How to use clips/mfaq with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("clips/mfaq") 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 clips/mfaq with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("clips/mfaq") model = AutoModel.from_pretrained("clips/mfaq")
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[ { "idx": 0, "name": "0", "path": "", "type": "sentence_transformers.models.Transformer" }, { "idx": 1, "name": "1", "path": "1_Pooling", "type": "sentence_transformers.models.Pooling" } ]
