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:
- cf2634fdd7f70f311c70fad1de532921e6b047aa90bf6e90e2177eb01a978f35
- Size of remote file:
- 2.92 MB
- SHA256:
- 7f6a31139654e8ff5da9afcaac53b621aad55871c81abb79e9bd2ba5db061ec1
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