How to use Devy1/MiniLM-cosqa-256 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Devy1/MiniLM-cosqa-256") sentences = [ "python multiprocessing show cpu count", "def unique(seq):\n \"\"\"Return the unique elements of a collection even if those elements are\n unhashable and unsortable, like dicts and sets\"\"\"\n cleaned = []\n for each in seq:\n if each not in cleaned:\n cleaned.append(each)\n return cleaned", "def is_in(self, point_x, point_y):\n \"\"\" Test if a point is within this polygonal region \"\"\"\n\n point_array = array(((point_x, point_y),))\n vertices = array(self.points)\n winding = self.inside_rule == \"winding\"\n result = points_in_polygon(point_array, vertices, winding)\n return result[0]", "def machine_info():\n \"\"\"Retrieve core and memory information for the current machine.\n \"\"\"\n import psutil\n BYTES_IN_GIG = 1073741824.0\n free_bytes = psutil.virtual_memory().total\n return [{\"memory\": float(\"%.1f\" % (free_bytes / BYTES_IN_GIG)), \"cores\": multiprocessing.cpu_count(),\n \"name\": socket.gethostname()}]" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4]
