How to use T-Blue/tsdae_pro_text2vec with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("T-Blue/tsdae_pro_text2vec") sentences = [ "च बच 𑀱चपच𑀟 पच पच 𑀙णच𑀪 𑀱च𑀳च 𑀠च𑀢 𑀳𑀫𑁦𑀞च𑀪न𑀣च पच 𑀞𑀱चलल𑁣 पच𑀪𑀢𑀫𑀢𑀟 ल𑁣𑀞चत𑀢𑀟 𑀱च𑀳च𑀟 𑀳च𑀠न 𑀟च𑀳च𑀪च 𑀱च𑀟𑀣च च ल𑁦खच𑀟प𑁦 लच𑀳 धलच𑀟च𑀳 𑀣𑀢ख𑀢𑀳𑀢𑀨𑀟", " च𑀟 पच𑀟पच𑀟त𑁦 पच च 𑀠चप𑀳चण𑀢𑀟 गणच𑀪 पच𑀞च𑀪च𑀪 𑁦च𑀳पल𑁦𑀢ब𑀫 च 𑀤चढ𑁦𑀟 𑀲𑀢𑀣𑀣च ब𑀱च𑀟𑀢 𑀟च 𑀳𑀫𑁦𑀞च𑀪च𑀪 𑀭थथर च𑀠𑀠च पच 𑀳𑀫च 𑀞चण𑁦 च 𑀤चढ𑁦𑀟𑀯", " च 𑀪च𑀟च𑀪 ठ𑀖 बच 𑀱चपच𑀟 𑀘च𑀟च𑀢𑀪न च 𑀳𑀫𑁦𑀞च𑀪च𑀪 ठ𑀧ठ𑀰 पच 𑀞च𑀲च पच𑀪𑀢𑀫𑀢 पच 𑀤च𑀠च 𑀠चपच𑀳𑀫𑀢णच𑀪 𑀙णच𑀪 𑀱च𑀳च 𑀠च𑀢 𑀞च𑀪च𑀟त𑀢𑀟 𑀳𑀫𑁦𑀞च𑀪न𑀣च पच त𑀢 𑀞𑀱चलल𑁣 च पच𑀪𑀢𑀫𑀢𑀟 ढच𑀪तच ल𑁣𑀞चत𑀢𑀟 𑀣च पच त𑀢 च 𑀱च𑀳च𑀟 𑀣च 𑀳न𑀞च 𑀳च𑀠न 𑀟च𑀳च𑀪च 𑀱च𑀟𑀣च 𑀞न𑀟ब𑀢णच𑀪 पच ढच𑀪त𑁦ल𑁣𑀟च 𑀬ष𑀧 च 𑀞च𑀟 ल𑁦खच𑀟प𑁦 लच𑀳 धलच𑀟च𑀳 च 𑀱च𑀳च𑀟 ध𑀪𑀢𑀠𑁦𑀪च 𑀣𑀢ख𑀢𑀳𑀢𑀨𑀟 𑀯", " च 𑀞च𑀞च𑀪 𑀱च𑀳च𑀟𑀳च 𑀟च ढ𑀢णन च त𑀢𑀞𑀢𑀟 ठ𑀧ठ𑀭𑀦 णच 𑀤च𑀠च 𑀣च𑀟 𑀱च𑀳च च 𑀞नल𑁣ढ 𑀣𑀢𑀟 𑀞न𑀠च णच पच𑀢𑀠च𑀞च 𑀠न𑀳न 𑀳न𑀟 त𑀢 𑁦पपच𑀟 ठ𑀧ठ𑀭𑀦 𑀞न𑀠च च𑀟 𑀟च𑀣च 𑀳𑀫𑀢 ब𑀱च𑀟𑀢𑀟 बच𑀳च𑀪 𑀞च𑀞च𑀪 𑀱च𑀳च𑀯" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4]
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