Instructions to use bpHigh/Cross-Encoder-LLamaIndex-Demo-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bpHigh/Cross-Encoder-LLamaIndex-Demo-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bpHigh/Cross-Encoder-LLamaIndex-Demo-v2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bpHigh/Cross-Encoder-LLamaIndex-Demo-v2") model = AutoModelForSequenceClassification.from_pretrained("bpHigh/Cross-Encoder-LLamaIndex-Demo-v2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9a37f5596b1809f3907b6a25cd306952b8543a8c74cc0f0160319aacd60a8a54
- Size of remote file:
- 134 MB
- SHA256:
- 7239d4a472c522d5ca0a6cc2df84f824b76750d3474f8c67bbe7a30af84b21d0
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