Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
question-answering
text-embeddings-inference
Instructions to use pinecone/mpnet-retriever-discourse with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use pinecone/mpnet-retriever-discourse with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("pinecone/mpnet-retriever-discourse") 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 pinecone/mpnet-retriever-discourse with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("pinecone/mpnet-retriever-discourse") model = AutoModel.from_pretrained("pinecone/mpnet-retriever-discourse") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e167e3caadf6b350cbaff51999b6a7f93bff805d0c78282e61b80a811dbf3e09
- Size of remote file:
- 438 MB
- SHA256:
- 9fcea9fb873837ee99b9b6a4e08a8538f92a12d6e381a38db353581e8f693ecc
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