Text Classification
Transformers
ONNX
Safetensors
English
roberta
editlens
ai-detection
quantization
local-inference
text-embeddings-inference
Instructions to use CoderBak/editlens_roberta_modelkit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CoderBak/editlens_roberta_modelkit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="CoderBak/editlens_roberta_modelkit")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("CoderBak/editlens_roberta_modelkit") model = AutoModelForSequenceClassification.from_pretrained("CoderBak/editlens_roberta_modelkit", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download CITATION.bib from CoderBak/editlens_roberta_modelkit: direct link, hf CLI and curl.
- Browser
- Download file 316 Bytes
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https://huggingface.co/CoderBak/editlens_roberta_modelkit/resolve/main/CITATION.bib
- Command line
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hf download hf://CoderBak/editlens_roberta_modelkit/CITATION.bib
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curl -L -o CITATION.bib https://huggingface.co/CoderBak/editlens_roberta_modelkit/resolve/main/CITATION.bib
316 Bytes
| @misc{thai2025editlensquantifyingextentai, | |
| title={EditLens: Quantifying the Extent of AI Editing in Text}, | |
| author={Katherine Thai and Bradley Emi and Elyas Masrour and Mohit Iyyer}, | |
| year={2025}, | |
| eprint={2510.03154}, | |
| archivePrefix={arXiv}, | |
| primaryClass={cs.CL}, | |
| url={https://arxiv.org/abs/2510.03154} | |
| } | |