Text Classification
Transformers
Safetensors
English
modernbert
ai-text-detection
idea-provenance
text-embeddings-inference
Instructions to use rishanthrajendhran/IdeaLens-ModernBERT-L-RolesOnly with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rishanthrajendhran/IdeaLens-ModernBERT-L-RolesOnly with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rishanthrajendhran/IdeaLens-ModernBERT-L-RolesOnly")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rishanthrajendhran/IdeaLens-ModernBERT-L-RolesOnly") model = AutoModelForSequenceClassification.from_pretrained("rishanthrajendhran/IdeaLens-ModernBERT-L-RolesOnly", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download thresholds.json from rishanthrajendhran/IdeaLens-ModernBERT-L-RolesOnly: direct link, hf CLI and curl.
- Browser
- Download file 8.56 kB
-
https://huggingface.co/rishanthrajendhran/IdeaLens-ModernBERT-L-RolesOnly/resolve/main/thresholds.json
- Command line
-
hf download hf://rishanthrajendhran/IdeaLens-ModernBERT-L-RolesOnly/thresholds.json
-
curl -L -o thresholds.json https://huggingface.co/rishanthrajendhran/IdeaLens-ModernBERT-L-RolesOnly/resolve/main/thresholds.json
8.56 kB
| { | |
| "model": "rishanthrajendhran/IdeaLens-ModernBERT-L-RolesOnly", | |
| "input": "role sequence", | |
| "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": 0.012178110932931304, | |
| "0.005": 0.03202226234599948, | |
| "0.01": 0.050820946171879766, | |
| "0.02": 0.08874025583267212, | |
| "0.05": 0.19271590709686282, | |
| "0.1": 0.3445788145065308, | |
| "0.2": 0.5637847781181335 | |
| }, | |
| "per_format": { | |
| "0.005": { | |
| "Academic Writing": 0.03074905068054795, | |
| "Creative Writing": 0.050780254583805806, | |
| "Knowledge Article": 0.02830683707818389, | |
| "News Article": 0.029749106999486685, | |
| "Nonfiction Writing": 0.026594594214111568, | |
| "Personal About Page": 0.057549342285841715, | |
| "Personal Blog": 0.033142972107976676, | |
| "User Reviews": 0.027614699173718695 | |
| }, | |
| "0.01": { | |
| "Academic Writing": 0.050818595901131636, | |
| "Creative Writing": 0.07435961253941059, | |
| "Knowledge Article": 0.046408899173140526, | |
| "News Article": 0.04519011946022511, | |
| "Nonfiction Writing": 0.03979125033318996, | |
| "Personal About Page": 0.09494371058046819, | |
| "Personal Blog": 0.05188002179563046, | |
| "User Reviews": 0.044581359669566153 | |
| }, | |
| "0.02": { | |
| "Academic Writing": 0.09393558585643769, | |
| "Creative Writing": 0.11354376375675201, | |
| "Knowledge Article": 0.08171488928794861, | |
| "News Article": 0.08126413011550904, | |
| "Nonfiction Writing": 0.06447851371765137, | |
| "Personal About Page": 0.15712606835365298, | |
| "Personal Blog": 0.09119489645957947, | |
| "User Reviews": 0.0763768013715744 | |
| }, | |
| "0.05": { | |
| "Academic Writing": 0.2177590584754944, | |
| "Creative Writing": 0.21153780460357668, | |
| "Knowledge Article": 0.1741941303014755, | |
| "News Article": 0.18787248730659487, | |
| "Nonfiction Writing": 0.15784218668937683, | |
| "Personal About Page": 0.3007956659793854, | |
| "Personal Blog": 0.18340661823749543, | |
| "User Reviews": 0.16683488667011265 | |
| }, | |
| "0.1": { | |
| "Academic Writing": 0.38454339504241947, | |
| "Creative Writing": 0.3564952921867371, | |
| "Knowledge Article": 0.32872443199157714, | |
| "News Article": 0.3589416813850403, | |
| "Nonfiction Writing": 0.28578355789184573, | |
| "Personal About Page": 0.4639974355697632, | |
| "Personal Blog": 0.3235807943344116, | |
| "User Reviews": 0.30377557992935184 | |
| }, | |
| "0.2": { | |
| "Academic Writing": 0.6284469676017761, | |
| "Creative Writing": 0.5545444560050965, | |
| "Knowledge Article": 0.5475596499443054, | |
| "News Article": 0.5851231265068055, | |
| "Nonfiction Writing": 0.5006991600990296, | |
| "Personal About Page": 0.6657140803337097, | |
| "Personal Blog": 0.5368955302238465, | |
| "User Reviews": 0.5169659304618835 | |
| } | |
| }, | |
| "per_topic": { | |
| "0.005": { | |
| "Health": 0.02771503714434355 | |
| }, | |
| "0.01": { | |
| "Art & Design": 0.06455368360228206, | |
| "Crime & Law": 0.0848147056484747, | |
| "Education & Jobs": 0.04893901671240653, | |
| "Entertainment": 0.052776282767416104, | |
| "Fashion & Beauty": 0.046214627183094464, | |
| "Finance & Business": 0.03809387910561566, | |
| "Food & Dining": 0.05620163321994055, | |
| "Games": 0.04456916508782946, | |
| "Health": 0.050240179982603834, | |
| "History": 0.0766941011790223, | |
| "Home & Hobbies": 0.05156919388435625, | |
| "Literature": 0.059639217307153866, | |
| "Politics": 0.06667086019571308, | |
| "Religion": 0.04779367122472122, | |
| "Science & Tech.": 0.05703240220951432, | |
| "Social Life": 0.05028271657099217, | |
| "Sports & Fitness": 0.054226262268434675, | |
| "Transportation": 0.05702462560109611, | |
| "Travel": 0.0513493331452123 | |
| }, | |
| "0.02": { | |
| "Art & Design": 0.11050019226097904, | |
| "Crime & Law": 0.13328248487486843, | |
| "Education & Jobs": 0.08694445474301735, | |
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| "Food & Dining": 0.09250792869702956, | |
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| "Hardware": 0.08280914541836315, | |
| "Health": 0.09527356621327339, | |
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| "Industrial": 0.08530236737135426, | |
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| "Politics": 0.11570153480549998, | |
| "Religion": 0.08428761937994572, | |
| "Science & Tech.": 0.09519586290916703, | |
| "Social Life": 0.08222430986423447, | |
| "Software": 0.0762969957417692, | |
| "Software Dev.": 0.09487202663196553, | |
| "Sports & Fitness": 0.09333997745304007, | |
| "Transportation": 0.09658251196146012, | |
| "Travel": 0.08589339747509595 | |
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| "Health": 0.20508712260344178, | |
| "History": 0.24279697752595658, | |
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| "Industrial": 0.18642086882651113, | |
| "Literature": 0.22340981288210326, | |
| "Politics": 0.2430728266502545, | |
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| "Software Dev.": 0.19704872133891174, | |
| "Sports & Fitness": 0.1966491911540352, | |
| "Transportation": 0.20710544273846573, | |
| "Travel": 0.19699962709628108 | |
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| "Science & Tech.": 0.38810623056955945, | |
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| "Software Dev.": 0.3329434520836422, | |
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