Instructions to use kumar9/super-cool-model5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kumar9/super-cool-model5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kumar9/super-cool-model5")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kumar9/super-cool-model5") model = AutoModelForSequenceClassification.from_pretrained("kumar9/super-cool-model5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3036ad94333612d22728ec2525cec415ae1c57d0edb7d14b1bc19fc992143b06
- Size of remote file:
- 1.59 kB
- SHA256:
- 008deedca1a067963748b154a2c795187ec79a4bd6b31af6b5e951c3cfe5b708
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