Text Classification
Transformers
PyTorch
ONNX
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use austinb/fraud_text_detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use austinb/fraud_text_detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="austinb/fraud_text_detection")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("austinb/fraud_text_detection") model = AutoModelForSequenceClassification.from_pretrained("austinb/fraud_text_detection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 12cfb76845666ec1803e837163524d60f42b2bcb554f5714f1899c04528f10b7
- Size of remote file:
- 268 MB
- SHA256:
- c2e787df9a85e712272a92fc4ec6235a368227a5d5cf0133042d2d569d56baa9
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