Sentence Similarity
sentence-transformers
PyTorch
Transformers
mpnet
feature-extraction
text-embeddings-inference
Instructions to use HgThinker/multi-qa-mpnet-base-dot-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use HgThinker/multi-qa-mpnet-base-dot-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("HgThinker/multi-qa-mpnet-base-dot-v1") 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 HgThinker/multi-qa-mpnet-base-dot-v1 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("HgThinker/multi-qa-mpnet-base-dot-v1") model = AutoModel.from_pretrained("HgThinker/multi-qa-mpnet-base-dot-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a901d790d8829f5b7988bbc83798c8974e589c2e6ec992cb60327d49a3958db8
- Size of remote file:
- 438 MB
- SHA256:
- 20b657f770ecea3c5732fe79ce31923942c4a9b31ec7b4f25d2052053a12242d
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