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README.md
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---
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license: apache-2.0
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language:
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-
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pipeline_tag: token-classification
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---
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# Model Card for GLiNER-base
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GLiNER is a Named Entity Recognition (NER) model capable of identifying any entity type using a bidirectional transformer encoder (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.
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## Links
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* Paper: https://arxiv.org/abs/2311.08526
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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```
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---
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license: apache-2.0
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language:
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- en
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pipeline_tag: token-classification
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datasets:
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- Universal-NER/Pile-NER-type
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---
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# Model Card for GLiNER-base
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GLiNER is a Named Entity Recognition (NER) model capable of identifying any entity type using a bidirectional transformer encoder (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.
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This version has been trained on the Pile-NER dataset (Research purpose). Commercially permission versions are available (urchade/gliner_smallv2, urchade/gliner_mediumv2, urchade/gliner_largev2)
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## Links
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* Paper: https://arxiv.org/abs/2311.08526
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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```
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