Instructions to use hf-internal-testing/tiny-random-PerceiverForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-PerceiverForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-internal-testing/tiny-random-PerceiverForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-PerceiverForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-internal-testing/tiny-random-PerceiverForSequenceClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a951ff765c6fd33e4744be2a5b6ab75966e6a4cc0450eae13cd528088e1f3ef6
- Size of remote file:
- 1.25 MB
- SHA256:
- f778da57a1a1bf44217dff438af30b317c8bb45aba2706fd6d082168c3f4e047
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