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:
- 74f19dc13f82b7ce61c878c9b0f1b6523957f4fec22009bc82ba10081aca6138
- Size of remote file:
- 46.5 MB
- SHA256:
- 6fc412efd3a94095f3c11743fbfee63996b15a6d5b72e051c0cc322e69ebeae1
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