impresso ad classification xlm one class EU AI Act Compliance Profile

impresso-project / impresso-ad-classification-xlm-one-class

Multilingual ad detector built on top of an XLM-R–based genre model and a lightweight binary decision layer. It combines the model’s “Promotion” probability with smart chunking, pooling, and rules (prices, phones, cues, etc.) to classify any input text as AD or NOT_AD.

Optimized for FR / DE / LB, but works on “other” languages too.

🔌 Quickstart

from transformers import pipeline
pipe = pipeline(
    "text-classification",
    model="impresso-project/impresso-ad-classification-xlm-one-class",
    trust_remote_code=True,   # required to use the bundled pipeline.py
)

pipe("Appartement 3 pièces… Fr. 2'100.–, tél. 079 ...")
# [{'label': 'AD', 'score': 0.87, 'promotion_prob': 0.83, 'threshold_used': 0.80,
#   'xgenre_top_label': 'Promotion', 'xgenre_top_prob': 0.88, ...diagnostics...}]

Batch input

pipe(["texte A", "Text B"])  # → list of dicts in the same order

Dict input (with metadata)

pipe([
  {"ft": "Annonce en français ...", "lg": "fr"},
  {"ft": "Mietwohnung ...", "lg": "de"}
])

📦 What this repo contains

• model.safetensors / pytorch_model.bin + config.json + tokenizer files
• pipeline.py — custom HF pipeline that turns the multi-genre model into a binary ad detector (AD / NOT_AD)
• best_params_final.json — default inference knobs (loaded automatically if present)
• (optional) meta_classifier.pkl — extra stacking model (if you provide one)

The Hugging Face inference widget doesn’t run custom code; use Python as shown above or build a small Space.

🧠 How it works (inference)

1. Normalize text → optional chunking by word count
2. Encode chunks → get logits from the base model
3. Pool across chunks (max/mean/logits_* methods)
4. Read “Promotion” probability + other label probs
5. Apply adaptive thresholding (language- & length-aware) and rules (prices, phones,

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