Instructions to use Larxmind/fraud-detection-model-fp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Larxmind/fraud-detection-model-fp with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Larxmind/fraud-detection-model-fp")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Larxmind/fraud-detection-model-fp") model = AutoModelForSequenceClassification.from_pretrained("Larxmind/fraud-detection-model-fp") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 56c19bb2e93a2ca45d0d3f2f6c42d5c87cf301413af1a4cad9f714057ad49cf3
- Size of remote file:
- 5.27 kB
- SHA256:
- 1314d5bef42025c3e01b757ccdb464d87041a6c98f190daf9d7cdfa11ce2a135
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