sentence-transformers How to use benjamintli/modernbert-codesearchnet with sentence-transformers:
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("benjamintli/modernbert-codesearchnet")
sentences = [
"Return a Python AST node for `recur` occurring inside a `loop`.",
"def _reset(self, name=None):\n \"\"\"Revert specified property to default value\n\n If no property is specified, all properties are returned to default.\n \"\"\"\n if name is None:\n for key in self._props:\n if isinstance(self._props[key], basic.Property):\n self._reset(key)\n return\n if name not in self._props:\n raise AttributeError(\"Input name '{}' is not a known \"\n \"property or attribute\".format(name))\n if not isinstance(self._props[name], basic.Property):\n raise AttributeError(\"Cannot reset GettableProperty \"\n \"'{}'\".format(name))\n if name in self._defaults:\n val = self._defaults[name]\n else:\n val = self._props[name].default\n if callable(val):\n val = val()\n setattr(self, name, val)",
"def cancel(self):\n '''\n Cancel a running workflow.\n\n Args:\n None\n\n Returns:\n None\n '''\n if not self.id:\n raise WorkflowError('Workflow is not running. Cannot cancel.')\n\n if self.batch_values:\n self.workflow.batch_workflow_cancel(self.id)\n else:\n self.workflow.cancel(self.id)",
"def __loop_recur_to_py_ast(ctx: GeneratorContext, node: Recur) -> GeneratedPyAST:\n \"\"\"Return a Python AST node for `recur` occurring inside a `loop`.\"\"\"\n assert node.op == NodeOp.RECUR\n\n recur_deps: List[ast.AST] = []\n recur_targets: List[ast.Name] = []\n recur_exprs: List[ast.AST] = []\n for name, expr in zip(ctx.recur_point.binding_names, node.exprs):\n expr_ast = gen_py_ast(ctx, expr)\n recur_deps.extend(expr_ast.dependencies)\n recur_targets.append(ast.Name(id=name, ctx=ast.Store()))\n recur_exprs.append(expr_ast.node)\n\n if len(recur_targets) == 1:\n assert len(recur_exprs) == 1\n recur_deps.append(ast.Assign(targets=recur_targets, value=recur_exprs[0]))\n else:\n recur_deps.append(\n ast.Assign(\n targets=[ast.Tuple(elts=recur_targets, ctx=ast.Store())],\n value=ast.Tuple(elts=recur_exprs, ctx=ast.Load()),\n )\n )\n recur_deps.append(ast.Continue())\n\n return GeneratedPyAST(node=ast.NameConstant(None), dependencies=recur_deps)"
]
embeddings = model.encode(sentences)
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [4, 4] 
