Instructions to use dima806/email-spam-detection-roberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dima806/email-spam-detection-roberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dima806/email-spam-detection-roberta")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dima806/email-spam-detection-roberta") model = AutoModelForSequenceClassification.from_pretrained("dima806/email-spam-detection-roberta") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 05cf7938ef7fe7702c9bf200da911f5c3930266c53ed316fc959ade6e22a570f
- Size of remote file:
- 499 MB
- SHA256:
- 769a427df450f973175d5de9b1cde8c6a330a7307034038fbac1199ceec1d7bb
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