embedding.py

from sentence_transformers import SentenceTransformer
from preprocess import meme_attribute, meme_filename, meme_list
# This model supports two prompts: "s2p_query" and "s2s_query" for sentence-to-passage and sentence-to-sentence tasks, respectively.
# They are defined in `config_sentence_transformers.json`


# you can also use this model without the features of `use_memory_efficient_attention` and `unpad_inputs`. It can be worked in CPU.
model = SentenceTransformer(
     "dunzhang/stella_en_400M_v5",
     trust_remote_code=True,
     device="cpu",
     config_kwargs={"use_memory_efficient_attention": False, "unpad_inputs": False}
)

docs_list = list(meme_attribute.values())

doc_embeddings = model.encode(docs_list)

embedded_dict = {key: embedding for key, embedding in zip(meme_attribute.keys(), doc_embeddings)}