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 tf_model.h5 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/tf_model.h5
- Command line
-
hf download hf://sentence-transformers/nli-mpnet-base-v2/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/sentence-transformers/nli-mpnet-base-v2/resolve/main/tf_model.h5
438 MB
- Xet hash:
- 0952b222e25f79d56952a67eb169b9d817f62d659c60ab74aa9abd95fdddd973
- Size of remote file:
- 438 MB
- SHA256:
- 573d04bc90e99482cf3c2e248df25e30da8a5993615d8acdd186da7effc810b7
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