Instructions to use sentence-transformers/all-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/all-mpnet-base-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sentence-transformers/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 sentence-transformers/all-mpnet-base-v2 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/all-mpnet-base-v2") model = AutoModelForMaskedLM.from_pretrained("sentence-transformers/all-mpnet-base-v2") - Inference
- Notebooks
- Google Colab
- Kaggle
onnx and openvino - huge files to download
few days ago, Bunch of files onnx and openvino files are included and they are getting downloaded as part of model, as the file sizes are huge, its taking more time, is there anyway we can control this behavior, i.e to download only the required files or specific file type.
Hello!
Indeed, the ONNX/OV files accompany the recent v3.2.0 release, which brings faster inference via various means: https://sbert.net/docs/sentence_transformer/usage/efficiency.html
Only old Sentence Transformers versions (2.2.2 or older I believe) download all files from the repository - all newer versions only download the required files.
My strong recommendation is to use a newer Sentence Transformers version. Note that all newer versions are designed to be backwards compatible with loading and computing embeddings.
- Tom Aarsen