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")# pip install -U transformers accelerate # 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
Download training_args.bin from austinb/fraud_text_detection: direct link, hf CLI and curl.
- Browser
- Download file 4.54 kB
-
https://huggingface.co/austinb/fraud_text_detection/resolve/main/training_args.bin
- Command line
-
hf download hf://austinb/fraud_text_detection/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/austinb/fraud_text_detection/resolve/main/training_args.bin
4.54 kB
- Xet hash:
- 7e76da4dd3653970c2177af2ee92c4bc4235be8f4edefe8090ad2be3062af0dc
- Size of remote file:
- 4.54 kB
- SHA256:
- 9d37442022425771f921b79c52ce7ae1dab794824959c83e1894af54cacf97d8
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