sentence-transformers How to use truong1301/bi-encode-HG-DOCS with sentence-transformers:
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("truong1301/bi-encode-HG-DOCS")
sentences = [
"How can AnimateDiff, a motion adapter for pretrained diffusion models, be used to generate videos from images?",
"Performs a single real input radix-2 transformation on the provided data Kind: instance method ofP2FFT The input data array The output data array The output offset The input offset The step",
"AnimateDiffis an adapter model that inserts a motion module into a pretrained diffusion model to animate an image. The adapter is trained on video clips to learn motion which is used to condition the generation process to create a video. It is faster and easier to only train the adapter and it can be loaded into most diffusion models, effectively turning them into “video models”. Start by loading aMotionAdapter. Then load a finetuned Stable Diffusion model with theAnimateDiffPipeline. Create a prompt and generate the video.",
"Utility class to handle streaming of tokens generated by whisper speech-to-text models. Callback functions are invoked when each of the following events occur: Kind: static class ofgeneration/streamers"
]
embeddings = model.encode(sentences)
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [4, 4] 
