sentence-transformers How to use Rsr2425/simplify-ft-arctic-embed-l with sentence-transformers:
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("Rsr2425/simplify-ft-arctic-embed-l")
sentences = [
"1. What is the first step to take when implementing architecture as code according to the provided context?\n2. How should the content of each file be formatted when outputting code?",
"architecture is, in the end, implemented as code.\\\\n\\\\nThink step by step and reason yourself to the right decisions to make sure we get it right.\\\\nYou will first lay out the names of the core classes, functions, methods that will be necessary, as well as a quick comment on their purpose.\\\\n\\\\nThen you will output the content of each file including ALL code.\\\\nEach file must strictly follow a markdown code block format, where the following tokens must be replaced such that\\\\nFILENAME is the lowercase file name including the file extension,\\\\nLANG is the markup code block language for the code\\'s language, and CODE is the code:\\\\n\\\\nFILENAME\\\\n\\`\\`\\`LANG\\\\nCODE\\\\n\\`\\`\\`\\\\n\\\\nYou will start with the \\\\\"entrypoint\\\\\" file, then go to the",
"Stream tokens:\nfor message, metadata in graph.stream( {\"question\": \"What is Task Decomposition?\"}, stream_mode=\"messages\"): print(message.content, end=\"|\")\n|Task| decomposition| is| the| process| of| breaking| down| complex| tasks| into| smaller|,| more| manageable| steps|.| It| can| be| achieved| through| techniques| like| Chain| of| Thought| (|Co|T|)| prompting|,| which| encourages| the| model| to| think| step| by| step|,| or| through| more| structured| methods| like| the| Tree| of| Thoughts|.| This| approach| not| only| simplifies| task| execution| but| also| provides| insights| into| the| model|'s| reasoning| process|.||\ntipFor async invocations, use:result = await graph.ainvoke(...)andasync for step in graph.astream(...):",
"return {\"answer\": response.content}graph_builder = StateGraph(State).add_sequence([analyze_query, retrieve, generate])graph_builder.add_edge(START, \"analyze_query\")graph = graph_builder.compile()"
]
embeddings = model.encode(sentences)
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [4, 4] 
