Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use gemasphi/mcontriever-msmarco with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use gemasphi/mcontriever-msmarco with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("gemasphi/mcontriever-msmarco") 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 gemasphi/mcontriever-msmarco with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("gemasphi/mcontriever-msmarco") model = AutoModel.from_pretrained("gemasphi/mcontriever-msmarco") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7bea98086ba95bdb7348f6bf291b0e0d51631aaa221f76c60b5a9cc728937a4f
- Size of remote file:
- 711 MB
- SHA256:
- 1b2161c0c4ec8d050b5943df0cb3d491d1e2ede7f93fa834bcbf07570717ee9d
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