Text Classification
Transformers
PyTorch
ONNX
Safetensors
English
bert
anti-spam
spam
text-embeddings-inference
Instructions to use Titeiiko/OTIS-Official-Spam-Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Titeiiko/OTIS-Official-Spam-Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Titeiiko/OTIS-Official-Spam-Model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Titeiiko/OTIS-Official-Spam-Model") model = AutoModelForSequenceClassification.from_pretrained("Titeiiko/OTIS-Official-Spam-Model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bcb9dbb0b54bbf64492fb2412444bb9c353b1c89188db6880b77b5270929762c
- Size of remote file:
- 17.6 MB
- SHA256:
- 834c4b5dbc4a8aa3cbe5b56e380932c0e152e43ceeaf219a36ae98d66fbbfacb
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