urchade

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. Paper: https://arxiv.org/abs/2311.08526 Repository: https://github.com/urchade/GLiNER | Release | Model Name | # of Parameters | Language | License | | - | - | - | - | - | | v0 | urchade/glinerbase urchade/glinermulti | 209M 209M | English Multilingual | cc-by-nc-4.0 | | v1 | urchade/glinersmall-v1 urchade/glinermedium-v1 urchade/glinerlarge-v1 | 166M 209M 459M | English English English | cc-by-nc-4.0 | | v2 | urchade/glinersmall-v2 urchade/glinermedium-v2 urchade/glinerlarge-v2 | 166M 209M 459M | English English English | apache-2.0 | | v2.1 | urchade/glinersmall-v2.1 urchade/glinermedium-v2.1 urchade/glinerlarge-v2.1 urchade/glinermulti-v2.1 | 166M 209M 459M 209M | English English English Multilingual | apache-2.0 | Installation To use this model, you must install the GLiNER Python library: Usage Once you've downloaded the GLiNER library, you can import the GLiNER class. You can then load this model using `GLiNER.frompretrained` and predict entities with `predictentities`. Model Authors The model authors are: Urchade Zaratiana Nadi Tomeh Pierre Holat Thierry Charnois

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. Paper: https://arxiv.org/abs/2311.08526 Repository: https://github.com/urchade/GLiNER | Release | Model Name | # of Parameters | Language | License | | - | - | - | - | - | | v0 | urchade/glinerbase urchade/glinermulti | 209M 209M | English Multilingual | cc-by-nc-4.0 | | v1 | urchade/glinersmall-v1 urchade/glinermedium-v1 urchade/glinerlarge-v1 | 166M 209M 459M | English English English | cc-by-nc-4.0 | | v2 | urchade/glinersmall-v2 urchade/glinermedium-v2 urchade/glinerlarge-v2 | 166M 209M 459M | English English English | apache-2.0 | | v2.1 | urchade/glinersmall-v2.1 urchade/glinermedium-v2.1 urchade/glinerlarge-v2.1 urchade/glinermulti-v2.1 | 166M 209M 459M 209M | English English English Multilingual | apache-2.0 | Installation To use this model, you must install the GLiNER Python library: Usage Once you've downloaded the GLiNER library, you can import the GLiNER class. You can then load this model using `GLiNER.frompretrained` and predict entities with `predictentities`. Model Authors The model authors are: Urchade Zaratiana Nadi Tomeh Pierre Holat Thierry Charnois

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. Paper: https://arxiv.org/abs/2311.08526 Repository: https://github.com/urchade/GLiNER | Release | Model Name | # of Parameters | Language | License | | - | - | - | - | - | | v0 | urchade/glinerbase urchade/glinermulti | 209M 209M | English Multilingual | cc-by-nc-4.0 | | v1 | urchade/glinersmall-v1 urchade/glinermedium-v1 urchade/glinerlarge-v1 | 166M 209M 459M | English English English | cc-by-nc-4.0 | | v2 | urchade/glinersmall-v2 urchade/glinermedium-v2 urchade/glinerlarge-v2 | 166M 209M 459M | English English English | apache-2.0 | | v2.1 | urchade/glinersmall-v2.1 urchade/glinermedium-v2.1 urchade/glinerlarge-v2.1 urchade/glinermulti-v2.1 | 166M 209M 459M 209M | English English English Multilingual | apache-2.0 | Installation To use this model, you must install the GLiNER Python library: Usage Once you've downloaded the GLiNER library, you can import the GLiNER class. You can then load this model using `GLiNER.frompretrained` and predict entities with `predictentities`. Model Authors The model authors are: Urchade Zaratiana Nadi Tomeh Pierre Holat Thierry Charnois

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. This version has been trained on the Pile-NER dataset (Research purpose). Commercially permission versions are available (urchade/glinersmallv2, urchade/glinermediumv2, urchade/glinerlargev2) Paper: https://arxiv.org/abs/2311.08526 Repository: https://github.com/urchade/GLiNER | Release | Model Name | # of Parameters | Language | License | | - | - | - | - | - | | v0 | urchade/glinerbase urchade/glinermulti | 209M 209M | English Multilingual | cc-by-nc-4.0 | | v1 | urchade/glinersmall-v1 urchade/glinermedium-v1 urchade/glinerlarge-v1 | 166M 209M 459M | English English English | cc-by-nc-4.0 | | v2 | urchade/glinersmall-v2 urchade/glinermedium-v2 urchade/glinerlarge-v2 | 166M 209M 459M | English English English | apache-2.0 | | v2.1 | urchade/glinersmall-v2.1 urchade/glinermedium-v2.1 urchade/glinerlarge-v2.1 urchade/glinermulti-v2.1 | 166M 209M 459M 209M | English English English Multilingual | apache-2.0 | Installation To use this model, you must install the GLiNER Python library: Usage Once you've downloaded the GLiNER library, you can import the GLiNER class. You can then load this model using `GLiNER.frompretrained` and predict entities with `predictentities`. Model Authors The model authors are: Urchade Zaratiana Nadi Tomeh Pierre Holat Thierry Charnois