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en_core_web_sm
spacy/en_core_web_sm
en_core_web_sm is a token classification model from spacy. 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
212 MB
Likes
66
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At a glance
- Task
- Token Classification
- Library
- spacy
- License
- mit
- Access
- Public
- Created
- Mar 2, 2022
- Updated
- Nov 21, 2023
- SHA
- 8fb6f815
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Model card
From the Hugging Face model README
Details: https://spacy.io/models/en#en_core_web_sm
English pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attribute_ruler, lemmatizer.
| Feature | Description |
|---|---|
| Name | en_core_web_sm |
| 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 | 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) |
| 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 | 99.86 |
TOKEN_P | 99.57 |
TOKEN_R | 99.58 |
TOKEN_F | 99.57 |
TAG_ACC | 97.25 |
SENTS_P | 92.02 |
SENTS_R | 89.21 |
SENTS_F | 90.59 |
DEP_UAS | 91.75 |
DEP_LAS | 89.87 |
ENTS_P | 84.55 |
ENTS_R | 84.57 |
ENTS_F | 84.56 |
- Task
- Token Classification
- Library
- spacy
- License
- mit
- Languages
- en
- Created
- Mar 2, 2022
- Updated
- Nov 21, 2023