Sentence Similarity
sentence-transformers
PyTorch
Transformers
roberta
feature-extraction
text-embeddings-inference
Instructions to use embedding-data/distilroberta-base-sentence-transformer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use embedding-data/distilroberta-base-sentence-transformer with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("embedding-data/distilroberta-base-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/distilroberta-base-sentence-transformer with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("embedding-data/distilroberta-base-sentence-transformer") model = AutoModel.from_pretrained("embedding-data/distilroberta-base-sentence-transformer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3d334ec09f2ce04997ac7cd18d3f3635b1b62eaaecbf8b73c696ac49d4fb3150
- Size of remote file:
- 329 MB
- SHA256:
- 13eb130de088fa9845b352ff94ffa3e2e28bdb4bec7fe752441d05fd71e21e02
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