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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("devloverumar/chatgpt-content-detector") model = AutoModelForSequenceClassification.from_pretrained("devloverumar/chatgpt-content-detector", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from devloverumar/chatgpt-content-detector: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/devloverumar/chatgpt-content-detector/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://devloverumar/chatgpt-content-detector/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/devloverumar/chatgpt-content-detector/resolve/main/pytorch_model.bin
499 MB
- Xet hash:
- 2172de529f6123b1fe18fe3e2a08b1f4f8dac204f94a3cacf094821cd391699b
- Size of remote file:
- 499 MB
- SHA256:
- 5ab1f449d4e931e60f51cdcded97297a3102cf801e57bc969775ebfbf0ee3d0d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.