Instructions to use Crystalcareai/Colbertv2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Crystalcareai/Colbertv2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Crystalcareai/Colbertv2")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Crystalcareai/Colbertv2") model = AutoModel.from_pretrained("Crystalcareai/Colbertv2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7ef34c7b917222d737e575220562389090c815483b3308d12d0dd57eadaa5ffe
- Size of remote file:
- 438 MB
- SHA256:
- 741908cf34398b6de142cb5bca8b1d1dc8dcafca0b4b24649a6ac456abdab0c8
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