How to use LeoCristt/hackathon-embedding-model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LeoCristt/hackathon-embedding-model") 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]
How to use LeoCristt/hackathon-embedding-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="LeoCristt/hackathon-embedding-model")
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("LeoCristt/hackathon-embedding-model") model = AutoModel.from_pretrained("LeoCristt/hackathon-embedding-model", device_map="auto")
https://huggingface.co/LeoCristt/hackathon-embedding-model/resolve/c741c3864ca6221e915d0df999f6377e9eedeca5/sentence_bert_config.json
hf download hf://LeoCristt/hackathon-embedding-model@c741c3864ca6221e915d0df999f6377e9eedeca5/sentence_bert_config.json
curl -L -o sentence_bert_config.json https://huggingface.co/LeoCristt/hackathon-embedding-model/resolve/c741c3864ca6221e915d0df999f6377e9eedeca5/sentence_bert_config.json
