CodeRankEmbed

83.3K
45
8K
GPT-3 class
license:mit
by
nomic-ai
Code Model
OTHER
Fair
83K downloads
Community-tested
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Quick Summary

`CodeRankEmbed` is a 137M bi-encoder supporting 8192 context length for code retrieval.

Code Examples

Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)
Usagepython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
query_embeddings = model.encode(queries)
print(query_embeddings)
code_embeddings = model.encode(codes)
print(code_embeddings)

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