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
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Download upstream/README.md from CoderBak/editlens_roberta_modelkit: direct link, hf CLI and curl.
- Browser
- Download file 1.34 kB
-
https://huggingface.co/CoderBak/editlens_roberta_modelkit/resolve/main/upstream/README.md
- Command line
-
hf download hf://CoderBak/editlens_roberta_modelkit/upstream/README.md
-
curl -L -o README.md https://huggingface.co/CoderBak/editlens_roberta_modelkit/resolve/main/upstream/README.md
1.34 kB
metadata
extra_gated_fields:
First Name: text
Last Name: text
Institution: text
Country: country
How do you intend to use this model?: text
I agree to use this model for non-commercial use ONLY: checkbox
base_model: FacebookAI/roberta-large
library_name: peft
tags:
- base_model:FacebookAI/roberta-large
- ai_detection
datasets:
- pangram/editlens_iclr
language:
- en
license: cc-by-nc-sa-4.0
Model Card for editlens_roberta-large by Pangram
This model is a FacebookAI/roberta-large base model finetuned for AI detection according to the techniques described in the EditLens paper by Thai et al. (ICLR 2026)
Model Details
- Developed by: Pangram
- Language(s) (NLP): English
- License: CC BY-NC-SA 4.0
- Finetuned from model:
FacebookAI/roberta-large
Resources
- Repository: https://github.com/pangramlabs/EditLens
- Paper: https://arxiv.org/abs/2510.03154
Citation
BibTeX:
@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},
}