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 pytorch_model.bin from austinb/fraud_text_detection: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/austinb/fraud_text_detection/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://austinb/fraud_text_detection/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/austinb/fraud_text_detection/resolve/main/pytorch_model.bin
268 MB
- Xet hash:
- 865112e94dddcfe5d02a3415f8000f10fe544e115156b83134a1fb6aa942d597
- Size of remote file:
- 268 MB
- SHA256:
- b42b89bdf2bd2a18c3af828f3197355e6d118b8ceaf1e1f96a95706f0fc36785
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