Text Classification
Transformers
Safetensors
PyTorch
Spanish
bert
spam-detection
sms
beto
spanish
Eval Results (legacy)
text-embeddings-inference
Instructions to use JavicR22/SpamVision with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JavicR22/SpamVision with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JavicR22/SpamVision")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("JavicR22/SpamVision") model = AutoModelForSequenceClassification.from_pretrained("JavicR22/SpamVision") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 42d0b348c7c137f1d09d0c98e7e61f95b507e3e4c682bcd6eee55c24b7df0886
- Size of remote file:
- 879 MB
- SHA256:
- c6c35b67ea744b3eeebe5cae8651bb6a6ce542816d5496673744055529e32771
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