sentence-transformers How to use kamp0010/all-minilm-l6-v2-code with sentence-transformers:
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("kamp0010/all-minilm-l6-v2-code")
sentences = [
"Modify the inner parameters of the Kepler propagator in order to place\n the spacecraft in the right Sphere of Influence",
"func (c *Conn) SetDeadline(t time.Time) error {\n\treturn c.p.SetDeadline(t)\n}",
"def _change_soi(self, body):\n \n\n if body == self.central:\n self.bodies = [self.central]\n self.step = self.central_step\n self.active = self.central.name\n self.frame = self.central.name\n else:\n soi = self.SOI[body.name]\n self.bodies = [body]\n self.step = self.alt_step\n self.active = body.name\n self.frame = soi.frame",
"def main(args=None):\n \"\"\"\"\"\"\n parser = _parser()\n\n # Python 2 will error 'too few arguments' if no subcommand is supplied.\n # No such error occurs in Python 3, which makes it feasible to check\n # whether a subcommand was provided (displaying a help message if not).\n # argparse internals vary significantly over the major versions, so it's\n # much easier to just override the args passed to it. In this case, print\n # the usage message if there are no args.\n if args is None and len(sys.argv) <= 1:\n sys.argv.append('--help')\n\n options = parser.parse_args(args)\n\n # pass options to subcommand\n options.func(options)\n\n return 0"
]
embeddings = model.encode(sentences)
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [4, 4] 
