NPS Description Generator (mT5-small)
Fine-tuned google/mt5-small pour générer des descriptions CRM personnalisées
pour les clients détracteurs NPS. Projet : Système NPS Dior/Reetain — Nada El Maliki.
Métriques
| Métrique | Valeur |
|---|---|
| ROUGE-2 (best) | 0.1529 |
| ROUGE-1 (best) | ~0.32 |
| ROUGE-L (best) | ~0.26 |
Usage
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch
tokenizer = AutoTokenizer.from_pretrained("nada-05/nps-description-generator-mt5")
model = AutoModelForSeq2SeqLM.from_pretrained("nada-05/nps-description-generator-mt5")
model.eval()
# Construire le prompt
prompt = (
"generate nps description: [lang:fr] "
"score:3/10 segment:Detractor urgency:high "
"comments: J'ai attendu 40 minutes sans assistance. "
"improvements: advisor | store"
)
enc = tokenizer(prompt, return_tensors="pt", max_length=256, truncation=True)
with torch.no_grad():
out = model.generate(**enc, max_new_tokens=200, num_beams=4,
no_repeat_ngram_size=3)
description = tokenizer.decode(out[0], skip_special_tokens=True)
print(description)
Format du prompt
generate nps description: [lang:<fr|en>]
score:<0-10>/10
segment:<Detractor|Passive|Promoter>
urgency:<high|medium|low|none>
comments: <texte commentaires>
improvements: <axes signalés> (optionnel)
Architecture
- Base :
google/mt5-small(~300M params) - Dataset : 1014 exemples détracteurs FR/EN (fine-tuning 10 epochs)
- Loss : Cross-Entropy seq2seq
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