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") - Notebooks
- Google Colab
- Kaggle
Adding ONNX model files
Browse files- model.onnx +3 -0
- model_quantized.onnx +3 -0
model.onnx
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version https://git-lfs.github.com/spec/v1
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oid sha256:c2e787df9a85e712272a92fc4ec6235a368227a5d5cf0133042d2d569d56baa9
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size 267955712
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model_quantized.onnx
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version https://git-lfs.github.com/spec/v1
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oid sha256:0372939b26995df2bca150ea9e058456c5cc8f635dba0216a8340289a1f70731
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size 67581198
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