Sentence Similarity
sentence-transformers
PyTorch
ONNX
Safetensors
OpenVINO
Transformers
English
mpnet
fill-mask
feature-extraction
text-embeddings-inference
Instructions to use sentence-transformers/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 sentence-transformers/multi-qa-mpnet-base-dot-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sentence-transformers/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 sentence-transformers/multi-qa-mpnet-base-dot-v1 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/multi-qa-mpnet-base-dot-v1") model = AutoModelForMaskedLM.from_pretrained("sentence-transformers/multi-qa-mpnet-base-dot-v1") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3c946d620cf954d3ba3d55ee70b98a1a49eaca2364679cae80212f832f0587dc
- Size of remote file:
- 438 MB
- SHA256:
- 9e1e76b7a067f72e49c7f571cd8e811f7a1567bec49f17e5eaaea899e7bc2c9e
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