Instructions to use Winst/cross-encoder-ru with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Winst/cross-encoder-ru with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Winst/cross-encoder-ru")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Winst/cross-encoder-ru") model = AutoModel.from_pretrained("Winst/cross-encoder-ru", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Update model metadata to set pipeline tag to the new `text-ranking`
Verified signatureHello! ## Pull Request overview * Update metadata to set pipeline tag to the new `text-ranking` ## Changes This is an automated pull request to update the metadata of the model card. We recently introduced the [`text-ranking`](https://huggingface.co/models?pipeline_tag=text-ranking) pipeline tag for models that are used for ranking tasks, and we have a suspicion that this model is one of them. Feel free to respond if you have questions or concerns. - Tom Aarsen6174b2d
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Rename model.pt to model.ckpt
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