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")# pip install -U transformers accelerate # 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
Download tokenizer.json from PeytonT/candidate-row-reranker: direct link, hf CLI and curl.
- Browser
- Download file 712 kB
-
https://huggingface.co/PeytonT/candidate-row-reranker/resolve/main/tokenizer.json
- Command line
-
hf download hf://PeytonT/candidate-row-reranker/tokenizer.json
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curl -L -o tokenizer.json https://huggingface.co/PeytonT/candidate-row-reranker/resolve/main/tokenizer.json
712 kB
File too large to display, you can check the raw version instead.