Details: https://spacy.io/models/en#en_core_web_sm
File checksum:
06c80936324012d1223291d2af41a5229e746dc2dee8fe31a532666ee3d18aaa
English multi-task CNN trained on OntoNotes. Assigns context-specific token vectors, POS tags, dependency parse and named entities.
| Feature | Description |
|---|---|
| Name | en_core_web_sm
|
| Version | 2.3.1
|
| spaCy | >=2.3.0,<2.4.0
|
| Model size | 11 MB |
| Pipeline | tagger, parser, ner
|
| Vectors | 0 keys, 0 unique vectors (0 dimensions) |
| Sources | OntoNotes 5 |
| License | MIT
|
| Author | Explosion |
Label Scheme
| 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 |
|---|---|
LAS
| 89.76 |
UAS
| 91.66 |
TOKEN_ACC
| 99.76 |
TAGS_ACC
| 97.06 |
ENTS_F
| 85.43 |
ENTS_P
| 85.72 |
ENTS_R
| 85.14 |
Installation
pip install spacy
python -m spacy download en_core_web_sm