Sentence Similarity
sentence-transformers
PyTorch
Transformers
mpnet
feature-extraction
text-embeddings-inference
Instructions to use JoBeer/multi-qa-mpnet-base-dot-v1-eclass with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use JoBeer/multi-qa-mpnet-base-dot-v1-eclass with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("JoBeer/multi-qa-mpnet-base-dot-v1-eclass") 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 JoBeer/multi-qa-mpnet-base-dot-v1-eclass with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("JoBeer/multi-qa-mpnet-base-dot-v1-eclass") model = AutoModel.from_pretrained("JoBeer/multi-qa-mpnet-base-dot-v1-eclass", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9c220895dc5e5cb41fe570166fe8f2be5f5fe970837b9c7197b0428e84faec7f
- Size of remote file:
- 438 MB
- SHA256:
- a1c83ee68619506b4feb45241aae283d8d3da7255f047b26746b9be0527a85f1
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