Instructions to use PereLluis13/question_encoder_blink with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PereLluis13/question_encoder_blink with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="PereLluis13/question_encoder_blink", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("PereLluis13/question_encoder_blink", trust_remote_code=True) model = AutoModel.from_pretrained("PereLluis13/question_encoder_blink", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d0721affc6f9f92f8b1fe68dba1c5ab361de7ee78c142bc11c0eb978bf242616
- Size of remote file:
- 438 MB
- SHA256:
- af56f8baaa3a17a85d0007b4e0ee277cd45efdb6625d7c7e40bcf1fe22c06a85
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