Sentence Similarity
sentence-transformers
Safetensors
Transformers
mpnet
feature-extraction
text-embeddings-inference
Instructions to use tomaarsen/mpnet-base-nli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use tomaarsen/mpnet-base-nli with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("tomaarsen/mpnet-base-nli") 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 tomaarsen/mpnet-base-nli with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("tomaarsen/mpnet-base-nli") model = AutoModelForMultimodalLM.from_pretrained("tomaarsen/mpnet-base-nli") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a77a6989588b3628a815f81df392eacd006c63acaaa8d815d3e327f7122f57f3
- Size of remote file:
- 438 MB
- SHA256:
- 6e4e42318b4e0ededfaeb76a4e544ecca228fed1c7c509c02981598f9e165267
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