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-multiset-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-multiset-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sentence-transformers/facebook-dpr-question_encoder-multiset-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-multiset-base with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/facebook-dpr-question_encoder-multiset-base") model = AutoModel.from_pretrained("sentence-transformers/facebook-dpr-question_encoder-multiset-base", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Commit History
-
Adding `safetensors` variant of this model
Verified signature7787a8f
-
Update Sentence Transformers metadata (#1)
Verified signature- Update Sentence Transformers metadata (cdc1efca8930ad12f144fc2297f7ec0c5e142851)c7cc719
-
Add TF weights
Joao Ganteb9b2a32
-
Update README.md
de8893f
-
Add new SentenceTransformer model.
nreimersb158eae -
initial commit
8e0aae8