How to use gemasphi/laprador-query-encoder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("gemasphi/laprador-query-encoder") 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]
How to use gemasphi/laprador-query-encoder with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("gemasphi/laprador-query-encoder") model = AutoModel.from_pretrained("gemasphi/laprador-query-encoder", device_map="auto")
https://huggingface.co/gemasphi/laprador-query-encoder/resolve/refs%2Fpr%2F1/tokenizer.json
hf download hf://gemasphi/laprador-query-encoder@refs/pr/1/tokenizer.json
curl -L -o tokenizer.json https://huggingface.co/gemasphi/laprador-query-encoder/resolve/refs%2Fpr%2F1/tokenizer.json
