Instructions to use jmmr-8282/email with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jmmr-8282/email with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jmmr-8282/email")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jmmr-8282/email") model = AutoModelForSequenceClassification.from_pretrained("jmmr-8282/email") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5fee55e43bb53f801cc2c51a5cfdbd74161500eea25c66c7f8cb7bdf900bd31b
- Size of remote file:
- 118 MB
- SHA256:
- 84ecf8f3a21cd9c5465c5646b40ff679305ca73e42a836475588610795c8282d
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