Instructions to use Den4ikAI/rubert_base_response_ranker with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Den4ikAI/rubert_base_response_ranker with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Den4ikAI/rubert_base_response_ranker")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Den4ikAI/rubert_base_response_ranker") model = AutoModelForSequenceClassification.from_pretrained("Den4ikAI/rubert_base_response_ranker", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Den4ikAI/rubert_base_response_ranker: direct link, hf CLI and curl.
- Browser
- Download file 712 MB
-
https://huggingface.co/Den4ikAI/rubert_base_response_ranker/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Den4ikAI/rubert_base_response_ranker/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Den4ikAI/rubert_base_response_ranker/resolve/main/pytorch_model.bin
712 MB
- Xet hash:
- 537ca5f4c656a1bf48535e1bd6d55dcb2aa09ff6b65b55203510516c8d73919c
- Size of remote file:
- 712 MB
- SHA256:
- 2dc3d8babc824fd3d4a269ba87136ae49a15e43e1835d176e4d8932e9aab29d1
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