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
- Xet hash:
- f816f8362fde578bda2054c6e42baf71e4bf483c60c7e3bdad3f4378766ffc34
- Size of remote file:
- 134 MB
- SHA256:
- 4018e2119a67c9439add718209927dec4902aaba68ba5dec80fed32414550ff1
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