How to use msugimura/gatekeeper_agent_responding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("msugimura/gatekeeper_agent_responding") 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 msugimura/gatekeeper_agent_responding with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("msugimura/gatekeeper_agent_responding") model = AutoModel.from_pretrained("msugimura/gatekeeper_agent_responding", device_map="auto")
https://huggingface.co/msugimura/gatekeeper_agent_responding/resolve/ccb71a79ea69e2aad17eac09f58822f9bf99fabd/tokenizer.json
hf download hf://msugimura/gatekeeper_agent_responding@ccb71a79ea69e2aad17eac09f58822f9bf99fabd/tokenizer.json
curl -L -o tokenizer.json https://huggingface.co/msugimura/gatekeeper_agent_responding/resolve/ccb71a79ea69e2aad17eac09f58822f9bf99fabd/tokenizer.json
