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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bd8ce8ae5bb99985c8dd215d4f92e51bb273d0073fe65fc22049bd4981e13bd5
- Size of remote file:
- 134 MB
- SHA256:
- e7b35678825723e10f1518547a81f83496b50595385ece5ce99078f81852711c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.