Instructions to use apanc/russian-sensitive-topics with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use apanc/russian-sensitive-topics with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="apanc/russian-sensitive-topics")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("apanc/russian-sensitive-topics") model = AutoModelForSequenceClassification.from_pretrained("apanc/russian-sensitive-topics") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1a9d8b6194fbb2b1850e281d1c7172dc12966bf1eaf99697e93b2cb50c76596b
- Size of remote file:
- 713 MB
- SHA256:
- 328b9793695aa851976f9745f9a0fea3bd2a0e469bd8dc5c29bcd7cf6ab11344
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.