Instructions to use lysandre/dum with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lysandre/dum with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="lysandre/dum")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("lysandre/dum") model = AutoModelForSequenceClassification.from_pretrained("lysandre/dum", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 323ac92c89700fca513f434c79283142fc08f1d6cf23edd5de4dabcfa11ec951
- Size of remote file:
- 438 MB
- SHA256:
- 2e8eaf14f2ca4dd16565a35b4d60b5632e57e360f3c95b828225a219343455f2
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