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:
- 0f293f81bd982a02f2f0142f69d450f24b1d985093353a948d33c0e249112ecc
- Size of remote file:
- 67.6 MB
- SHA256:
- 0372939b26995df2bca150ea9e058456c5cc8f635dba0216a8340289a1f70731
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