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:
# pip install -U transformers accelerate # 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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from nuvocare/WikiMedical_sent_biobert_multi: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/nuvocare/WikiMedical_sent_biobert_multi/resolve/main/tokenizer.json
- Command line
-
hf download hf://nuvocare/WikiMedical_sent_biobert_multi/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/nuvocare/WikiMedical_sent_biobert_multi/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- 3b429a09d329f856192cd5dc7d96a255df52cd54bc6504619ccaebac2702b251
- Size of remote file:
- 17.1 MB
- SHA256:
- 9fccef8772d9532204f02e10085a41c171b0f001c7d42fb5cc9c3b58da5127ab
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