Instructions to use plexatic/whack-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use plexatic/whack-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="plexatic/whack-model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("plexatic/whack-model") model = AutoModelForSequenceClassification.from_pretrained("plexatic/whack-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7268390d32d931f500fedbbefd1032244fb1a075b0d9ee4d4b0f2f859a0ecdba
- Size of remote file:
- 738 MB
- SHA256:
- 434491d91fb0585315814fea0bdf7cc2f693123279e8d2f1d34c4688c58fcb0b
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