ONNX port of sentence-transformers/clip-ViT-B-32 for text classification and similarity searches.
Usage
Here's an example of performing inference using the model with FastEmbed.
from fastembed import TextEmbedding
documents = [
"You should stay, study and sprint.",
"History can only prepare us to be surprised yet again.",
]
model = TextEmbedding(model_name="Qdrant/clip-ViT-B-32-text")
embeddings = list(model.embed(documents))
# [
# array([1.57889184e-02, -2.21896712e-02, -1.40235685e-02, -2.36918423e-02, ...],
# dtype=float32)
# ]