How to use h4c5/sts-camembert-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("h4c5/sts-camembert-base") 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 h4c5/sts-camembert-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="h4c5/sts-camembert-base")
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("h4c5/sts-camembert-base") model = AutoModel.from_pretrained("h4c5/sts-camembert-base", device_map="auto")
https://huggingface.co/h4c5/sts-camembert-base/resolve/5cec51eb13081350b5598f16df848875c257f2a5/tokenizer.json
hf download hf://h4c5/sts-camembert-base@5cec51eb13081350b5598f16df848875c257f2a5/tokenizer.json
curl -L -o tokenizer.json https://huggingface.co/h4c5/sts-camembert-base/resolve/5cec51eb13081350b5598f16df848875c257f2a5/tokenizer.json
