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panmiqi/
spacy-deberta

Name: spacy-deberta
Availability: InStock

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panmiqi/spaCy-DeBERTa

This model is a spaCy NER pipeline built on top of microsoft/deberta-v3, trained on the CoNLL-2003 English NER dataset.

It predicts the four standard CoNLL entity types:

PER
ORG
LOC
MISC

Training

Base model: DeBERTa v3
Dataset: CoNLL-2003 (train + dev)
Framework: spaCy 3.x with spacy-transformers
Hardware: A800 GPU

Performance (Dev Set)

The model reaches performance in the expected range for transformer-based CoNLL systems:

F1 ≈ 96.1
Precision ≈ 95.6
Recall ≈ 96.6

Usage (Linux and Mac)

Dependencies (Bash):

pip install "spacy>=3.8.11,<3.9.0" "numpy<2" spacy-transformers huggingface_hub

Installation & Test (Python):

python

python - << 'PY'
import spacy
from huggingface_hub import snapshot_download

def load_panmiqi_spacy_deberta():
    repo_id = "panmiqi/spacy-deberta"
    local_dir = snapshot_download(repo_id)
    return spacy.load(local_dir)

nlp = load_panmiqi_spacy_deberta()

text = "U.N. official Patrick heads for Beijing."
doc = nlp(text)

print([(ent.text, ent.label_) for ent in doc.ents])
PY