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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 59c7a460ba84e5a7896810fab34c9ccfd85b1d60541b7f746acbd07c8f160b2a
- Size of remote file:
- 3.45 kB
- SHA256:
- b95da818902114578a4d0d1dac4183a0d9039f6150c797cc95d1eac55435fa01
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