Instructions to use devloverumar/chatgpt-content-detector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use devloverumar/chatgpt-content-detector with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="devloverumar/chatgpt-content-detector")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("devloverumar/chatgpt-content-detector") model = AutoModelForSequenceClassification.from_pretrained("devloverumar/chatgpt-content-detector") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2172de529f6123b1fe18fe3e2a08b1f4f8dac204f94a3cacf094821cd391699b
- Size of remote file:
- 499 MB
- SHA256:
- 5ab1f449d4e931e60f51cdcded97297a3102cf801e57bc969775ebfbf0ee3d0d
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