Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use pritamdeka/S-PubMedBert-MS-MARCO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use pritamdeka/S-PubMedBert-MS-MARCO with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("pritamdeka/S-PubMedBert-MS-MARCO") 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 pritamdeka/S-PubMedBert-MS-MARCO with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("pritamdeka/S-PubMedBert-MS-MARCO") model = AutoModel.from_pretrained("pritamdeka/S-PubMedBert-MS-MARCO") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6d2b655cf51094ae19ca10410c483754b7b003d4f53f2f531f02cf7319c4a035
- Size of remote file:
- 438 MB
- SHA256:
- 65911292f1477515dd3ac71ddf8ac54f12495b8a3eab81b1ebe3b0c86cde70eb
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