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Fabian Billert

Bailefan
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reacted to urchade's post with โค๏ธ 11 months ago
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**Some updates on GLiNER**

๐Ÿ†• A new commercially permissible multilingual version is available urchade/gliner_multiv2.1

๐Ÿ› A subtle bug that causes performance degradation on some models has been corrected. Thanks to @yyDing1 for raising the issue.

from gliner import GLiNER

# Initialize GLiNER
model = GLiNER.from_pretrained("urchade/gliner_multiv2.1")

text = "This is a text about Bill Gates and Microsoft."

# Labels for entity prediction
labels = ["person", "organization", "email"]

entities = model.predict_entities(text, labels, threshold=0.5)

for entity in entities:
    print(entity["text"], "=>", entity["label"])
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reacted to urchade's post with โค๏ธ 12 months ago
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Hi everyone,

I'd like to share our project on open-type Named Entity Recognition (NER). Our model uses a transformer encoder (BERT-like), making the computation overhead very minimal compared to use of LLMs. I've developed a demo that runs on CPU on Google Colab.

Colab Demo: https://colab.research.google.com/drive/1mhalKWzmfSTqMnR0wQBZvt9-ktTsATHB?usp=sharing

Code: https://github.com/urchade/GLiNER

Paper: https://arxiv.org/abs/2311.08526
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