Feature Extraction
Transformers
Safetensors
sentence-transformers
PyTorch
English
bert_retriever
bert
dense-retrieval
semantic-search
information-retrieval
faiss
retrieval
semantic
embeddings
custom_code
Instructions to use Innovatewithapple/bert-dense-retriever with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Innovatewithapple/bert-dense-retriever with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Innovatewithapple/bert-dense-retriever", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Innovatewithapple/bert-dense-retriever", trust_remote_code=True, device_map="auto") - sentence-transformers
How to use Innovatewithapple/bert-dense-retriever with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Innovatewithapple/bert-dense-retriever", trust_remote_code=True) sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
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