Instructions to use Mahmoud3899/simple with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mahmoud3899/simple with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Mahmoud3899/simple")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Mahmoud3899/simple") model = AutoModelForSequenceClassification.from_pretrained("Mahmoud3899/simple", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a85f777badd6194bff7a0f00f138bd64e667834651a8b9b79606c97cd5f2ea2c
- Size of remote file:
- 438 MB
- SHA256:
- 23cd693b015af47dbad9eaa08f1ea7f217e3f45c2686b620491cc0d3c69da3f3
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