Text Classification
Transformers
Safetensors
bert
candidate-row-reranker
l4
repository-library
repository_library_search_stack
research-library
retrieval
text-embeddings-inference
Instructions to use PeytonT/candidate-row-reranker with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PeytonT/candidate-row-reranker with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="PeytonT/candidate-row-reranker")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("PeytonT/candidate-row-reranker") model = AutoModelForSequenceClassification.from_pretrained("PeytonT/candidate-row-reranker", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 366c3f7c38892026747099913f6fac7dd2edb7aa8621d8a0e1233545a5439102
- Size of remote file:
- 5.78 kB
- SHA256:
- aa2a9e4fd3d35187c99650f5ef8ae5ca3f33bf228d6bc994744e1ec77d3baa75
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