How to use Nerdofdot/Nerdofdot_distilbert-base-uncased_TM_FTM with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Nerdofdot/Nerdofdot_distilbert-base-uncased_TM_FTM") 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 Nerdofdot/Nerdofdot_distilbert-base-uncased_TM_FTM with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Nerdofdot/Nerdofdot_distilbert-base-uncased_TM_FTM") model = AutoModel.from_pretrained("Nerdofdot/Nerdofdot_distilbert-base-uncased_TM_FTM")
