Merge pull request #6 from Tbruand/test/fine-tuned-integration
Browse filestest(handler): ajout du test de sortie pour le modèle fine-tuné
- .cz.toml +29 -0
- .github/PULL_REQUEST_TEMPLATE.md +24 -0
- .github/workflows/ci.yml +31 -0
- .gitignore +55 -0
- app/handler.py +25 -0
- app/interface.py +18 -0
- data/README.md +15 -0
- launch_project.sh +20 -0
- main.py +4 -0
- models/base.py +6 -0
- models/few_shot.py +14 -0
- models/fine_tuned.py +22 -0
- models/zero_shot.py +11 -0
- notebooks/01_exploration.ipynb +0 -0
- notebooks/02_train_camenbert.ipynb +586 -0
- requirements.txt +11 -0
- tests/README.md +62 -0
- tests/test_handler.py +36 -0
- tests/test_interface.py +33 -0
- tox.ini +5 -0
.cz.toml
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[tool.commitizen]
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name = "cz_customize"
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tag_format = "v$version"
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version = "0.1.0"
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version_scheme = "semver"
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update_changelog_on_bump = true
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major_version_zero = true
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[tool.commitizen.customize]
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message_template = "{{type}}({{scope}}): {{message}}"
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questions = [
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{ name = "type", type = "list", message = "Select the type of change you are committing", choices = [
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{ value = "feat", name = "feat: A new feature. Correlates with MINOR in SemVer" },
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{ value = "fix", name = "fix: A bug fix. Correlates with PATCH in SemVer" },
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{ value = "docs", name = "docs: Documentation only changes" },
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{ value = "style", name = "style: Changes that do not affect the meaning of the code" },
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{ value = "refactor", name = "refactor: A code change that neither fixes a bug nor adds a feature" },
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{ value = "perf", name = "perf: A code change that improves performance" },
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{ value = "test", name = "test: Adding or correcting tests" },
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{ value = "build", name = "build: Changes that affect the build system or dependencies" },
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{ value = "ci", name = "ci: Changes to CI/CD configuration" },
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{ value = "chore", name = "chore: Routine tasks like config or meta updates" }
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] },
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{ name = "scope", type = "input", message = "What is the scope of this change (e.g. component or file name):" },
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{ name = "message", type = "input", message = "Write a short, imperative tense description of the change:" },
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{ name = "body", type = "input", message = "Provide a longer description of the change (optional):" },
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{ name = "is_breaking_change", type = "confirm", message = "Are there any breaking changes?" },
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{ name = "breaking_change", type = "input", message = "Describe the breaking changes:" }
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]
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.github/PULL_REQUEST_TEMPLATE.md
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### ✨ Description de la Pull Request
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Décris brièvement ce que fait cette PR. Liste les fonctionnalités ajoutées, corrigées ou modifiées.
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---
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### 🧪 Checklist
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- [ ] Le code fonctionne localement
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- [ ] Tous les tests passent (`pytest`)
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- [ ] Les commits respectent la convention Commitizen
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- [ ] La CI GitHub passe correctement
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---
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### 📌 Notes complémentaires
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Ajoute ici tout commentaire utile, comme des limitations connues ou des TODO à venir.
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---
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### 📷 Captures d’écran (si interface)
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Ajoute des captures d’écran si tu modifies l’interface utilisateur.
