metadata
tags:
- spacy
- token-classification
language:
- en
license: cc-by-sa-3.0
model-index:
- name: en_core_sci_md
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.6992658401
- name: NER Recall
type: recall
value: 0.6909365474
- name: NER F Score
type: f_score
value: 0.6950762416
- task:
name: TAG
type: token-classification
metrics:
- name: TAG (XPOS) Accuracy
type: accuracy
value: 0
- task:
name: LEMMA
type: token-classification
metrics:
- name: Lemma Accuracy
type: accuracy
value: 0
- task:
name: UNLABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Unlabeled Attachment Score (UAS)
type: f_score
value: 0
- task:
name: LABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Labeled Attachment Score (LAS)
type: f_score
value: 0
- task:
name: SENTS
type: token-classification
metrics:
- name: Sentences F-Score
type: f_score
value: 0
Spacy Models for Biomedical Text.
| Feature | Description |
|---|---|
| Name | en_core_sci_md |
| Version | 0.5.0 |
| spaCy | >=3.2.3,<3.3.0 |
| Default Pipeline | tok2vec, tagger, attribute_ruler, lemmatizer, parser, ner, scispacy_linker |
| Components | tok2vec, tagger, attribute_ruler, lemmatizer, parser, ner, scispacy_linker |
| Vectors | 4087446 keys, 50000 unique vectors (200 dimensions) |
| Sources | OntoNotes 5 Common Crawl GENIA 1.0 |
| License | CC BY-SA 3.0 |
| Author | Allen Institute for Artificial Intelligence |
Label Scheme
View label scheme (98 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, acl:relcl, acomp, advcl, advmod, amod, amod@nmod, appos, attr, aux, auxpass, case, cc, cc:preconj, ccomp, compound, compound:prt, conj, cop, csubj, dative, dep, det, det:predet, dobj, expl, intj, mark, meta, mwe, neg, nmod, nmod:npmod, nmod:poss, nmod:tmod, nsubj, nsubjpass, nummod, parataxis, pcomp, pobj, preconj, predet, prep, punct, quantmod, xcomp |
ner |
ENTITY |
Accuracy
| Type | Score |
|---|---|
TAG_ACC |
0.00 |
LEMMA_ACC |
0.00 |
DEP_UAS |
0.00 |
DEP_LAS |
0.00 |
DEP_LAS_PER_TYPE |
0.00 |
SENTS_P |
0.00 |
SENTS_R |
0.00 |
SENTS_F |
0.00 |
ENTS_F |
69.51 |
ENTS_P |
69.93 |
ENTS_R |
69.09 |
NER_LOSS |
18222557.46 |