Instructions to use sdinger/dummy-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sdinger/dummy-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sdinger/dummy-model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sdinger/dummy-model") model = AutoModelForSequenceClassification.from_pretrained("sdinger/dummy-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a443f899f7594d0b46e9d7a5233e6c14c4db683d293e0b27176ba924c1b508fd
- Size of remote file:
- 438 MB
- SHA256:
- 69a27355255b7c2a98a3a7e804f218d08cbd40050649450fc31b5ed9ca1b32ce
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