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.github/workflows/ci.yml
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name: CI
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on:
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push:
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branches: [main, dev]
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pull_request:
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branches: [main, dev]
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jobs:
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test:
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runs-on: ubuntu-latest
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steps:
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- name: Checkout code
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uses: actions/checkout@v3
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- name: Set up Python
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uses: actions/setup-python@v4
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with:
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python-version: '3.10'
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- name: Install dependencies
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run: |
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python -m pip install --upgrade pip
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pip install -r requirements.txt
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- name: Run tests
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run: |
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pytest
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.gitignore
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# Environnement virtuel
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venv/
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.env
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# Python cache
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__pycache__/
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*.py[cod]
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*.pyo
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*.pyd
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# Notebooks temporaires
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.ipynb_checkpoints/
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# Fichiers temporaires
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.DS_Store
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*.log
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*.tmp
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*.bak
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# Tests
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.coverage
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.tox/
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.cache/
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htmlcov/
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# Ignore les fichiers de modèle mais pas les scripts
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models/
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models/*.pt
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models/*.pth
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models/report.json
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models/test_model.pt
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# Artefacts
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*.csv
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*.tsv
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*.json
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*.xls*
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*.db
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*.sqlite
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*.bin
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*.bpe.model
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# CI/CD / commitizen
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cz.yaml
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cz.toml
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cz.json
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# VSCode / PyCharm / JetBrains
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.vscode/
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.idea/
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*.swp
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# OS
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Thumbs.db
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ehthumbs.db
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app/handler.py
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from models.zero_shot import ZeroShotModel
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from models.few_shot import FewShotModel
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zero_shot_model = ZeroShotModel()
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few_shot_model = FewShotModel()
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def get_fine_tuned_model():
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from models.fine_tuned import FineTunedModel
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return FineTunedModel()
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def predict(text: str, model_type: str = "zero-shot") -> str:
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if model_type == "few-shot":
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results = few_shot_model.predict(text)
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title = "Few-Shot"
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elif model_type == "fine-tuned":
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results = get_fine_tuned_model().predict(text)
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title = "Fine-Tuned"
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else:
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results = zero_shot_model.predict(text)
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title = "Zero-Shot"
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output = f"### Résultat de la classification ({title}) :\n\n"
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for label, score in results:
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output += f"- **{label}** : {score * 100:.1f}%\n"
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return output
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app/interface.py
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import gradio as gr
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from app.handler import predict
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def create_interface():
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return gr.Interface(
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fn=predict,
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inputs=[
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gr.Textbox(label="Texte à analyser"),
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gr.Dropdown(choices=["zero-shot", "few-shot", "fine-tuned"], label="Type de modèle", value="zero-shot")
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],
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outputs="markdown",
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title="🧪 ToxiCheck",
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description="Entrez un texte pour détecter s'il est toxique. Résultat avec score de confiance pour chaque label."
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)
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def launch_app():
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iface = create_interface()
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iface.launch()
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data/README.md
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# 📁 Données locales
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Ce dossier est exclu du versionnage Git via `.gitignore`.
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## 📝 Dataset attendu
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- `jigsaw_toxic_fr_clean.csv` : dataset nettoyé pour le fine-tuning du modèle CamemBERT.
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## 📥 Source du dataset
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Tu peux télécharger le fichier original nettoyé depuis Kaggle ici :
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🔗 [Kaggle – Jigsaw Multilingual Comments (FR)](https://www.kaggle.com/datasets/miklgr500/jigsaw-train-multilingual-coments-google-api?select=jigsaw-toxic-comment-train-google-fr-cleaned.csv)
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## 📦 Instructions
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1. Télécharger le fichier `jigsaw-toxic-comment-train-google-fr-cleaned.csv` depuis Kaggle.
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2. Le renommer en `jigsaw_toxic_fr_clean.csv` (ou adapter les scripts).
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3. Le placer ici dans ce dossier `data/`.
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launch_project.sh
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#!/bin/bash
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echo "🚀 Lancement du projet ToxiCheck..."
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# 1. Active l'environnement virtuel
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if [ -d "venv" ]; then
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echo "📦 Activation de l'environnement virtuel..."
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source venv/bin/activate
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else
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echo "❌ Aucun environnement virtuel trouvé. Tu peux en créer un avec : python -m venv venv"
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exit 1
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fi
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# 2. Installation des dépendances
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echo "📦 Installation des dépendances..."
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pip install -r requirements.txt
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# 3. Lancement de l'app Gradio
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echo "🧪 Lancement de l'application Gradio..."
