Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use Muennighoff/SBERT-base-msmarco-bitfit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Muennighoff/SBERT-base-msmarco-bitfit with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Muennighoff/SBERT-base-msmarco-bitfit") 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 Muennighoff/SBERT-base-msmarco-bitfit with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Muennighoff/SBERT-base-msmarco-bitfit") model = AutoModel.from_pretrained("Muennighoff/SBERT-base-msmarco-bitfit") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6c69efc11fb9dd67eb649fa3959d48e4be2be127b37899122ee101faa66460cc
- Size of remote file:
- 438 MB
- SHA256:
- e3ede72550fa5ce72ba54cd20a27dc54382ec067fab46171bc157cc439364e23
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