Sentence Similarity
sentence-transformers
Safetensors
Transformers
English
bert
feature-extraction
text-embeddings-inference
Instructions to use Tao-AI-Informatics/NA-SapBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Tao-AI-Informatics/NA-SapBERT with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Tao-AI-Informatics/NA-SapBERT") 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 Tao-AI-Informatics/NA-SapBERT with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Tao-AI-Informatics/NA-SapBERT") model = AutoModel.from_pretrained("Tao-AI-Informatics/NA-SapBERT") - Notebooks
- Google Colab
- Kaggle
| { | |
| "__version__": { | |
| "sentence_transformers": "2.6.1", | |
| "transformers": "4.38.2", | |
| "pytorch": "2.10.0+cu128" | |
| }, | |
| "prompts": {}, | |
| "default_prompt_name": null | |
| } |