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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0269c1a9c4a84390039044825a426d0fb338eeee6896ac107628520d30c4fb3b
- Size of remote file:
- 2.94 MB
- SHA256:
- 26d221ac9cd85e8b878f126123d746222a718b515774394505e9c7aaa6df0398
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