Instructions to use bpHigh/Cross-Encoder-LLamaIndex-Demo-v4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bpHigh/Cross-Encoder-LLamaIndex-Demo-v4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bpHigh/Cross-Encoder-LLamaIndex-Demo-v4")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bpHigh/Cross-Encoder-LLamaIndex-Demo-v4") model = AutoModelForSequenceClassification.from_pretrained("bpHigh/Cross-Encoder-LLamaIndex-Demo-v4") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
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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 sha256:64cbb52098e4b627f612aa27d2808b2962093e3a04f3c9f39bf951bb492f10f3
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size 133464836
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