Sentence Similarity
sentence-transformers
Safetensors
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use Rupesh2/sbert_paraphrase_MiniLM_L6_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Rupesh2/sbert_paraphrase_MiniLM_L6_v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Rupesh2/sbert_paraphrase_MiniLM_L6_v2") 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] - Transformers
How to use Rupesh2/sbert_paraphrase_MiniLM_L6_v2 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Rupesh2/sbert_paraphrase_MiniLM_L6_v2") model = AutoModel.from_pretrained("Rupesh2/sbert_paraphrase_MiniLM_L6_v2") - Notebooks
- Google Colab
- Kaggle
Ctrl+K