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NuMind has just released 3 new state-of-the-art GLiNER models for Named Entity Recognition/Information Extraction. These GLiNER models allow you to specify any label that you want, and it'll find spans in the text corresponding to your label. It's been shown to work quite well on unusual domains, e.g. celestial entities in my picture.
NuMind has just released 3 new state-of-the-art GLiNER models for Named Entity Recognition/Information Extraction. These GLiNER models allow you to specify any label that you want, and it'll find spans in the text corresponding to your label. It's been shown to work quite well on unusual domains, e.g. celestial entities in my picture.
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There are 3 models released:
There are 3 models released:
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resource
https://huggingface.co/numind/NuNER_Zero
model
numind/NuNER_Zero
https://huggingface.co/numind/NuNER_Zero
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  The primary model, SOTA & can detect really long entities.
  The primary model, SOTA & can detect really long entities.
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https://huggingface.co/numind/NuNER_Zero-span
model
numind/NuNER_Zero-span
https://huggingface.co/numind/NuNER_Zero-span
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  Slightly better performance than NuNER Zero, but can't detect entities longer than 12 tokens.
  Slightly better performance than NuNER Zero, but can't detect entities longer than 12 tokens.
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resource
https://huggingface.co/numind/NuNER_Zero-4k
model
numind/NuNER_Zero-4k
https://huggingface.co/numind/NuNER_Zero-4k
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  Slightly worse than NuNER Zero, but has a context length of 4k tokens.
  Slightly worse than NuNER Zero, but has a context length of 4k tokens.
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Some more details about these models in general:
Some more details about these models in general:
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- They are *really* small, orders of magnitude smaller than LLMs, which don't reach this level of performance.
- They are *really* small, orders of magnitude smaller than LLMs, which don't reach this level of performance.
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- Because they're small - they're fast: <1s per sentence on free GPUs.
- Because they're small - they're fast: <1s per sentence on free GPUs.
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- They have an MIT license: free commercial usage.
- They have an MIT license: free commercial usage.
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Try out the demo here: 
Try out the demo here: 
link
https://huggingface.co/spaces/numind/NuZero
https://huggingface.co/spaces/numind/NuZero
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Or check out all of the models here: 
Or check out all of the models here: 
resource
https://huggingface.co/collections/numind/nunerzero-zero-shot-ner-662b59803b9b438ff56e49e2
collection
numind/nunerzero-zero-shot-ner-662b59803b9b438ff56e49e2
https://huggingface.co/collections/numind/nunerzero-zero-shot-ner-662b59803b9b438ff56e49e2
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If there's ever a need for me to extract some information from any text: I'll be using these. Great work 
If there's ever a need for me to extract some information from any text: I'll be using these. Great work 
mention
@Serega6678
Serega6678
text
!
!
NuMind has just released 3 new state-of-the-art GLiNER models for Named Entity Recognition/Information Extraction. These GLiNER models allow you to specify any label that you want, and it'll find spans in the text corresponding to your label. It's been shown to work quite well on unusual domains, e.g. celestial entities in my picture.

There are 3 models released:
- https://huggingface.co/numind/NuNER_Zero:
  The primary model, SOTA & can detect really long entities.
- https://huggingface.co/numind/NuNER_Zero-span:
  Slightly better performance than NuNER Zero, but can't detect entities longer than 12 tokens.
- https://huggingface.co/numind/NuNER_Zero-4k:
  Slightly worse than NuNER Zero, but has a context length of 4k tokens.

Some more details about these models in general:
- They are *really* small, orders of magnitude smaller than LLMs, which don't reach this level of performance.
- Because they're small - they're fast: <1s per sentence on free GPUs.
- They have an MIT license: free commercial usage.

Try out the demo here: https://huggingface.co/spaces/numind/NuZero
Or check out all of the models here: https://huggingface.co/collections/numind/nunerzero-zero-shot-ner-662b59803b9b438ff56e49e2

If there's ever a need for me to extract some information from any text: I'll be using these. Great work @Serega6678!
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Tom Aarsen
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