Text Classification
Transformers
Safetensors
English
modernbert
ai-text-detection
idea-provenance
text-embeddings-inference
Instructions to use rishanthrajendhran/IdeaLens-ModernBERT-L with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rishanthrajendhran/IdeaLens-ModernBERT-L with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rishanthrajendhran/IdeaLens-ModernBERT-L")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rishanthrajendhran/IdeaLens-ModernBERT-L") model = AutoModelForSequenceClassification.from_pretrained("rishanthrajendhran/IdeaLens-ModernBERT-L", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download thresholds.json from rishanthrajendhran/IdeaLens-ModernBERT-L: direct link, hf CLI and curl.
- Browser
- Download file 8.48 kB
-
https://huggingface.co/rishanthrajendhran/IdeaLens-ModernBERT-L/resolve/main/thresholds.json
- Command line
-
hf download hf://rishanthrajendhran/IdeaLens-ModernBERT-L/thresholds.json
-
curl -L -o thresholds.json https://huggingface.co/rishanthrajendhran/IdeaLens-ModernBERT-L/resolve/main/thresholds.json
8.48 kB
| { | |
| "model": "rishanthrajendhran/IdeaLens-ModernBERT-L", | |
| "input": "outline", | |
| "flag_rule": "flag the document as AI when P(human) < cut", | |
| "calibration": { | |
| "data": "WildOutlines, calibration split (human documents only)", | |
| "scores_from": "the training run's stored predictions on the calibration split", | |
| "n_humans": 80000, | |
| "n_per_format": { | |
| "Academic Writing": 10000, | |
| "Creative Writing": 10000, | |
| "Knowledge Article": 10000, | |
| "News Article": 10000, | |
| "Nonfiction Writing": 10000, | |
| "Personal About Page": 10000, | |
| "Personal Blog": 10000, | |
| "User Reviews": 10000 | |
| }, | |
| "n_per_topic": { | |
| "Art & Design": 3338, | |
| "Crime & Law": 3931, | |
| "Education & Jobs": 3974, | |
| "Entertainment": 4692, | |
| "Fashion & Beauty": 2999, | |
| "Finance & Business": 4855, | |
| "Food & Dining": 2696, | |
| "Games": 4257, | |
| "Hardware": 2253, | |
| "Health": 5069, | |
| "History": 2554, | |
| "Home & Hobbies": 4192, | |
| "Industrial": 2299, | |
| "Literature": 3571, | |
| "Politics": 3728, | |
| "Religion": 3617, | |
| "Science & Tech.": 4154, | |
| "Social Life": 3126, | |
| "Software": 2257, | |
| "Software Dev.": 2468, | |
| "Sports & Fitness": 4224, | |
| "Transportation": 2810, | |
| "Travel": 2936 | |
| } | |
| }, | |
| "method": { | |
| "global": "the target-FPR quantile of P(human) over all calibration humans", | |
| "per_group": "the group's own quantile, shrunk toward the global cut with weight n / (n + 2500)", | |
| "min_group_humans": 200, | |
| "estimability": "a cut at FPR q is given only when q * n >= 25 calibration humans", | |
| "missing_cut": "no cut for a group means none could be estimated; do not substitute the global cut" | |
| }, | |
| "fpr_targets": [ | |
| 0.001, | |
| 0.005, | |
| 0.01, | |
| 0.02, | |
| 0.05, | |
| 0.1, | |
| 0.2 | |
| ], | |
| "global": { | |
| "0.001": 0.005139531451277435, | |
| "0.005": 0.024237636476755142, | |
| "0.01": 0.05541279539465904, | |
| "0.02": 0.182997182905674, | |
| "0.05": 0.6486834049224853, | |
| "0.1": 0.8966140151023865, | |
| "0.2": 0.9773707389831543 | |
| }, | |
| "per_format": { | |
| "0.005": { | |
| "Academic Writing": 0.03953759199380875, | |
| "Creative Writing": 0.05771391174197197, | |
| "Knowledge Article": 0.019235945463180543, | |
| "News Article": 0.019345112003386022, | |
| "Nonfiction Writing": 0.016097568452358248, | |
| "Personal About Page": 0.04852480654418469, | |
| "Personal Blog": 0.03317504641413689, | |
| "User Reviews": 0.02776208963990212 | |
| }, | |
| "0.01": { | |
| "Academic Writing": 0.12872246548533442, | |
| "Creative Writing": 0.1665476575791836, | |
| "Knowledge Article": 0.03676119011640549, | |
| "News Article": 0.046824600577354436, | |
| "Nonfiction Writing": 0.029045478478074076, | |
| "Personal About Page": 0.12370659753680231, | |
| "Personal Blog": 0.11285716816782951, | |
| "User Reviews": 0.08841504457592966 | |
| }, | |
| "0.02": { | |
| "Academic Writing": 0.37497584372758863, | |
| "Creative Writing": 0.36964609414339067, | |
| "Knowledge Article": 0.08466875571012497, | |
| "News Article": 0.16145060497522357, | |
| "Nonfiction Writing": 0.07510314583778382, | |
| "Personal About Page": 0.31876455098390594, | |
| "Personal Blog": 0.3270333116650582, | |
| "User Reviews": 0.2942755115628243 | |
| }, | |
| "0.05": { | |
| "Academic Writing": 0.7944207191467285, | |
| "Creative Writing": 0.7330899429321289, | |
| "Knowledge Article": 0.3667260706424713, | |
| "News Article": 0.5772577738761903, | |
| "Nonfiction Writing": 0.4114002323150635, | |
| "Personal About Page": 0.6979178619384766, | |
| "Personal Blog": 0.7110324096679688, | |
| "User Reviews": 0.7446396446228027 | |
| }, | |
| "0.1": { | |
| "Academic Writing": 0.9471324324607849, | |
| "Creative Writing": 0.9085190653800964, | |
| "Knowledge Article": 0.8029681372642518, | |
| "News Article": 0.8600945830345154, | |
| "Nonfiction Writing": 0.8666762709617615, | |
| "Personal About Page": 0.8915582060813904, | |
| "Personal Blog": 0.9003047823905945, | |
| "User Reviews": 0.9365684390068054 | |
| }, | |
| "0.2": { | |
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| "Creative Writing": 0.9766665458679199, | |
| "Knowledge Article": 0.9701791286468506, | |
| "News Article": 0.9669312953948974, | |
| "Nonfiction Writing": 0.9753310680389404, | |
| "Personal About Page": 0.9737387657165527, | |
| "Personal Blog": 0.9732376575469971, | |
| "User Reviews": 0.9861243724822998 | |
| } | |
| }, | |
| "per_topic": { | |
| "0.005": { | |
| "Health": 0.023053244813589616 | |
| }, | |
| "0.01": { | |
| "Art & Design": 0.06763211139087584, | |
| "Crime & Law": 0.07036863836068664, | |
| "Education & Jobs": 0.05330997003780395, | |
| "Entertainment": 0.04512200002518127, | |
| "Fashion & Beauty": 0.088135457826969, | |
| "Finance & Business": 0.04148502488309716, | |
| "Food & Dining": 0.05131299507598449, | |
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| "History": 0.11171335421877035, | |
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| "Politics": 0.07981549680108586, | |
| "Religion": 0.05817645865597424, | |
| "Science & Tech.": 0.05320098759011352, | |
| "Social Life": 0.04563931021024928, | |
| "Sports & Fitness": 0.06428856616490729, | |
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| "Software Dev.": 0.18237710482808323, | |
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| "Science & Tech.": 0.6269058667327106, | |
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| "Software Dev.": 0.6317542892003405, | |
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