Sentence Similarity
sentence-transformers
PyTorch
Safetensors
Transformers
xlm-roberta
feature-extraction
text-embeddings-inference
Instructions to use nuvocare/WikiMedical_sent_biobert_multi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use nuvocare/WikiMedical_sent_biobert_multi with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("nuvocare/WikiMedical_sent_biobert_multi") 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 nuvocare/WikiMedical_sent_biobert_multi with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("nuvocare/WikiMedical_sent_biobert_multi") model = AutoModel.from_pretrained("nuvocare/WikiMedical_sent_biobert_multi") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f6586f41a732c609768cf5c93b49bc7a3eb7434c67ba410c37906dfabd50e127
- Size of remote file:
- 1.11 GB
- SHA256:
- 9222387e4b9ff40a4a03dbd6084aecdbf4d571467d586f1f357a4c4b2f5962f0
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