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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7db86dd6187303217b78d6db645e91d65e7374ab51a3b5ec90ea990480b4764c
- Size of remote file:
- 1.25 MB
- SHA256:
- 089c2cb5ae11631c69402d61b0c4187781312b7745b6de3ac2ef3ee47ca9427b
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