Instructions to use hf-tiny-model-private/tiny-random-ReformerForSequenceClassification 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-ReformerForSequenceClassification 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-ReformerForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-ReformerForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-tiny-model-private/tiny-random-ReformerForSequenceClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ba140ea39684f2cb158f51a1d00aad48401835fc6dbfca1d48bafb100a724ccd
- Size of remote file:
- 358 kB
- SHA256:
- 6a62feebee395eed2f93c7977580410d1f4af2bceb46f80030293ab3a6aa3ba8
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