Instructions to use WpythonW/RUbert-tiny_custom_cross-encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use WpythonW/RUbert-tiny_custom_cross-encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="WpythonW/RUbert-tiny_custom_cross-encoder")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("WpythonW/RUbert-tiny_custom_cross-encoder") model = AutoModelForSequenceClassification.from_pretrained("WpythonW/RUbert-tiny_custom_cross-encoder") - Notebooks
- Google Colab
- Kaggle
File size: 134 Bytes
3d7ac4a | 1 2 3 4 | version https://git-lfs.github.com/spec/v1
oid sha256:589ec9d6bb08b86303c6b9a9732ae89455693ad566f8a30b1dc842e2a97d5ee2
size 116782884
|