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:
- 0b9fcfb016980c137d18f3e0624eea8a372379b60ce5acd2cabbe137c06f440c
- Size of remote file:
- 5.84 kB
- SHA256:
- 1718134e60da56a90d4257755566fca2f874e7861e3964a19427f964b5eefbbe
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