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:
- 0cb7c2af194e92b188bb9e767a56773a1c991194c5209578fd5daddfb4b29508
- Size of remote file:
- 3.58 kB
- SHA256:
- 308dab436ff4337c1b2154413d66d115c69b88e66bc51450d3e5a93ac18a6f3e
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