Text Classification
Transformers
Safetensors
English
modernbert
ai-text-detection
idea-provenance
text-embeddings-inference
Instructions to use rishanthrajendhran/IdeaLens-ModernBERT-L-NoParaphrase with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rishanthrajendhran/IdeaLens-ModernBERT-L-NoParaphrase with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rishanthrajendhran/IdeaLens-ModernBERT-L-NoParaphrase")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rishanthrajendhran/IdeaLens-ModernBERT-L-NoParaphrase") model = AutoModelForSequenceClassification.from_pretrained("rishanthrajendhran/IdeaLens-ModernBERT-L-NoParaphrase", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download thresholds.json from rishanthrajendhran/IdeaLens-ModernBERT-L-NoParaphrase: direct link, hf CLI and curl.
- Browser
- Download file 5.93 kB
-
https://huggingface.co/rishanthrajendhran/IdeaLens-ModernBERT-L-NoParaphrase/resolve/main/thresholds.json
- Command line
-
hf download hf://rishanthrajendhran/IdeaLens-ModernBERT-L-NoParaphrase/thresholds.json
-
curl -L -o thresholds.json https://huggingface.co/rishanthrajendhran/IdeaLens-ModernBERT-L-NoParaphrase/resolve/main/thresholds.json
5.93 kB
| { | |
| "model": "rishanthrajendhran/IdeaLens-ModernBERT-L-NoParaphrase", | |
| "input": "outline", | |
| "flag_rule": "flag the document as AI when P(human) < cut", | |
| "calibration": { | |
| "data": "WildOutlines, calibration split (human documents only)", | |
| "scores_from": "the released weights, scored through the idealens package", | |
| "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 | |
| ], | |
| "global": { | |
| "0.001": 0.004950821630149081, | |
| "0.005": 0.02342468035197943, | |
| "0.01": 0.06067131979531868, | |
| "0.02": 0.23576730120276254, | |
| "0.05": 0.7909952590839185 | |
| }, | |
| "per_format": { | |
| "0.005": { | |
| "Academic Writing": 0.07555358258311677, | |
| "Creative Writing": 0.11624698148069071, | |
| "Knowledge Article": 0.019424670220982234, | |
| "News Article": 0.018877491877219182, | |
| "Nonfiction Writing": 0.014801753559624426, | |
| "Personal About Page": 0.04334194044726723, | |
| "Personal Blog": 0.05175564260631697, | |
| "User Reviews": 0.05215746833281784 | |
| }, | |
| "0.01": { | |
| "Academic Writing": 0.30168418345610853, | |
| "Creative Writing": 0.26211381646434895, | |
| "Knowledge Article": 0.036163894470849334, | |
| "News Article": 0.049543920392250654, | |
| "Nonfiction Writing": 0.028810618555034417, | |
| "Personal About Page": 0.12242291308888592, | |
| "Personal Blog": 0.16644452540526145, | |
| "User Reviews": 0.19031812686047817 | |
| }, | |
| "0.02": { | |
| "Academic Writing": 0.5626391690756242, | |
| "Creative Writing": 0.5343154320318914, | |
| "Knowledge Article": 0.09131072890661113, | |
| "News Article": 0.18014470176754305, | |
| "Nonfiction Writing": 0.08423913090880084, | |
| "Personal About Page": 0.3933419680360302, | |
| "Personal Blog": 0.44821969233765957, | |
| "User Reviews": 0.4615502978594149 | |
| }, | |
| "0.05": { | |
| "Academic Writing": 0.9034828838060145, | |
| "Creative Writing": 0.8762679898897557, | |
| "Knowledge Article": 0.4550881365538776, | |
| "News Article": 0.6791936334402358, | |
| "Nonfiction Writing": 0.45733708141797413, | |
| "Personal About Page": 0.8346417259891522, | |
| "Personal Blog": 0.8398029361270587, | |
| "User Reviews": 0.877508203466506 | |
| } | |
| }, | |
| "per_topic": { | |
| "0.005": { | |
| "Health": 0.022789031562102866 | |
| }, | |
| "0.01": { | |
| "Art & Design": 0.08523636574413213, | |
| "Crime & Law": 0.0805688569701574, | |
| "Education & Jobs": 0.07711304848205908, | |
| "Entertainment": 0.051725862674968315, | |
| "Fashion & Beauty": 0.13440074693829035, | |
| "Finance & Business": 0.03902568614421119, | |
| "Food & Dining": 0.05803920525042723, | |
| "Games": 0.057223274190934315, | |
| "Health": 0.04492207499015485, | |
| "History": 0.23157010595566033, | |
| "Home & Hobbies": 0.06596159123798341, | |
| "Literature": 0.1656287907568481, | |
| "Politics": 0.10915063082060605, | |
| "Religion": 0.062251729096985446, | |
| "Science & Tech.": 0.051504562711774754, | |
| "Social Life": 0.05717329947448666, | |
| "Sports & Fitness": 0.08485655220538925, | |
| "Transportation": 0.07194678865458264, | |
| "Travel": 0.09220869624054712 | |
| }, | |
| "0.02": { | |
| "Art & Design": 0.34981177606734215, | |
| "Crime & Law": 0.3211582586127026, | |
| "Education & Jobs": 0.230268689278505, | |
| "Entertainment": 0.22814291050574612, | |
| "Fashion & Beauty": 0.4103276416986731, | |
| "Finance & Business": 0.11991580144833784, | |
| "Food & Dining": 0.2970898645922292, | |
| "Games": 0.19147999880510852, | |
| "Hardware": 0.29635509273881666, | |
| "Health": 0.14928968279483779, | |
| "History": 0.4990280725601881, | |
| "Home & Hobbies": 0.2536066942843139, | |
| "Industrial": 0.1867194129459971, | |
| "Literature": 0.4753249064288996, | |
| "Politics": 0.3197100815106474, | |
| "Religion": 0.3150631545315935, | |
| "Science & Tech.": 0.1942258902493757, | |
| "Social Life": 0.23831430057064804, | |
| "Software": 0.1849620188878797, | |
| "Software Dev.": 0.25388250839795734, | |
| "Sports & Fitness": 0.30859623266675495, | |
| "Transportation": 0.2858273867294666, | |
| "Travel": 0.27639604001919316 | |
| }, | |
| "0.05": { | |
| "Art & Design": 0.8448356881542616, | |
| "Crime & Law": 0.8600257853570801, | |
| "Education & Jobs": 0.7522090353933664, | |
| "Entertainment": 0.7987425335197011, | |
| "Fashion & Beauty": 0.823750877914615, | |
| "Finance & Business": 0.5272754046421285, | |
| "Food & Dining": 0.8276740432412281, | |
| "Games": 0.7451750476334372, | |
| "Hardware": 0.7941178644535245, | |
| "Health": 0.6633092143777839, | |
| "History": 0.879794141844777, | |
| "Home & Hobbies": 0.7967050876857762, | |
| "Industrial": 0.7766523212735237, | |
| "Literature": 0.8734477816691915, | |
| "Politics": 0.8360229706184283, | |
| "Religion": 0.8487787044200342, | |
| "Science & Tech.": 0.753716519473606, | |
| "Social Life": 0.757869426930674, | |
| "Software": 0.7058872195208943, | |
| "Software Dev.": 0.7640932873028324, | |
| "Sports & Fitness": 0.8165972168318504, | |
| "Transportation": 0.8289652521808427, | |
| "Travel": 0.8109604440410111 | |
| } | |
| } | |
| } | |