Instructions to use hf-tiny-model-private/tiny-random-PerceiverForSequenceClassification 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-PerceiverForSequenceClassification 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-PerceiverForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-PerceiverForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-tiny-model-private/tiny-random-PerceiverForSequenceClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bbe3ee2d76730d25a99ca6c2d6d04844284d1e4baccd5da9e66cb6d25ee8522d
- Size of remote file:
- 1.15 MB
- SHA256:
- c32b8960a7fddcfce351845cfab5d909adfcd04a7322e6158a2b107dc2761f96
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