Sentence Similarity
sentence-transformers
PyTorch
TensorFlow
ONNX
Safetensors
OpenVINO
Transformers
mpnet
feature-extraction
text-embeddings-inference
Instructions to use sentence-transformers/nli-mpnet-base-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sentence-transformers/nli-mpnet-base-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sentence-transformers/nli-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 sentence-transformers/nli-mpnet-base-v2 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/nli-mpnet-base-v2") model = AutoModel.from_pretrained("sentence-transformers/nli-mpnet-base-v2", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from sentence-transformers/nli-mpnet-base-v2: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/sentence-transformers/nli-mpnet-base-v2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://sentence-transformers/nli-mpnet-base-v2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/sentence-transformers/nli-mpnet-base-v2/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 3e61d6631ef30549840bd88ef7d806921a1dcfd0a1033f0e5382e26eb519f28f
- Size of remote file:
- 438 MB
- SHA256:
- c250de5d7533c0e5d1836293d7e30de4b55af8f13b57c2fc3284a4fad303a7d2
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