Feature Extraction
Transformers
Safetensors
English
bert
retrieval
constbert
colbert
multi-vector
embedding
custom_code
text-embeddings-inference
Instructions to use pinecone/ConstBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pinecone/ConstBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="pinecone/ConstBERT", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("pinecone/ConstBERT", trust_remote_code=True) model = AutoModel.from_pretrained("pinecone/ConstBERT", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- eb1b703a15bbc0d11845be8c8b6968c7b9f41284ec4552a6430c9737f5811455
- Size of remote file:
- 438 MB
- SHA256:
- e63f29e724efa1b9461cdc11af501c0c0fc09ac8c2c334ebf6e5dc4e45e422be
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