Instructions to use leffff/ruBert-base-response-quality with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use leffff/ruBert-base-response-quality with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="leffff/ruBert-base-response-quality")# Load model directly from transformers import AutoTokenizer, AutoModelForNextSentencePrediction tokenizer = AutoTokenizer.from_pretrained("leffff/ruBert-base-response-quality") model = AutoModelForNextSentencePrediction.from_pretrained("leffff/ruBert-base-response-quality", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d870767ab54806c503e2195e966900835b649919e2fd665d3e0ac84045f524fb
- Size of remote file:
- 713 MB
- SHA256:
- 1d0ea4d7c7c36f2fd23fdb73fa4ae9f1728c5c2f5a5179efee02f5069d3fa6bb
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