How to use TakalaWang/qwen3-embedding-4B-code-search with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("TakalaWang/qwen3-embedding-4B-code-search") sentences = [ "Collects the tracing data from the given parameters.\n :param request: The Flask request.\n :param response: The flask response.\n :param error: The error occurred if any.\n :param latency: The time elapsed to process the request.\n :return: The tracing data.", "def enum_to_yaml(cls: Type[T_EnumToYAML], representer: Representer, data: T_EnumToYAML) -> ruamel.yaml.nodes.ScalarNode:\n \n return representer.represent_scalar(\n f\"!{cls.__name__}\",\n f\"{str(data)}\"\n )", "def subclasses(self, inherited=False):\n \n data = clips.data.DataObject(self._env)\n\n lib.EnvClassSubclasses(self._env, self._cls, data.byref, int(inherited))\n\n for klass in classes(self._env, data.value):\n yield klass", "def identify(self, req, resp, resource, uri_kwargs):\n \n header = req.get_header(, False)\n auth = header.split() if header else None\n\n if auth is None or auth[0].lower() != :\n return None\n\n if len(auth) != 2:\n raise HTTPBadRequest(\n \"Invalid Authorization header\",\n \"The Authorization header for Token auth should be in form:\\n\"\n \"Authorization: Token <token_value>\"\n )\n\n return auth[1]" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4]
