Instructions to use minimario/rankers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use minimario/rankers with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="minimario/rankers")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("minimario/rankers") model = AutoModelForSequenceClassification.from_pretrained("minimario/rankers", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2f358b0e7317d2dcce541553f77927d52e4a082711bee7bd2bccf41643120b46
- Size of remote file:
- 499 MB
- SHA256:
- 510c3eb3cc160b18106c0f6a20224a8b5d85cc214f1cb5a4fd7a081f66b3ec84
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