Instructions to use seanmor5/tiny-random-mistral-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use seanmor5/tiny-random-mistral-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="seanmor5/tiny-random-mistral-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("seanmor5/tiny-random-mistral-classification") model = AutoModelForSequenceClassification.from_pretrained("seanmor5/tiny-random-mistral-classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d40bc22b1709b88ac78d1f9216346326caa3e166afc61b317f77b4cce9e387ed
- Size of remote file:
- 4.16 MB
- SHA256:
- 04d4fa9b33dffe8676082275383f454270f24b7b53491ef4db924d2047591caa
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