Instructions to use plexatic/big-whack-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use plexatic/big-whack-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="plexatic/big-whack-model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("plexatic/big-whack-model") model = AutoModelForSequenceClassification.from_pretrained("plexatic/big-whack-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 00568a4e9c2b09a0f1202a5e4ea17be8840b4af2769696884a6496d8fbcb1baf
- Size of remote file:
- 1.74 GB
- SHA256:
- c54c15cca47f635898ba60c2bce6a29d9d792fbbd6370a12d48a7e24d441f92a
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