Instructions to use hossamamer12/OUTPUT_KD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hossamamer12/OUTPUT_KD with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hossamamer12/OUTPUT_KD")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hossamamer12/OUTPUT_KD") model = AutoModelForSequenceClassification.from_pretrained("hossamamer12/OUTPUT_KD") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 25e57b809c2a971dfb1ea07c1156b5de8a23d0b493cd5ae4ac92bc596d4e7318
- Size of remote file:
- 3.44 kB
- SHA256:
- b40773f9690708ff8c39832e967fad0d7bdd435c154a867a4f78f98ecdbf2f5b
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