Instructions to use stanford-nlpxed/confusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use stanford-nlpxed/confusion with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="stanford-nlpxed/confusion")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("stanford-nlpxed/confusion") model = AutoModelForSequenceClassification.from_pretrained("stanford-nlpxed/confusion") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4d7bd5784576061cd4097aa1bde6f3576ab9c2cee6f77c25bf3394e44d660cc3
- Size of remote file:
- 1.42 GB
- SHA256:
- 96d68519d48bf57c797295499d58f6e65a2e61172344ff4292fbba8896b59dcb
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