Sentence Similarity
sentence-transformers
PyTorch
Transformers
mpnet
feature-extraction
text-embeddings-inference
Instructions to use jamescalam/mpnet-qa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use jamescalam/mpnet-qa with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("jamescalam/mpnet-qa") 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 jamescalam/mpnet-qa with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("jamescalam/mpnet-qa") model = AutoModel.from_pretrained("jamescalam/mpnet-qa") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e41ef15adb29f5b7711e50af3d251e094d803d06c4556c426dc8bdd3a5e8054f
- Size of remote file:
- 711 kB
- SHA256:
- 88e8cacca6c33c3a142ce67b84e861e17e67664590e102c659ba631e13ef4bc0
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