Sentence Similarity
sentence-transformers
PyTorch
Transformers
English
mpnet
feature-extraction
text-embeddings-inference
Instructions to use jamescalam/mpnet-nli-sts with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use jamescalam/mpnet-nli-sts with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("jamescalam/mpnet-nli-sts") 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 jamescalam/mpnet-nli-sts with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("jamescalam/mpnet-nli-sts") model = AutoModel.from_pretrained("jamescalam/mpnet-nli-sts", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from jamescalam/mpnet-nli-sts: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/jamescalam/mpnet-nli-sts/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://jamescalam/mpnet-nli-sts/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jamescalam/mpnet-nli-sts/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- c24e06ef3763c86a6f545e4939ef287f8de28d0dfbe299406712684eded0505d
- Size of remote file:
- 438 MB
- SHA256:
- 5f8ebfbd72d4f29040e1204d03c1a8f27a109ded4a8760dfe03bc17b03153d50
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