Instructions to use shomez/blink-biencoder-description-encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shomez/blink-biencoder-description-encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="shomez/blink-biencoder-description-encoder")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("shomez/blink-biencoder-description-encoder") model = AutoModel.from_pretrained("shomez/blink-biencoder-description-encoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5ad77bc40f8324f550361f9f80117fb7e38e85f00d41fe0da9f8ef2b0c7efab1
- Size of remote file:
- 1.34 GB
- SHA256:
- 0c29ec1ce64bed74f7f8d55bde1eae2723dbb8ee6f9ac804ae16d1c70f4deae1
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