Text Classification
Transformers
Safetensors
English
deberta-v2
digital-humanities
historical-text
ai-text-detection
deberta-v3
chronologic
Eval Results (legacy)
text-embeddings-inference
Instructions to use chronologic/chronologic-authenticity-deberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chronologic/chronologic-authenticity-deberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="chronologic/chronologic-authenticity-deberta")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("chronologic/chronologic-authenticity-deberta") model = AutoModelForSequenceClassification.from_pretrained("chronologic/chronologic-authenticity-deberta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
update with correct preprint bibtext
Browse files
README.md
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Preprint forthcoming.
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```bibtex
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@misc{
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```
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Preprint forthcoming.
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```bibtex
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@misc{underwood2026chronologicmeasuringlanguagemodels,
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title={Chronologic: Measuring Language Models' Ability to Represent the Past},
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author={Ted Underwood and Ziliang Qiu and Sarah Griebel and Laura K. Nelson and Edwin Roland and Wenyi Shang and Matthew Wilkens},
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year={2026},
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eprint={2609.23178},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2609.23178},
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}
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```
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