Instructions to use Aktsvigun/deberta_cola with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Aktsvigun/deberta_cola with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Aktsvigun/deberta_cola")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Aktsvigun/deberta_cola") model = AutoModelForSequenceClassification.from_pretrained("Aktsvigun/deberta_cola", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bfb4c9fd8d94969f7a06b039d2c0e9880e2fdbca5de3dad0c54bbf99db12b730
- Size of remote file:
- 1.74 GB
- SHA256:
- a0dfa0a726be782c53428e53d0a7ecb5f1e244ceaaf43e321f9bcf112ab19aa5
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