Instructions to use aekupor/adding_on with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aekupor/adding_on with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="aekupor/adding_on")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("aekupor/adding_on") model = AutoModelForSequenceClassification.from_pretrained("aekupor/adding_on") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b38e484a1bc7540ff056f6e7f2f9c7a7f32fce6c2b3d7e38e6d6681b19dc9683
- Size of remote file:
- 499 MB
- SHA256:
- 122eaaa022ab475b13b08aadb676588bb348eda12d30451d21596ef60041de9f
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