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import re
from typing import List
from embeddings.embedding.auto_flair import AutoFlairDocumentEmbedding
from flair.data import Sentence
from numpy import typing as nt
from unidecode import unidecode
embedding = AutoFlairDocumentEmbedding.from_hub("clarin-pl/word2vec-kgr10")
PLOT_COLOR_PALETTE = [
"#FAEBD7",
"#00FFFF",
"#7FFFD4",
"#000000",
"#0000FF",
"#8A2BE2",
"#A52A2A",
"#DEB887",
"#5F9EA0",
"#7FFF00",
"#D2691E",
"#FF7F50",
"#6495ED",
"#FFF8DC",
"#DC143C",
"#00FFFF",
"#00008B",
"#008B8B",
"#B8860B",
"#A9A9A9",
"#006400",
"#BDB76B",
"#8B008B",
"#556B2F",
"#FF8C00",
"#9932CC",
"#8B0000",
"#E9967A",
"#8FBC8F",
"#2F4F4F",
"#00CED1",
"#FFD700",
"#DAA520",
"#808080",
"#FF69B4",
"#4B0082",
"#CD5C5C",
"#7CFC00",
"#F08080",
"#66CDAA",
]
def flatten_list(main_list: List[List]) -> List:
return [item for sublist in main_list for item in sublist]
def count_num_of_characters(text: str) -> int:
return len(re.sub(r"[^a-zA-Z]", "", unidecode(text)))
def count_num_of_words(text: str) -> int:
return len(re.sub(r"[^a-zA-Z ]", "", unidecode(text)).split(" "))
def embed_sentence(sentence: str) -> nt.NDArray:
sentence = Sentence(sentence)
embedding.embed([sentence])
return sentence.embedding.numpy()
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