Instructions to use hf-internal-testing/tiny-random-LEDForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-LEDForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-internal-testing/tiny-random-LEDForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-LEDForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-internal-testing/tiny-random-LEDForSequenceClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2c5159b95e02e498c38d9c13c5893ed3f7f1e2f8494374b78f6295c50645ca71
- Size of remote file:
- 1.23 MB
- SHA256:
- bbbea834d936dc5d70875ecb58cacd40624f42cb5238e41fb27fb659e6a67e04
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