Instructions to use bondarchukb/bert-ads-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bondarchukb/bert-ads-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bondarchukb/bert-ads-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bondarchukb/bert-ads-classification") model = AutoModelForSequenceClassification.from_pretrained("bondarchukb/bert-ads-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 615415815b6b2c337744e4a76cf282f306811be6cf21b7a8df45a6a32710bd03
- Size of remote file:
- 433 MB
- SHA256:
- 0d7936e1dd19b5b46f19ce131d316ea18573d9673fa22ade137329ab608273f4
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