metadata

license: apache-2.0
language:
  - en
  - fr
  - de
  - es
  - pt
  - it
library_name: gliner
pipeline_tag: token-classification
datasets:
  - urchade/synthetic-pii-ner-mistral-v1

Model Card for GLiNER-multi

GLiNER is a Named Entity Recognition (NER) model capable of identifying any entity type using a bidirectional transformer encoder (BERT-like). It provides a practical alternative to traditional NER models, which are limited to predefined entities, and Large Language Models (LLMs) that, despite their flexibility, are costly and large for resource-constrained scenarios.

This version has been ot recognize and classify Personally Identifiable Information (PII) within text. The training dataset has been generated using mistralai/Mistral-7B-Instruct-v0.2.

Links

Paper: https://arxiv.org/abs/2311.08526
Repository: https://github.com/urchade/GLiNER

from gliner import GLiNER

model = GLiNER.from_pretrained("urchade/gliner_multi_pii-v1")

text = """
Harilala Rasoanaivo, un homme d'affaires local d'Antananarivo, a enregistré une nouvelle société nommée "Rasoanaivo Enterprises" au Lot II M 92 Antohomadinika. Son numéro est le +261 32 22 345 67, et son adresse électronique est [email protected]. Il a fourni son numéro de sécu 501-02-1234 pour l'enregistrement.
"""

labels = ["work", "booking number", "personally identifiable information", "driver licence", "person", "book", "full address", "company", "actor", "character", "email", "passport number", "Social Security Number", "phone number"]
entities = model.predict_entities(text, labels)

for entity in entities:
    print(entity["text"], "=>", entity["label"])

Harilala Rasoanaivo => person
Rasoanaivo Enterprises => company
Lot II M 92 Antohomadinika => full address
+261 32 22 345 67 => phone number
[email protected] => email
501-02-1234 => Social Security Number