Sentence Similarity
sentence-transformers
PyTorch
Transformers
mpnet
feature-extraction
text-embeddings-inference
Instructions to use Brendan/refpydst-5p-icdst-split-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Brendan/refpydst-5p-icdst-split-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Brendan/refpydst-5p-icdst-split-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 Brendan/refpydst-5p-icdst-split-v2 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Brendan/refpydst-5p-icdst-split-v2") model = AutoModel.from_pretrained("Brendan/refpydst-5p-icdst-split-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ab39c08ffdcaf816922c0a16d8a4f76a887a802dbd29a6c19e9e3fbe498f74b4
- Size of remote file:
- 438 MB
- SHA256:
- b1607079cb498e9b1955461c70536b21fb592cbf18410db7ab0af74146c4f80e
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