# LSTM-CRF architecture

Flair's standard 4-class NER model for Danish, based on Transformer embeddings and LSTM-CRF architecture

Flair's built-in fast model for 18-class English named entity recognition, trained on the Ontonotes dataset

Flair's built-in standard English phrase chunking model for identifying grammatical structures such as noun phrases and verb phrases in sentences.

Flair's built-in standard English 4-category named entity recognition model, based on Flair embeddings and LSTM-CRF architecture, achieving an F1 score of 93.06 on the CoNLL-03 dataset.

Flair's built-in standard German 4-class NER model, based on Flair embeddings and LSTM-CRF architecture, achieves an F1 score of 87.94 on the German revised version of CoNLL-03.

Flair's built-in fast English POS tagging model, trained on Ontonotes dataset with an F1 score of 98.10.

Flair's standard 4-class French NER model, based on Flair word embeddings and LSTM-CRF architecture, achieves an F1 score of 90.61 on the WikiNER dataset.

Flair's standard 4-class NER model, suitable for named entity recognition tasks in English, German, Dutch, and Spanish

Flair's built-in English 18-class named entity recognition model, trained on the Ontonotes dataset with an F1 score of 89.27.