Sentence Similarity
sentence-transformers
PyTorch
ONNX
Safetensors
OpenVINO
Transformers
English
mpnet
fill-mask
feature-extraction
text-embeddings-inference
Instructions to use willmh0/all-mpnet-base-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use willmh0/all-mpnet-base-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("willmh0/all-mpnet-base-v2") 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 willmh0/all-mpnet-base-v2 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("willmh0/all-mpnet-base-v2") model = AutoModelForMaskedLM.from_pretrained("willmh0/all-mpnet-base-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e855ebfba767df1dfabd5e1995cd831bbf9e674d4ff13458270d1787d05680f7
- Size of remote file:
- 436 MB
- SHA256:
- 14d01256f5f3d2245b15b596173bca4367c9405fde5700dd7fb4e110708c1793
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