How to use embedding-data/distilroberta-base-sentence-transformer with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("embedding-data/distilroberta-base-sentence-transformer") 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 embedding-data/distilroberta-base-sentence-transformer with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("embedding-data/distilroberta-base-sentence-transformer") model = AutoModel.from_pretrained("embedding-data/distilroberta-base-sentence-transformer", device_map="auto")