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python main.py
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main.py
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from app.interface import launch_app
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if __name__ == '__main__':
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launch_app()
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models/base.py
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from abc import ABC, abstractmethod
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class BaseModel(ABC):
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@abstractmethod
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def predict(self, text: str) -> str:
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pass
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models/few_shot.py
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from models.base import BaseModel
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from transformers import pipeline
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class FewShotModel(BaseModel):
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def __init__(self):
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# On utilise un modèle préentraîné pour la classification de texte
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self.classifier = pipeline("text-classification", model="textattack/roberta-base-rotten-tomatoes")
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def predict(self, text: str) -> list[tuple[str, float]]:
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result = self.classifier(text, truncation=True)[0]
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label = result["label"].lower()
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score = result["score"]
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label = "non-toxique" if "pos" in label else "toxique"
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return [(label, score)]
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models/fine_tuned.py
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from transformers import CamembertTokenizer, CamembertForSequenceClassification
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import torch
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class FineTunedModel:
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def __init__(self):
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model_id = "ymokay/toxicheck-camembert"
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self.tokenizer = CamembertTokenizer.from_pretrained(model_id)
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self.model = CamembertForSequenceClassification.from_pretrained(model_id)
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self.model.eval()
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self.label_map = {
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"LABEL_0": "non-toxique",
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"LABEL_1": "toxique"
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}
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def predict(self, text: str):
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inputs = self.tokenizer(text, return_tensors="pt", truncation=True, padding=True)
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with torch.no_grad():
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logits = self.model(**inputs).logits
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probs = torch.softmax(logits, dim=1).squeeze()
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labels = [self.label_map.get(self.model.config.id2label[i], self.model.config.id2label[i]) for i in range(len(probs))]
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return [(label, float(probs[i])) for i, label in enumerate(labels)]
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models/zero_shot.py
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from transformers import pipeline
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from models.base import BaseModel
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class ZeroShotModel(BaseModel):
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def __init__(self):
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self.classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
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def predict(self, text: str) -> list[tuple[str, float]]:
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labels = ["toxique", "non-toxique"]
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result = self.classifier(text, candidate_labels=labels)
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return list(zip(result["labels"], result["scores"]))
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notebooks/01_exploration.ipynb
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The diff for this file is too large to render.
See raw diff
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notebooks/02_train_camenbert.ipynb
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1 |
+
{
|
2 |
+
"cells": [
|
3 |
+
{
|
4 |
+
"cell_type": "code",
|
5 |
+
"execution_count": 1,
|
6 |
+
"id": "e63f42f0-e971-4017-8550-21fdcfc2de11",
|
7 |
+
"metadata": {},
|
8 |
+
"outputs": [
|
