def embed_sentence(sentence: str,
layers: str = "1",
use_scalar_mix: bool = False) -> Sentence:
embeddings = TransformerXLEmbeddings(model=transfo_model,
layers=layers,
use_scalar_mix=use_scalar_mix)
flair_sentence = Sentence(sentence)
embeddings.embed(flair_sentence)
return flair_sentence
def embed_sentence(sentence: str,
layers: str = '1',
use_scalar_mix: bool = False) -> Sentence:
embeddings = TransformerXLEmbeddings(
pretrained_model_name_or_path=transfo_model,
layers=layers,
use_scalar_mix=use_scalar_mix)
flair_sentence = Sentence(sentence)
embeddings.embed(flair_sentence)
return flair_sentence
cos = CosineSimilarity(dim=1, eps=1e-6)
dynamic = True
graph = False
doc_embeddings = []
scores = []
stacked_embeddings = DocumentPoolEmbeddings([
#WordEmbeddings('en'),
#WordEmbeddings('glove'),
#WordEmbeddings('extvec'),#ELMoEmbeddings('original'),
#BertEmbeddings('bert-base-cased'),
#FlairEmbeddings('news-forward-fast'),
#FlairEmbeddings('news-backward-fast'),
#OpenAIGPTEmbeddings()
TransformerXLEmbeddings()
]) #, mode='max')
def set_card():
print("Input the Card Text, press Ctrl-D to end text entry")
card = sys.stdin.read() #input("Input the Card Text: ")
card_tag = input(
"Input the card_tag, or a -1 to summarize in-terms of the card itself: "
)
card = str(card)
if str(
card_tag
) == "-1": #This will not work with large documents when bert is enabled
card_tag = Sentence(str(card))
tag_str = ""
def get_transformerxl(model_name):
return TransformerXLEmbeddings(model_name)