How to use Nutanix/bge-base-mbpp with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Nutanix/bge-base-mbpp") sentences = [ "Write a function to extract every specified element from a given two dimensional list.", "def nCr_mod_p(n, r, p): \r\n\tif (r > n- r): \r\n\t\tr = n - r \r\n\tC = [0 for i in range(r + 1)] \r\n\tC[0] = 1 \r\n\tfor i in range(1, n + 1): \r\n\t\tfor j in range(min(i, r), 0, -1): \r\n\t\t\tC[j] = (C[j] + C[j-1]) % p \r\n\treturn C[r] ", "import cmath\r\ndef len_complex(a,b):\r\n cn=complex(a,b)\r\n length=abs(cn)\r\n return length", "def specified_element(nums, N):\r\n result = [i[N] for i in nums]\r\n return result" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4]
