Text Classification
Transformers
Safetensors
English
roberta
spam
sms
distilroberta
Eval Results (legacy)
text-embeddings-inference
Instructions to use SharpWoofer/distilroberta-sms-spam-detector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SharpWoofer/distilroberta-sms-spam-detector with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SharpWoofer/distilroberta-sms-spam-detector")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SharpWoofer/distilroberta-sms-spam-detector") model = AutoModelForSequenceClassification.from_pretrained("SharpWoofer/distilroberta-sms-spam-detector") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f995a27577ef3d650e38da46f3b3d0f85e93ce11b3c8c4f32197c590d378fe26
- Size of remote file:
- 5.78 kB
- SHA256:
- 51af621b99b5b52dc93b690ad3e77fce721b694231f2e0eb2e6fbf579111cd93
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