Instructions to use textsightai/textsight-detector-v12-custom with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use textsightai/textsight-detector-v12-custom with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="textsightai/textsight-detector-v12-custom")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("textsightai/textsight-detector-v12-custom") model = AutoModelForSequenceClassification.from_pretrained("textsightai/textsight-detector-v12-custom", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Superseded checkpoint โ do not use
This is an old development checkpoint, kept only so that earlier results remain reproducible. It is not the current detector, it is not what any TextSight product runs, and its behaviour has not been measured on any current benchmark.
Use textsightai/textsight-detector-v23-custom
instead. That card documents measured performance, per-domain false-positive
rates, how to score correctly (log-odds, not softmax), and where the model fails.
If you are benchmarking AI detectors
Please do not report numbers from this checkpoint as representing TextSight. It predates the current model and carries none of the input handling a deployed detector needs. Reproducible measurement code for the current model is at textsight/textsight-detector-benchmark.
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