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gliner-bi-ko-small-v1

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lots-o commited on Feb 23

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@@ -20,7 +20,7 @@ base_model:
 GLiNER is a Named Entity Recognition (NER) model capable of identifying any entity type using a bidirectional transformer encoders (BERT-like). It provides a practical alternative to traditional NER models, which are limited to predefined entities, and Large Language Models (LLMs) that, despite their flexibility, are costly and large for resource-constrained scenarios.
-This particular version utilize bi-encoder architecture, where textual encoder is [team-lucid/DeBERTa v3 small](team-lucid/deberta-v3-small-korean) and entity label encoder is sentence transformer - [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3).
 Such architecture brings several advantages over uni-encoder GLiNER:

 GLiNER is a Named Entity Recognition (NER) model capable of identifying any entity type using a bidirectional transformer encoders (BERT-like). It provides a practical alternative to traditional NER models, which are limited to predefined entities, and Large Language Models (LLMs) that, despite their flexibility, are costly and large for resource-constrained scenarios.
+This particular version utilize bi-encoder architecture, where textual encoder is [team-lucid/DeBERTa v3 small](https://huggingface.co/team-lucid/deberta-v3-base-korean) and entity label encoder is sentence transformer - [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3).
 Such architecture brings several advantages over uni-encoder GLiNER: