Text Classification
Transformers
Safetensors
English
distilbert
ai-text-detection
academic-integrity
paperguard
text-embeddings-inference
Instructions to use vediumsameer/paperguard-ai-detector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vediumsameer/paperguard-ai-detector with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="vediumsameer/paperguard-ai-detector")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("vediumsameer/paperguard-ai-detector") model = AutoModelForSequenceClassification.from_pretrained("vediumsameer/paperguard-ai-detector", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e57b267d3c7fd90963331d7b49fd5690df3a9a8255ab90fe65b78935622f91f0
- Size of remote file:
- 4.73 kB
- SHA256:
- a400e98fbbc4c2a6d96b9382a7a3a027c2b3fda163cc31e6fb968b832a6e6067
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