Sentence Similarity
sentence-transformers
PyTorch
Transformers
mpnet
feature-extraction
text-embeddings-inference
Instructions to use uclanlp/keyphrase-mpnet-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use uclanlp/keyphrase-mpnet-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("uclanlp/keyphrase-mpnet-v1") 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 uclanlp/keyphrase-mpnet-v1 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("uclanlp/keyphrase-mpnet-v1") model = AutoModel.from_pretrained("uclanlp/keyphrase-mpnet-v1") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b05dc72c219c6015e677241223c39696d368b5bccaea9a0912747ab0560c88cd
- Size of remote file:
- 438 MB
- SHA256:
- 2f35251bba89754cb5c1047beaea60c1cdfade4dfba17b14de92e732cea39f21
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.