Text Classification
Transformers
Safetensors
English
modernbert
ai-text-detection
prose-provenance
text-embeddings-inference
Instructions to use rishanthrajendhran/ProseLens-ModernBERT-L with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rishanthrajendhran/ProseLens-ModernBERT-L with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rishanthrajendhran/ProseLens-ModernBERT-L")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rishanthrajendhran/ProseLens-ModernBERT-L") model = AutoModelForSequenceClassification.from_pretrained("rishanthrajendhran/ProseLens-ModernBERT-L", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Add calibrated thresholds (IdeaLens-1M calibration split)
Browse files- thresholds.json +83 -0
thresholds.json
ADDED
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{
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"model": "rishanthrajendhran/ProseLens-ModernBERT-L",
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"input": "document",
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"flag_rule": "flag the document as AI when P(human) < cut",
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"calibration": {
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"data": "IdeaLens-1M, calibration split (human documents only)",
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"scores_from": "the training run's stored predictions on the calibration split",
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"n_humans": 80000,
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"n_per_format": {
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"Academic Writing": 10000,
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"Creative Writing": 10000,
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"Knowledge Article": 10000,
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"News Article": 10000,
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"Nonfiction Writing": 10000,
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"Personal About Page": 10000,
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"Personal Blog": 10000,
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"User Reviews": 10000
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}
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},
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"method": {
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"global": "the target-FPR quantile of P(human) over all calibration humans",
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"per_group": "the group's own quantile, shrunk toward the global cut with weight n / (n + 2500)",
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"min_group_humans": 200,
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"estimability": "a cut at FPR q is given only when q * n >= 25 calibration humans",
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"missing_cut": "no cut for a group means none could be estimated; do not substitute the global cut"
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},
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"fpr_targets": [
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0.001,
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0.005,
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0.01,
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0.02,
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0.05
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],
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"global": {
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"0.001": 0.005766863141208887,
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"0.005": 0.043511959370225665,
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"0.01": 0.18790396720170977,
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"0.02": 0.8414405298233032,
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"0.05": 0.9982767522335052
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},
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"per_format": {
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"0.005": {
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"Academic Writing": 0.48511697150394323,
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"Creative Writing": 0.7774797902218998,
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"Knowledge Article": 0.0264915764220059,
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"News Article": 0.042131727289408456,
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"Nonfiction Writing": 0.016996091064065695,
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"Personal About Page": 0.08180167488381268,
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"Personal Blog": 0.42620853902027017,
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"User Reviews": 0.24418430317565804
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},
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"0.01": {
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"Academic Writing": 0.8101551976799966,
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"Creative Writing": 0.83455596023798,
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"Knowledge Article": 0.07479736334085464,
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"News Article": 0.16216902357339863,
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"Nonfiction Writing": 0.0544652368426323,
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"Personal About Page": 0.36414291030168533,
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"Personal Blog": 0.7883320108056069,
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"User Reviews": 0.7249871950745583
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},
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"0.02": {
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"Academic Writing": 0.9664362020492554,
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"Creative Writing": 0.9677177896499634,
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"Knowledge Article": 0.28712427091598514,
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"News Article": 0.6937795658111573,
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"Nonfiction Writing": 0.22745220160484314,
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| 68 |
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"Personal About Page": 0.9371422376632691,
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| 69 |
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"Personal Blog": 0.9637664785385132,
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"User Reviews": 0.9604384603500367
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},
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"0.05": {
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"Academic Writing": 0.9994967925548554,
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"Creative Writing": 0.999563969373703,
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"Knowledge Article": 0.9463144671916961,
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"News Article": 0.9944453680515289,
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"Nonfiction Writing": 0.8933786427974701,
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| 78 |
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"Personal About Page": 0.9992063510417939,
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| 79 |
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"Personal Blog": 0.9993082511425019,
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"User Reviews": 0.999339975118637
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}
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}
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}
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