Instructions to use bpHigh/Cross-Encoder-LLamaIndex-Demo-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bpHigh/Cross-Encoder-LLamaIndex-Demo-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bpHigh/Cross-Encoder-LLamaIndex-Demo-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bpHigh/Cross-Encoder-LLamaIndex-Demo-v1") model = AutoModelForSequenceClassification.from_pretrained("bpHigh/Cross-Encoder-LLamaIndex-Demo-v1") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#1
by SFconvertbot - opened
- model.safetensors +3 -0
model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:5055cfb560f011d91d72d6e1eed11b3778af6980258999416581c400ca147859
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size 133464836
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