Instructions to use hf-tiny-model-private/tiny-random-LEDForSequenceClassification 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-LEDForSequenceClassification 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-LEDForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-LEDForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-tiny-model-private/tiny-random-LEDForSequenceClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d354a03bc8f0d41163e4187469a2f484204702a8410976ca012bd653991ece66
- Size of remote file:
- 1.23 MB
- SHA256:
- 9bbace9cadf0de8e98e67a26cfe7234f8b234b47ba8b56128a83812a3d2c346a
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