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NER_for_contacts
SuramyaPokharel/NER_for_contacts
NER_for_contacts is a token classification model from SuramyaPokharel. 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
86.9 MB
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At a glance
- Task
- Token Classification
- Library
- spacy
- License
- mit
- Access
- Public
- Created
- May 2, 2024
- Updated
- May 3, 2024
- SHA
- 654b8acd
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Model card
From the Hugging Face model README
English pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attribute_ruler, lemmatizer.
| Feature | Description |
|---|---|
| Name | en_core_web_md |
| 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 | 514157 keys, 20000 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 (117 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, Father, GPE, LANGUAGE, LAW, LOC, MONEY, Mother, NORP, ORDINAL, ORG, PERCENT, PERSON, PRODUCT, Partner, Profession, 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 | 97.33 |
SENTS_P | 92.21 |
SENTS_R | 89.37 |
SENTS_F | 90.77 |
DEP_UAS | 92.05 |
DEP_LAS | 90.23 |
ENTS_P | 84.94 |
ENTS_R | 85.49 |
ENTS_F | 85.22 |
- Task
- Token Classification
- Library
- spacy
- License
- mit
- Languages
- en
- Created
- May 2, 2024
- Updated
- May 3, 2024