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:
- c54eb484c7b1d4655a195d6fb8f3bf55a1910098cbe2ebf927a10eac0f8ed57d
- Size of remote file:
- 1.14 MB
- SHA256:
- 2ed9996d763f332f2a10eed859fde440622887446e3a95e12265eac64feca098
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