Sentence Similarity
sentence-transformers
PyTorch
TensorFlow
ONNX
Safetensors
OpenVINO
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use sentence-transformers/facebook-dpr-ctx_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-ctx_encoder-multiset-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sentence-transformers/facebook-dpr-ctx_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-ctx_encoder-multiset-base with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/facebook-dpr-ctx_encoder-multiset-base") model = AutoModel.from_pretrained("sentence-transformers/facebook-dpr-ctx_encoder-multiset-base") - Notebooks
- Google Colab
- Kaggle
Remove deprecated (SEB) evaluation results section
Browse files
README.md
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## Evaluation Results
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For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/facebook-dpr-ctx_encoder-multiset-base)
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## Full Model Architecture
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```
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SentenceTransformer(
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## Full Model Architecture
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```
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SentenceTransformer(
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