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@@ -23,12 +23,12 @@ GLiNER is a Named Entity Recognition (NER) model capable of identifying any enti
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  GLiNER-bi-V2 Models:
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- | Model name | Params | Encoder | Labels Encoder | Avg. CrossNER Benchmark | Avg. Inference Speed (H100, examples/s) | Avg. Inference Speed (H100, examples/s, pre-computed labels) |
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- |------------|--------|---------|----------------|-------------------------|------------------------------------------|----------------------------------------------------------|
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- | gliner-bi-edge-v2.0 | 60 M | ettin-encoder-32m | all-MiniLM-L6-v2 | 54.0% | 13.64 | 24.62 |
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- | gliner-bi-small-v2.0 | 108 M | ettin-encoder-68m | all-MiniLM-L12-v2 | 57.2% | 7.99 | 15.22 |
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- | gliner-bi-base-v2.0 | 194 M | ettin-encoder-150m | bge-small-en-v1.5 | 60.3% | 5.91 | 9.51 |
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- | gliner-bi-large-v2.0 | 530 M | ettin-encoder-400m | bge-base-en-v1.5 | 61.5% | 2.68 | 3.60 |
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  Such architecture brings several advantages over uni-encoder GLiNER:
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  * An unlimited amount of entities can be recognized at a single time;
 
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  GLiNER-bi-V2 Models:
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+ | Model name | Params | Encoder | Labels Encoder | Avg. CrossNER Benchmark | Avg. Inference Speed (H100, examples/s) | Avg. Inference Speed (H100, examples/s, pre-computed labels) |
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+ |-------------------------------------------------------------------------------------------------|--------|------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------|-------------------------|------------------------------------------|----------------------------------------------------------|
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+ | [gliner-bi-edge-v2.0](https://huggingface.co/knowledgator/gliner-bi-edge-v2.0) | 60 M | [ettin-encoder-32m](https://huggingface.co/jhu-clsp/ettin-encoder-32m) | [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) | 54.0% | 13.64 | 24.62 |
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+ | [gliner-bi-small-v2.0](https://huggingface.co/knowledgator/gliner-bi-small-v2.0) | 108 M | [ettin-encoder-68m](https://huggingface.co/jhu-clsp/ettin-encoder-68m) | [all-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2) | 57.2% | 7.99 | 15.22 |
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+ | [gliner-bi-base-v2.0](https://huggingface.co/knowledgator/gliner-bi-base-v2.0) | 194 M | [ettin-encoder-150m](https://huggingface.co/jhu-clsp/ettin-encoder-150m) | [bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | 60.3% | 5.91 | 9.51 |
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+ | [gliner-bi-large-v2.0](https://huggingface.co/knowledgator/gliner-bi-large-v2.0) | 530 M | [ettin-encoder-400m](https://huggingface.co/jhu-clsp/ettin-encoder-400m) | [bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | 61.5% | 2.68 | 3.60 |
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  Such architecture brings several advantages over uni-encoder GLiNER:
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  * An unlimited amount of entities can be recognized at a single time;