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en_core_web_trf
spacy/en_core_web_trf
en_core_web_trf 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 transformer pipeline (Transformer(name='roberta-base', pieceencoder='byte-bpe', stride=104, type='roberta', width=768, window=144, vocabsize=50265)). Components: transformer, tagger, parser, ner, attributerule…
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At a glance
- Task
- Token Classification
- Library
- spacy
- License
- mit
- Access
- Public
- Created
- Mar 2, 2022
- Updated
- Jun 13, 2024
- SHA
- 58223163
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Model card
From the Hugging Face model README
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)<br />ClearNLP Constituent-to-Dependency Conversion (Emory University)<br />WordNet 3.0 (Princeton University)<br />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 |
Label Scheme
<details> <summary>View label scheme (112 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, ```` |
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 |
- Task
- Token Classification
- Library
- spacy
- License
- mit
- Languages
- en
- Created
- Mar 2, 2022
- Updated
- Jun 13, 2024