How to use agentlans/snowflake-arctic-embed-s-nli with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("agentlans/snowflake-arctic-embed-s-nli") 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 agentlans/snowflake-arctic-embed-s-nli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="agentlans/snowflake-arctic-embed-s-nli")
# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("agentlans/snowflake-arctic-embed-s-nli") model = AutoModelForSequenceClassification.from_pretrained("agentlans/snowflake-arctic-embed-s-nli")
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