Text Classification
Transformers
TensorFlow
distilbert
generated_from_keras_callback
text-embeddings-inference
Instructions to use Wintersmith/LLM_generated_text_detector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Wintersmith/LLM_generated_text_detector with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Wintersmith/LLM_generated_text_detector")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Wintersmith/LLM_generated_text_detector") model = AutoModelForSequenceClassification.from_pretrained("Wintersmith/LLM_generated_text_detector") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 22c835794062862a1aa9a4fe8cef4022183826e3a1c0ae79d25f62f2a2922979
- Size of remote file:
- 268 MB
- SHA256:
- 64be676b3ad0e43006b5078681ff0d153cdf9f93d9ac4a6ee64dbb0a6c0fe8c9
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