Instructions to use hf-tiny-model-private/tiny-random-CanineForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-CanineForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-tiny-model-private/tiny-random-CanineForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-CanineForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-tiny-model-private/tiny-random-CanineForSequenceClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0db292d554b31be52ceb48d48e6d533ceed3590446a55fb3e75587ff61dcba41
- Size of remote file:
- 4.49 MB
- SHA256:
- eff956374d0ab394116dad45d0950e569604bebf80fb97d8051f3779299cf1d2
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