SpaCy models
Collection
5 items • Updated
How to use ner4archives/fr_ner4archives_V3_camembert_base with spaCy:
!pip install https://huggingface.co/ner4archives/fr_ner4archives_V3_camembert_base/resolve/main/fr_ner4archives_V3_camembert_base-any-py3-none-any.whl
# Using spacy.load().
import spacy
nlp = spacy.load("fr_ner4archives_V3_camembert_base")
# Importing as module.
import fr_ner4archives_V3_camembert_base
nlp = fr_ner4archives_V3_camembert_base.load()| Feature | Description |
|---|---|
| Name | fr_ner4archives_V3_camembert_base |
| Version | 0.0.0 |
| spaCy | >=3.4.1,<3.5.0 |
| Default Pipeline | transformer, ner |
| Components | transformer, ner |
| Vectors | 0 keys, 0 unique vectors (0 dimensions) |
| Sources | French corpus for the NER task composed of finding aids in XML-EAD from the National Archives of France (v. 3.0) - Check corpus version on GitHub |
| License | CC-BY-4.0 license |
| Author | Archives nationales / Inria-Almanach |
| Component | Labels |
|---|---|
ner |
EVENT, LOCATION, ORGANISATION, PERSON, TITLE |
| Type | Score |
|---|---|
ENTS_F |
91.95 |
ENTS_P |
91.61 |
ENTS_R |
92.30 |
TRANSFORMER_LOSS |
395487.28 |
NER_LOSS |
11238.70 |