Sentence Similarity
sentence-transformers
PyTorch
Safetensors
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use TimKond/S-PubMedBert-MedQuAD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use TimKond/S-PubMedBert-MedQuAD with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("TimKond/S-PubMedBert-MedQuAD") 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 TimKond/S-PubMedBert-MedQuAD with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("TimKond/S-PubMedBert-MedQuAD") model = AutoModel.from_pretrained("TimKond/S-PubMedBert-MedQuAD", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 55b546d7272b4950e8d2a04d37545426a9d97ccf9ef3b7fe9d40217ceab3e39b
- Size of remote file:
- 438 MB
- SHA256:
- f78b94740bd4b98020b72081dc20ecfb7fced9982a8f0b5b48e7d5da0892d48d
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