Sentence Similarity
sentence-transformers
PyTorch
Safetensors
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use nuvocare/WikiMedical_sent_biobert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use nuvocare/WikiMedical_sent_biobert with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("nuvocare/WikiMedical_sent_biobert") 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 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("nuvocare/WikiMedical_sent_biobert") model = AutoModel.from_pretrained("nuvocare/WikiMedical_sent_biobert", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 14ccfa369cba8478372beab6d42ef906498c7d9e4d0a20f380ef35e9581a97c3
- Size of remote file:
- 433 MB
- SHA256:
- adb8ecf0bac23c56d33534d8b83a8113954d68af60cbb16724084fccd231d98d
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