Instructions to use nbroad/openai-detector-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nbroad/openai-detector-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nbroad/openai-detector-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nbroad/openai-detector-base") model = AutoModelForSequenceClassification.from_pretrained("nbroad/openai-detector-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
Adding `safetensors` variant of this model
This is an automated PR created with https://huggingface.co/spaces/safetensors/convert This new file is equivalent to `pytorch_model.bin` but safe in the sense that no arbitrary code can be put into it. These files also happen to load much faster than their pytorch counterpart: https://colab.research.google.com/github/huggingface/notebooks/blob/main/safetensors_doc/en/speed.ipynb The widgets on your model page will run using this model even if this is not merged making sure the file actually works. If you find any issues: please report here: https://huggingface.co/spaces/safetensors/convert/discussions Feel free to ignore this PR.35095f8
-
Update README.md
8dc9b0d
-
copy from https://huggingface.co/roberta-base-openai-detector (#1)
- copy from https://huggingface.co/roberta-base-openai-detector (ede964e012045f431725b88578f5268543f3a09f)46cb0b6
-
change architecture from maskedlm to seq classification
3855525 -
add label2id
64b8eb1 -
add tokenizers to reqs
516ecec -
add requirements.txt
97fdc8d -
add files
22a4cc9 -
initial commit
5e18d97