Instructions to use hf-internal-testing/tiny-random-NezhaForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-NezhaForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-internal-testing/tiny-random-NezhaForSequenceClassification")# Load model directly from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("hf-internal-testing/tiny-random-NezhaForSequenceClassification", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2b6f7bfbf31c7238dc6ece5b7ad591e87af35dbf3ab058f19fb37faff10af15f
- Size of remote file:
- 2.92 MB
- SHA256:
- 608ebca7d4da46981b1ab6248869dca2a8aab87a2eea06243de7a8f005bb8db4
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