English NER (7 classes) — Flukes
Named entity recognition for English text. Predicts seven entity types:
| Label | Description |
|---|---|
PER | Person names |
ORG | Organization names |
LOC | Locations (cities, countries, geographical features) |
EVENT | Named events (elections, wars, conferences, ...) |
PRODUCT | Named products |
WORK_OF_ART | Books, films, songs, paintings, ... |
MISC | All other entities (nationalities, languages, etc.) |
⚠️ Default license: noncommercial use only. This model is released under the Flukes NC 1.0 License. Commercial use — including using this model's predictions in a commercial product or service — requires a separate license. Contact
alan.akbik@gmail.com.
Use
Install flukes:
pip install flukes
Then:
from flukes.models import Model
model = Model.load("ner")
doc = model.predict("Washington bought himself a Chevy Roadster.\n\nHe drove it to the Berlin Marathon.")
print(doc)
Output:
Document[80]: Washington bought himself a Chevy Roadster.
╰───PER───╯ ╰───PRODUCT──╯
He drove it to the Berlin Marathon.
╰─────EVENT────╯
Access Annotations
You can also access all annotations directly:
from flukes.models import Model
model = Model.load("ner")
doc = model.predict("Washington bought himself a Chevy Roadster.\n\nHe drove it to the Berlin Marathon.")
for span in doc.spans:
print(f"{span.text!r:30} {span.label} ({span.score:.2f})")
Output:
'Washington' PER (1.00)
'Chevy Roadster' PRODUCT (1.00)
'Berlin Marathon' EVENT (1.00)
Performance
This model scores 95.44 F1 on our internal test sets.
License
Model weights: Flukes Noncommercial License 1.0.
Personal, academic, and other noncommercial use permitted. Commercial use
requires a separate license — contact alan.akbik@gmail.com.
Flukes library code: Apache 2.0.