training_flair.py

from flair.embeddings import FlairEmbeddings, BertEmbeddings, WordEmbeddings, DocumentRNNEmbeddings

# init Flair embeddings
flair_forward_embedding = FlairEmbeddings('multi-forward')
flair_backward_embedding = FlairEmbeddings('multi-backward')

# init BERT base (cases)
optional_embedding = BertEmbeddings('bert-base-cased')
# OR init ELMo (original)
# optional_embedding = ELMoEmbeddings('original')

word_embeddings = list(filter(None, [
    optional_embedding,
    FlairEmbeddings('news-forward'),
    FlairEmbeddings('news-backward'),
]))

# Initialize document embedding by passing list of word embeddings
document_embeddings = DocumentRNNEmbeddings(
    word_embeddings,
    hidden_size=512,
    reproject_words=True,
    reproject_words_dimension=256,
)