Duplicated from intfloat/multilingual-e5-large-instruct
How to use vllm-project/multilingual-e5-large-instruct with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("vllm-project/multilingual-e5-large-instruct") 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 vllm-project/multilingual-e5-large-instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="vllm-project/multilingual-e5-large-instruct")
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("vllm-project/multilingual-e5-large-instruct") model = AutoModel.from_pretrained("vllm-project/multilingual-e5-large-instruct", device_map="auto")
https://huggingface.co/vllm-project/multilingual-e5-large-instruct/resolve/main/config_sentence_transformers.json
hf download hf://vllm-project/multilingual-e5-large-instruct/config_sentence_transformers.json
curl -L -o config_sentence_transformers.json https://huggingface.co/vllm-project/multilingual-e5-large-instruct/resolve/main/config_sentence_transformers.json
