beki/privy
Updated β’ 266 β’ 23
How to use beki/en_spacy_pii_distilbert with spaCy:
!pip install https://huggingface.co/beki/en_spacy_pii_distilbert/resolve/main/en_spacy_pii_distilbert-any-py3-none-any.whl
# Using spacy.load().
import spacy
nlp = spacy.load("en_spacy_pii_distilbert")
# Importing as module.
import en_spacy_pii_distilbert
nlp = en_spacy_pii_distilbert.load()| Feature | Description |
|---|---|
| Name | en_spacy_pii_distilbert |
| Version | 0.0.0 |
| spaCy | >=3.4.1,<=3.8.2 |
| Default Pipeline | transformer, ner |
| Components | transformer, ner |
| Vectors | 0 keys, 0 unique vectors (0 dimensions) |
| Sources | Trained on a new dataset for structured PII generated by Privy. For more details, see this blog post |
| License | MIT |
| Author | Benjamin Kilimnik |
| Component | Labels |
|---|---|
ner |
DATE_TIME, LOC, NRP, ORG, PER |
| Type | Score |
|---|---|
ENTS_F |
95.42 |
ENTS_P |
95.30 |
ENTS_R |
95.54 |
TRANSFORMER_LOSS |
61154.85 |
NER_LOSS |
56001.88 |