Instructions to use ethanrom/a2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ethanrom/a2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ethanrom/a2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ethanrom/a2") model = AutoModelForSequenceClassification.from_pretrained("ethanrom/a2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e06538cdc5274efa96bb36f82831c99e0da5fecde7343e58c324355f9977c813
- Size of remote file:
- 1.42 GB
- SHA256:
- 7d433ecc3af78eaa209e1af2f77ee07f8cf7c1dfb8f601c5bf0641621aa94ddd
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