Instructions to use simonschoe/TransformationTransformer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use simonschoe/TransformationTransformer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="simonschoe/TransformationTransformer")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("simonschoe/TransformationTransformer") model = AutoModelForSequenceClassification.from_pretrained("simonschoe/TransformationTransformer") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a0b1506e8543bbd4cf57ca3c7f88c2e01b3e197f08de30c9fa382042a3b98c88
- Size of remote file:
- 499 MB
- SHA256:
- b5006e332e896dd92d6dcf81b9d6175ae4b9bca9f2add46a6f298322fd410eea
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