How to use claritylab/zero-shot-vanilla-bi-encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="claritylab/zero-shot-vanilla-bi-encoder")
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("claritylab/zero-shot-vanilla-bi-encoder") model = AutoModel.from_pretrained("claritylab/zero-shot-vanilla-bi-encoder", device_map="auto")
How to use claritylab/zero-shot-vanilla-bi-encoder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("claritylab/zero-shot-vanilla-bi-encoder") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3]
https://huggingface.co/claritylab/zero-shot-vanilla-bi-encoder/resolve/main/tokenizer.json
hf download hf://claritylab/zero-shot-vanilla-bi-encoder/tokenizer.json
curl -L -o tokenizer.json https://huggingface.co/claritylab/zero-shot-vanilla-bi-encoder/resolve/main/tokenizer.json
