Sentence Similarity
sentence-transformers
PyTorch
TensorFlow
JAX
ONNX
Safetensors
OpenVINO
Transformers
roberta
feature-extraction
text-embeddings-inference
Instructions to use sentence-transformers/paraphrase-distilroberta-base-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sentence-transformers/paraphrase-distilroberta-base-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sentence-transformers/paraphrase-distilroberta-base-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 sentence-transformers/paraphrase-distilroberta-base-v1 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/paraphrase-distilroberta-base-v1") model = AutoModel.from_pretrained("sentence-transformers/paraphrase-distilroberta-base-v1", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2e09c71965076572847597757b2d0a2bfbbf7e208753249ab5085f705af87ca2
- Size of remote file:
- 328 MB
- SHA256:
- 6bebbcc7ead5534be3f4dee3e5a1883e2a7e185f1c610256eaf5d804332fc4f9
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