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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f9b6e82b7bdc4f5c14cde187a3aa7b0421058bf8825189730ef45bf7d8a2405f
- Size of remote file:
- 1.25 MB
- SHA256:
- cef03010b4bbdaa7042eaceaeb83a47b492472244dbd482730241ae639eb3580
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