Sentence Similarity
sentence-transformers
OpenVINO
Transformers
English
mpnet
fill-mask
feature-extraction
nncf
fp16
text-embeddings-inference
Instructions to use AIFunOver/all-mpnet-base-v2-openvino-fp16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use AIFunOver/all-mpnet-base-v2-openvino-fp16 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("AIFunOver/all-mpnet-base-v2-openvino-fp16") 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 AIFunOver/all-mpnet-base-v2-openvino-fp16 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("AIFunOver/all-mpnet-base-v2-openvino-fp16") model = AutoModelForMaskedLM.from_pretrained("AIFunOver/all-mpnet-base-v2-openvino-fp16") - Notebooks
- Google Colab
- Kaggle