9 |
+
{
|
10 |
+
"name": "stdout",
|
11 |
+
"output_type": "stream",
|
12 |
+
"text": [
|
13 |
+
"Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (1.7.0)\n",
|
14 |
+
"Requirement already satisfied: numpy>=1.22.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.24.1)\n",
|
15 |
+
"Requirement already satisfied: scipy>=1.8.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.15.3)\n",
|
16 |
+
"Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.5.1)\n",
|
17 |
+
"Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (3.6.0)\n",
|
18 |
+
"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
|
19 |
+
"\u001b[0m\n",
|
20 |
+
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.1.1\u001b[0m\n",
|
21 |
+
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython -m pip install --upgrade pip\u001b[0m\n",
|
22 |
+
"Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (2.3.0)\n",
|
23 |
+
"Requirement already satisfied: numpy>=1.22.4 in /usr/local/lib/python3.10/dist-packages (from pandas) (1.24.1)\n",
|
24 |
+
"Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas) (2.8.2)\n",
|
25 |
+
"Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2025.2)\n",
|
26 |
+
"Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.10/dist-packages (from pandas) (2025.2)\n",
|
27 |
+
"Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n",
|
28 |
+
"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
|
29 |
+
"\u001b[0m\n",
|
30 |
+
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.1.1\u001b[0m\n",
|
31 |
+
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython -m pip install --upgrade pip\u001b[0m\n",
|
32 |
+
"Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (4.67.1)\n",
|
33 |
+
"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
|
34 |
+
"\u001b[0m\n",
|
35 |
+
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.1.1\u001b[0m\n",
|
36 |
+
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython -m pip install --upgrade pip\u001b[0m\n",
|
37 |
+
"Requirement already satisfied: sentencepiece in /usr/local/lib/python3.10/dist-packages (0.2.0)\n",
|
38 |
+
"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
|
39 |
+
"\u001b[0m\n",
|
40 |
+
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.1.1\u001b[0m\n",
|
41 |
+
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython -m pip install --upgrade pip\u001b[0m\n",
|
42 |
+
"Looking in indexes: https://download.pytorch.org/whl/cu118\n",
|
43 |
+
"Collecting torch==2.0.1\n",
|
44 |
+
" Using cached https://download.pytorch.org/whl/cu118/torch-2.0.1%2Bcu118-cp310-cp310-linux_x86_64.whl (2267.3 MB)\n",
|
45 |
+
"Requirement already satisfied: torchvision in /usr/local/lib/python3.10/dist-packages (0.16.0+cu118)\n",
|
46 |
+
"Requirement already satisfied: torchaudio in /usr/local/lib/python3.10/dist-packages (2.1.0+cu118)\n",
|
47 |
+
"Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch==2.0.1) (3.9.0)\n",
|
48 |
+
"Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch==2.0.1) (4.4.0)\n",
|
49 |
+
"Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch==2.0.1) (1.12)\n",
|
50 |
+
"Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch==2.0.1) (3.0)\n",
|
51 |
+
"Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch==2.0.1) (3.1.2)\n",
|
52 |
+
"Requirement already satisfied: triton==2.0.0 in /usr/local/lib/python3.10/dist-packages (from torch==2.0.1) (2.0.0)\n",
|
53 |
+
"Requirement already satisfied: cmake in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch==2.0.1) (4.0.3)\n",
|
54 |
+
"Requirement already satisfied: lit in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch==2.0.1) (18.1.8)\n",
|
55 |
+
"Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from torchvision) (1.24.1)\n",
|
56 |
+
"Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from torchvision) (2.31.0)\n",
|
57 |
+
"INFO: pip is looking at multiple versions of torchvision to determine which version is compatible with other requirements. This could take a while.\n",
|
58 |
+
"Collecting torchvision\n",
|
59 |
+
" Using cached https://download.pytorch.org/whl/cu118/torchvision-0.22.1%2Bcu118-cp310-cp310-manylinux_2_28_x86_64.whl.metadata (6.1 kB)\n",
|
60 |
+
" Using cached https://download.pytorch.org/whl/cu118/torchvision-0.22.0%2Bcu118-cp310-cp310-manylinux_2_28_x86_64.whl.metadata (6.1 kB)\n",
|
61 |
+
" Using cached https://download.pytorch.org/whl/cu118/torchvision-0.21.0%2Bcu118-cp310-cp310-linux_x86_64.whl.metadata (6.1 kB)\n",
|
62 |
+
" Using cached https://download.pytorch.org/whl/cu118/torchvision-0.20.1%2Bcu118-cp310-cp310-linux_x86_64.whl (6.5 MB)\n",
|
63 |
+
" Using cached https://download.pytorch.org/whl/cu118/torchvision-0.20.0%2Bcu118-cp310-cp310-linux_x86_64.whl (6.5 MB)\n",
|
64 |
+
" Using cached https://download.pytorch.org/whl/cu118/torchvision-0.19.1%2Bcu118-cp310-cp310-linux_x86_64.whl (6.3 MB)\n",
|
65 |
+
" Using cached https://download.pytorch.org/whl/cu118/torchvision-0.19.0%2Bcu118-cp310-cp310-linux_x86_64.whl (6.3 MB)\n",
|
66 |
+
"INFO: pip is still looking at multiple versions of torchvision to determine which version is compatible with other requirements. This could take a while.\n",
|
67 |
+
" Using cached https://download.pytorch.org/whl/cu118/torchvision-0.18.1%2Bcu118-cp310-cp310-linux_x86_64.whl (6.3 MB)\n",
|
68 |
+
" Using cached https://download.pytorch.org/whl/cu118/torchvision-0.18.0%2Bcu118-cp310-cp310-linux_x86_64.whl (6.3 MB)\n",
