Text Classification
Transformers
PyTorch
TensorFlow
JAX
Safetensors
English
roberta
exbert
text-embeddings-inference
Instructions to use openai-community/roberta-base-openai-detector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openai-community/roberta-base-openai-detector with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="openai-community/roberta-base-openai-detector")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("openai-community/roberta-base-openai-detector") model = AutoModelForSequenceClassification.from_pretrained("openai-community/roberta-base-openai-detector") - Inference
- Notebooks
- Google Colab
- Kaggle
what does each LABEL represents?
#15
by WYHu - opened
In the huggingface portal, What does LABEL_0 and LABEL_1 represents?
In the code, what does Real and Fake represents respectively? Does "Real" presents the text was generated by a GPT-2 model and "Fake" presents the text was not generated by a GPT-2 model ?
you can see the id2label mapping in https://huggingface.co/roberta-base-openai-detector/blob/main/config.json (from #4)