flair/fluke-ner-en-7class

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.

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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.