|
69 |
+
" Using cached https://download.pytorch.org/whl/cu118/torchvision-0.17.2%2Bcu118-cp310-cp310-linux_x86_64.whl (6.2 MB)\n",
|
70 |
+
" Using cached https://download.pytorch.org/whl/cu118/torchvision-0.17.1%2Bcu118-cp310-cp310-linux_x86_64.whl (6.2 MB)\n",
|
71 |
+
" Using cached https://download.pytorch.org/whl/cu118/torchvision-0.17.0%2Bcu118-cp310-cp310-linux_x86_64.whl (6.2 MB)\n",
|
72 |
+
"INFO: This is taking longer than usual. You might need to provide the dependency resolver with stricter constraints to reduce runtime. See https://pip.pypa.io/warnings/backtracking for guidance. If you want to abort this run, press Ctrl + C.\n",
|
73 |
+
" Using cached https://download.pytorch.org/whl/cu118/torchvision-0.16.2%2Bcu118-cp310-cp310-linux_x86_64.whl (6.1 MB)\n",
|
74 |
+
" Using cached https://download.pytorch.org/whl/cu118/torchvision-0.16.1%2Bcu118-cp310-cp310-linux_x86_64.whl (6.1 MB)\n",
|
75 |
+
" Using cached https://download.pytorch.org/whl/cu118/torchvision-0.15.2%2Bcu118-cp310-cp310-linux_x86_64.whl (6.1 MB)\n",
|
76 |
+
"Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.10/dist-packages (from torchvision) (9.3.0)\n",
|
77 |
+
"INFO: pip is looking at multiple versions of torchaudio to determine which version is compatible with other requirements. This could take a while.\n",
|
78 |
+
"Collecting torchaudio\n",
|
79 |
+
" Using cached https://download.pytorch.org/whl/cu118/torchaudio-2.7.1%2Bcu118-cp310-cp310-manylinux_2_28_x86_64.whl.metadata (6.6 kB)\n",
|
80 |
+
" Using cached https://download.pytorch.org/whl/cu118/torchaudio-2.7.0%2Bcu118-cp310-cp310-manylinux_2_28_x86_64.whl.metadata (6.6 kB)\n",
|
81 |
+
" Using cached https://download.pytorch.org/whl/cu118/torchaudio-2.6.0%2Bcu118-cp310-cp310-linux_x86_64.whl.metadata (6.6 kB)\n",
|
82 |
+
" Using cached https://download.pytorch.org/whl/cu118/torchaudio-2.5.1%2Bcu118-cp310-cp310-linux_x86_64.whl (3.3 MB)\n",
|
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+
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" Using cached https://download.pytorch.org/whl/cu118/torchaudio-2.4.1%2Bcu118-cp310-cp310-linux_x86_64.whl (3.3 MB)\n",
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"INFO: pip is still looking at multiple versions of torchaudio to determine which version is compatible with other requirements. This could take a while.\n",
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" Moving to /usr/local/lib/python3.10/dist-packages/torch-2.1.0+cu118.dist-info/\n",
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"\u001b[31mERROR: Could not install packages due to an OSError: [Errno 28] No space left on device\n",
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"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.1.1\u001b[0m\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython -m pip install --upgrade pip\u001b[0m\n"
|
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+
]
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}
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+
],
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+
"source": [
|
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+
"!pip install scikit-learn\n",
|
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+
"!pip install pandas\n",
|
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+
"!pip install tqdm\n",
|
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+
"!pip install sentencepiece\n",
|
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+
"!pip install torch==2.0.1 torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118\n",
|
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+
"!pip install --upgrade transformers"
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+
]
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+
},
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+
{
|
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+
"cell_type": "code",
|
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+
"execution_count": 2,
|
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+
"id": "7db96f7b-0cd3-4710-93d8-391622e60c25",
|
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+
"metadata": {},
|
168 |
+
"outputs": [],
|
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+
"source": [
|
170 |
+
"import os\n",
|
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+
"import json\n",
|
172 |
+
"import pandas as pd\n",
|
173 |
+
"import torch\n",
|
174 |
+
"from torch.optim import AdamW\n",
|
175 |
+
"from torch.utils.data import Dataset, DataLoader\n",
|
176 |
+
"from torch.nn import CrossEntropyLoss\n",
|
177 |
+
"from transformers import CamembertTokenizer, CamembertForSequenceClassification, get_scheduler\n",
|
178 |
+
"from sklearn.model_selection import train_test_split\n",
|
179 |
+
"from sklearn.metrics import classification_report\n",
|
180 |
+
"from tqdm import tqdm"
|
181 |
+
]
|
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+
},
|
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+
{
|
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+
"cell_type": "code",
|
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+
"execution_count": 3,
|
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+
"id": "338ab5df-bc6d-4f2c-8a5e-bc9bb7b658f1",
|
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+
"metadata": {},
|
188 |
+
