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en_ner_bionlp13cg_md
KTBaruti/en_ner_bionlp13cg_md
en_ner_bionlp13cg_md is a token classification model from KTBaruti. Use it when you need labels on individual words, such as names. It is set up for spacy. The card lists the license as cc-by-sa-3.0.
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Repo size
337 MB
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.whl127 MB · 38%
At a glance
- Task
- Token Classification
- Library
- spacy
- License
- cc-by-sa-3.0
- Access
- Public
- Created
- Sep 10, 2026
- Updated
- Sep 11, 2026
- SHA
- 63b94f78
Try a prompt
Model card
From the Hugging Face model README
Spacy Models for Biomedical Text.
| Feature | Description |
|---|---|
| Name | en_ner_bionlp13cg_md |
| Version | 0.5.4 |
| spaCy | >=3.7.4,<3.8.0 |
| Default Pipeline | abbreviation_detector, tok2vec, tagger, attribute_ruler, lemmatizer, parser, ner, bc5cdr_ner, human_ner |
| Components | abbreviation_detector, tok2vec, tagger, attribute_ruler, lemmatizer, parser, ner, bc5cdr_ner, human_ner |
| Vectors | 4087446 keys, 50000 unique vectors (200 dimensions) |
| Sources | BIONLP13CG<br>OntoNotes 5<br>Common Crawl<br>GENIA 1.0 |
| License | CC BY-SA 3.0 |
| Author | Allen Institute for Artificial Intelligence |
⚠️ Note for Kaggle and Google Colab Users
Modern environments use newer versions of pip (24.1+) that will reject this model's legacy package version format (==any), causing an Invalid requirement installation error.
To successfully install this model in a Kaggle or Colab notebook, run the following setup block in a code cell to align the dependency versions, then restart your session/kernel:
# 1. Temporarily downgrade pip to accept the legacy wheel metadata format
!pip install -q "pip<24.1"
# 2. Ensure your spaCy version matches the model requirements
!pip install -q "spacy>=3.7.4,<3.8.0" --force-reinstall
# 3. Install the model package
!pip install https://huggingface.co
# 4. Restore pip to the latest version
!pip install -q -U pip
Once the cell finishes running, go to the top menu and select Run -> Restart session (or Runtime -> Restart session in Colab) before importing spacy.
Label Scheme
<details> <summary>View label scheme (115 labels for 4 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, 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 | AMINO_ACID, ANATOMICAL_SYSTEM, CANCER, CELL, CELLULAR_COMPONENT, DEVELOPING_ANATOMICAL_STRUCTURE, GENE_OR_GENE_PRODUCT, IMMATERIAL_ANATOMICAL_ENTITY, MULTI_TISSUE_STRUCTURE, ORGAN, ORGANISM, ORGANISM_SUBDIVISION, ORGANISM_SUBSTANCE, PATHOLOGICAL_FORMATION, SIMPLE_CHEMICAL, TISSUE |
bc5cdr_ner | CHEMICAL, DISEASE |
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 | 77.13 |
ENTS_P | 79.36 |
ENTS_R | 75.03 |
NER_LOSS | 555638.66 |
- Task
- Token Classification
- Library
- spacy
- License
- cc-by-sa-3.0
- Languages
- en
- Created
- Sep 10, 2026
- Updated
- Sep 11, 2026