Sentence Similarity
sentence-transformers
PyTorch
TensorFlow
ONNX
Safetensors
OpenVINO
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use sentence-transformers/facebook-dpr-question_encoder-single-nq-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sentence-transformers/facebook-dpr-question_encoder-single-nq-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sentence-transformers/facebook-dpr-question_encoder-single-nq-base") 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/facebook-dpr-question_encoder-single-nq-base with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/facebook-dpr-question_encoder-single-nq-base") model = AutoModel.from_pretrained("sentence-transformers/facebook-dpr-question_encoder-single-nq-base", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 57e6041db4b7da16725d4bfd215cb0b0fe95631c52895451701fff4cc5c9209d
- Size of remote file:
- 438 MB
- SHA256:
- 988f481177409b764570fd78a925b89aa00dcf2722d7cde9b5d7595d5a2c8c2b
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