Instructions to use WpythonW/RUbert-tiny_custom_CE-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use WpythonW/RUbert-tiny_custom_CE-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="WpythonW/RUbert-tiny_custom_CE-v2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("WpythonW/RUbert-tiny_custom_CE-v2") model = AutoModelForSequenceClassification.from_pretrained("WpythonW/RUbert-tiny_custom_CE-v2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 21c6ed46cc89bc37dcb2851144df526c2fb03a4c61c94312702361ec6d21805f
- Size of remote file:
- 117 MB
- SHA256:
- ec9d8d4a50ec5bd51be131175a7abc193219fc1d8c56b3c3201ef5dd99e1e371
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