Instructions to use hf-tiny-model-private/tiny-random-NezhaForSequenceClassification 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-NezhaForSequenceClassification 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-NezhaForSequenceClassification")# Load model directly from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("hf-tiny-model-private/tiny-random-NezhaForSequenceClassification", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cef30f95a6275571243212ce5cf2fc3216d9d106417e0b4f6418d35d62216545
- Size of remote file:
- 2.94 MB
- SHA256:
- 68fd5bdf685d60ff19c06e75454ae65c77db316e5ffa4ae1d954e80ec701e961
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