GLiNER2: Unified Schema-Based Information Extraction (opens in new tab)

(github.com)

61 pointsapwheele20d ago14 comments

14 comments

adsharma20d ago

Feels like it's written by ML people not following python software engineering practices.

No black, UV or ruff.

Prints messages with emojis to stdout by default.

Makes a connection to hugging face on every import.

https://github.com/fastino-ai/GLiNER2/pull/74

fbilhaut19d ago

GLiNER is a really great research work. But putting this kind of things in production is just another job. Not trying to do self promotion here, but there are alternatives for this purpose, like gline-rs (https://github.com/fbilhaut/gline-rs). Support of GLiNER 2 models is on the way.

adsharma19d ago

Any chance you could wrap this in pyo3? There is a large python market for this.

iwhalen20d ago

Very cool stuff. Love the focus on CPU-first.

Would also love to see some throughput numbers on basic VM setup.

Edit: there are some latency numbers in the paper https://arxiv.org/pdf/2507.18546

deepsquirrelnet20d ago

Zero-shot encoder models are so cool. I'll definitely be checking this out.

If you're looking for a zero-shot classifier, tasksource is in a similar vein.

https://huggingface.co/tasksource/ModernBERT-large-nli

goodlux19d ago

gliner2 does classification as well as entities and relationships

plaguna20d ago

Is this only for text I guess? What if the documents are in PDF? What is the recommendation to transform PDF to text?

akreal19d ago

Docling: https://github.com/docling-project/docling

snthpy20d ago

This looks great. Thank you!

hbcondo71420d ago

There is another version at:

https://github.com/urchade/GLiNER

Looks like it’s still being maintained too?

goodlux19d ago

GLiNER2 is an upgrade that allows for relationship extraction and classification, built on GLiNER, and added additional research / papers, then trained a new model.

adsharma20d ago

Use Gliner2. Much better model.

hbcondo71419d ago

Okay but there is a dependency on gliner1:

https://github.com/fastino-ai/GLiNER2/issues/69

1 more reply

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14 comments

adsharma20d ago

Feels like it's written by ML people not following python software engineering practices.

No black, UV or ruff.

Prints messages with emojis to stdout by default.

Makes a connection to hugging face on every import.

https://github.com/fastino-ai/GLiNER2/pull/74

fbilhaut19d ago

adsharma19d ago

Any chance you could wrap this in pyo3? There is a large python market for this.

iwhalen20d ago

Very cool stuff. Love the focus on CPU-first.

Would also love to see some throughput numbers on basic VM setup.

Edit: there are some latency numbers in the paper https://arxiv.org/pdf/2507.18546

deepsquirrelnet20d ago

Zero-shot encoder models are so cool. I'll definitely be checking this out.

If you're looking for a zero-shot classifier, tasksource is in a similar vein.

https://huggingface.co/tasksource/ModernBERT-large-nli

goodlux19d ago

gliner2 does classification as well as entities and relationships

plaguna20d ago

Is this only for text I guess? What if the documents are in PDF? What is the recommendation to transform PDF to text?

akreal19d ago

Docling: https://github.com/docling-project/docling

snthpy20d ago

This looks great. Thank you!

hbcondo71420d ago

There is another version at:

https://github.com/urchade/GLiNER

Looks like it’s still being maintained too?

goodlux19d ago

GLiNER2 is an upgrade that allows for relationship extraction and classification, built on GLiNER, and added additional research / papers, then trained a new model.

adsharma20d ago

Use Gliner2. Much better model.

hbcondo71419d ago

Okay but there is a dependency on gliner1:

https://github.com/fastino-ai/GLiNER2/issues/69

1 more reply

j / k navigate · click thread line to collapse