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
- Xet hash:
- 42c66fb9cba9eb6b71698b076239239e52013dfbb2150d4c88d5c755e30729b9
- Size of remote file:
- 499 MB
- SHA256:
- da61f309153df496af7a5aa3192c3fcd8d37408cbbb3b7a4ffbfb70659841a4b
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