Sentence Similarity
sentence-transformers
PyTorch
Transformers
deberta-v2
feature-extraction
text-embeddings-inference
Instructions to use embedding-data/deberta-sentence-transformer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use embedding-data/deberta-sentence-transformer with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("embedding-data/deberta-sentence-transformer") 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 embedding-data/deberta-sentence-transformer with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("embedding-data/deberta-sentence-transformer") model = AutoModel.from_pretrained("embedding-data/deberta-sentence-transformer") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6cf87dc616be0c45b9f0eff6b94b5df2a35635738b9126888333b034527ea661
- Size of remote file:
- 8.66 MB
- SHA256:
- 03f216aca2eec6942cfd406938ebd5656f0035f372c43b284ddb09ace46c6444
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