Instructions to use plexatic/ultrawhack-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use plexatic/ultrawhack-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="plexatic/ultrawhack-model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("plexatic/ultrawhack-model") model = AutoModelForSequenceClassification.from_pretrained("plexatic/ultrawhack-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f35a6b81a1e529f18a46202460edadcd15c81060bbd06c1a200581d936b23c70
- Size of remote file:
- 2.24 GB
- SHA256:
- d4dc430df2f26c3fa1670010b8448be0dda7daf39430f2148d1f24a9f5386fc9
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