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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d53fd334c9c954cd08ce2d97583149a28c3e3505a0e4ab7835d9ad961d28746b
- Size of remote file:
- 1.15 MB
- SHA256:
- 9472557440dc874a3ddcf6314c8d63d56ae1e8335de53861906bd1517847f7cf
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