Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use vaibhavad/facebook-dpr-question_encoder-single-topiocqa-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use vaibhavad/facebook-dpr-question_encoder-single-topiocqa-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("vaibhavad/facebook-dpr-question_encoder-single-topiocqa-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 vaibhavad/facebook-dpr-question_encoder-single-topiocqa-base with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("vaibhavad/facebook-dpr-question_encoder-single-topiocqa-base") model = AutoModel.from_pretrained("vaibhavad/facebook-dpr-question_encoder-single-topiocqa-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 37d8c8ca15e946185fb925a13e2a003c2541f6a080fe2d81266e9f89b987e900
- Size of remote file:
- 438 MB
- SHA256:
- 4154c6d4ab9c1d50a7d74fff88178b7662d647fcdace79621b7ff25e51efdb52
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