Checksum .tar.gz:
dae355f7f419bee53f2804a8e62a6473425e8680ac8ff8e8a7b30b7e2b8b0c4f
Checksum .whl:f72abb34bdf174876bd4267b29b2501677e605e0a251fdc56c163003182ed68b
Details: https://spacy.io/models/en#en_core_web_trf
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_core_web_trf
|
| 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) ClearNLP Constituent-to-Dependency Conversion (Emory University) WordNet 3.0 (Princeton University) 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 |
| Model size | 436 MB |
Label Scheme
View label scheme (112 labels for 3 components)
| 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
|
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 |
Installation
pip install spacy
python -m spacy download en_core_web_trf