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
| { | |
| "__version__": { | |
| "sentence_transformers": "2.1.0", | |
| "transformers": "4.11.3", | |
| "pytorch": "1.9.1" | |
| } | |
| } |