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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3717ab22ee46238372a14694286443b54cf58da4724344350c7512dfcd8ab532
- Size of remote file:
- 438 MB
- SHA256:
- 8ff217991e009bcffed0a55974ec2113a3ae52ed971dfe84c61a2993a8e14da2
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