# Bidirectional Transformer

GLiNER is a general-purpose named entity recognition (NER) model capable of identifying any type of entity, providing a practical alternative to traditional NER models.

GLiNER is a general-purpose named entity recognition (NER) model capable of identifying any type of entity, providing a practical alternative to traditional NER models while addressing the high resource consumption issues of large language models.

GLiNER is a general-purpose Named Entity Recognition (NER) model capable of identifying any entity type through a bidirectional Transformer encoder.

GLiNER is a bidirectional Transformer-based general named entity recognition model, specifically optimized for Italian.

GLiNER is a general-purpose named entity recognition (NER) model capable of identifying any type of entity, providing a practical alternative to traditional NER models and large language models.

GLiNER is a Named Entity Recognition (NER) model capable of identifying any entity type, providing a practical alternative to traditional NER models.

GLiNER is a general-purpose Named Entity Recognition (NER) model capable of identifying any type of entity through a bidirectional Transformer encoder, providing a practical alternative to traditional NER models.

GLiNER is a multilingual Named Entity Recognition (NER) model capable of identifying any entity type through a bidirectional Transformer encoder, providing a flexible alternative to traditional NER models.

XLM-RoBERTa-XL is a multilingual model pre-trained on 2.5TB of filtered CommonCrawl data, covering 100 languages.

A large English language model pre-trained with masked language modeling objectives, using improved BERT training methods