How to use Elise-hf/distilbert-base-pwc-task-multi-label-classification with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Elise-hf/distilbert-base-pwc-task-multi-label-classification") 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 Elise-hf/distilbert-base-pwc-task-multi-label-classification with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Elise-hf/distilbert-base-pwc-task-multi-label-classification") model = AutoModel.from_pretrained("Elise-hf/distilbert-base-pwc-task-multi-label-classification", device_map="auto")
