Instructions to use mholi/dummy_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mholi/dummy_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mholi/dummy_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mholi/dummy_model") model = AutoModelForSequenceClassification.from_pretrained("mholi/dummy_model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fa1968571516345235330d097ecc822ffd61f9091af757dfdef7e545c8288658
- Size of remote file:
- 433 MB
- SHA256:
- 691a270f079668900839cf57a9d7d3a170c8d47607afae2b89cf9c588905b9cc
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