUAGE, LAW, LOC, MONEY, NORP, ORDINAL, ORG, PERCENT, PERSON, PRODUCT, QUANTITY, TIME, WORK_OF_ART |
Accuracy
| Type | Score |
|---|---|
TOKEN_ACC | 99.93 |
TOKEN_P | 99.57 |
TOKEN_R | 99.58 |
TOKEN_F | 99.57 |
TAG_ACC | 97.34 |
SENTS_P | 91.79 |
SENTS_R | 89.14 |
SENTS_F | 90.44 |
DEP_UAS | 92.04 |
DEP_LAS | 90.23 |
ENTS_P | 85.35 |
ENTS_R | 85.93 |
ENTS_F | 85.64 |
- Task
- Token Classification
- Library
- spacy
- License
- mit
- Languages
- en
- Created
- Nov 4, 2022
- Updated
- Nov 4, 2022