How to use aspire/acge_text_embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("aspire/acge_text_embedding") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4]
You can use the following code
from pathlib import Path from transformers.convert_graph_to_onnx import convert # Handles all the above steps for you convert(framework="pt", model="your_path/acge_text_embedding", output=Path("your_path_temp/pytorch_model.onnx"), opset=11)
cite
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