"outputs": [],
|
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+
"source": [
|
190 |
+
"# ─────────────────────────────────────────────\n",
|
191 |
+
"# ⚙️ Config\n",
|
192 |
+
"# ─────────────────────────────────────────────\n",
|
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+
"DEBUG = False\n",
|
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+
"BATCH_SIZE = 64\n",
|
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+
"EPOCHS = 3 if not DEBUG else 1\n",
|
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+
"MAX_LEN = 128\n",
|
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+
"LR = 2e-5\n",
|
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+
"PATIENCE = 2 # pour l'early stopping"
|
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+
]
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+
},
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+
{
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+
"cell_type": "code",
|
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+
"execution_count": 4,
|
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+
"id": "3ccb7df2-77fc-461b-ac1e-05f1d8be7ed0",
|
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+
"metadata": {},
|
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+
"outputs": [
|
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+
{
|
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+
"name": "stdout",
|
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+
"output_type": "stream",
|
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+
"text": [
|
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+
"Classes : df_labels\n",
|
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+
"0 189412\n",
|
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+
"1 33982\n",
|
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+
"Name: count, dtype: int64\n"
|
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+
]
|
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+
}
|
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+
],
|
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+
"source": [
|
219 |
+
"# ─────────────────────────────────────────────\n",
|
220 |
+
"# 📁 Chargement du dataset\n",
|
221 |
+
"# ─────────────────────────────────────────────\n",
|
222 |
+
"df = pd.read_csv(\"jigsaw-toxic-comment-train-google-fr-cleaned.csv\")\n",
|
223 |
+
"df['comment_text'] = df['comment_text'].astype(str)\n",
|
224 |
+
"df.rename(columns={'comment_text': 'texts'}, inplace=True)\n",
|
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+
"\n",
|
226 |
+
"label_cols = ['toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'identity_hate']\n",
|
227 |
+
"other_cols_to_drop = ['Unnamed: 0.1', 'Unnamed: 0', 'id']\n",
|
228 |
+
"cols_to_drop = label_cols + other_cols_to_drop\n",
|
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+
"\n",
|
230 |
+
"df['df_labels'] = df[label_cols].max(axis=1)\n",
|
231 |
+
"df = df.drop(columns=cols_to_drop)\n",
|
232 |
+
"\n",
|
233 |
+
"# Debug : sous-échantillonnage équilibré\n",
|
234 |
+
"if DEBUG:\n",
|
235 |
+
" df_0 = df[df[\"df_labels\"] == 0].sample(500, random_state=42)\n",
|
236 |
+
" df_1 = df[df[\"df_labels\"] == 1].sample(500, random_state=42)\n",
|
237 |
+
" df = pd.concat([df_0, df_1]).sample(frac=1, random_state=42)\n",
|
238 |
+
"\n",
|
239 |
+
"print(\"Classes :\", df['df_labels'].value_counts())"
|
240 |
+
]
|
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+
},
|
242 |
+
{
|
243 |
+
"cell_type": "code",
|
244 |
+
"execution_count": 5,
|
245 |
+
"id": "83c4cf79-57d9-4d54-8d8f-0e4249b8a930",
|
246 |
+
"metadata": {},
|
247 |
+
"outputs": [],
|
248 |
+
"source": [
|
249 |
+
"# ─────────────────────────────────────────────\n",
|
250 |
+
"# 🔢 Dataset\n",
|
251 |
+
"# ─────────────────────────────────────────────\n",
|
252 |
+
"tokenizer = CamembertTokenizer.from_pretrained(\"camembert-base\")\n",
|
253 |
+
"\n",
|
254 |
+
"class CommentDataset(Dataset):\n",
|
255 |
+
" def __init__(self, texts, labels, tokenizer, max_len):\n",
|
256 |
+
" self.texts = texts\n",
|
257 |
+
" self.labels = labels\n",
|
258 |
+
" self.tokenizer = tokenizer\n",
|
259 |
+
" self.max_len = max_len\n",
|
260 |
+
"\n",
|
261 |
+
" def __len__(self):\n",
|
262 |
+
" return len(self.texts)\n",
|
263 |
+
"\n",
|
264 |
+
" def __getitem__(self, idx):\n",
|
265 |
+
" encoding = self.tokenizer(\n",
|
266 |
+
" self.texts[idx],\n",
|
267 |
+
" padding=\"max_length\",\n",
|
268 |
+
" truncation=True,\n",
|
269 |
+
" max_length=self.max_len,\n",
|
270 |
+
" return_tensors=\"pt\"\n",
|
271 |
+
" )\n",
|
272 |
+
" item = {key: val.squeeze() for key, val in encoding.items()}\n",
|
273 |
+
" item['labels'] = torch.tensor(self.labels[idx], dtype=torch.long)\n",
|
274 |
+
" return item\n",
|
275 |
+
"\n",
|
276 |
+
"# Split\n",
|
277 |
+
"X_train, X_val, y_train, y_val = train_test_split(df[\"texts\"].tolist(), df[\"df_labels\"].tolist(), test_size=0.2, random_state=42)\n",
|
278 |
+
"\n",
|
279 |
+
"train_dataset = CommentDataset(X_train, y_train, tokenizer, MAX_LEN)\n",
|
280 |
+
"val_dataset = CommentDataset(X_val, y_val, tokenizer, MAX_LEN)\n",
|
281 |
+
"\n",
|
282 |
+
"train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True)\n",
|
283 |
+
"val_loader = DataLoader(val_dataset, batch_size=BATCH_SIZE)"
|
284 |
+
]
|
285 |
+
},
|
286 |
+
{
|
287 |
+
"cell_type": "code",
|
288 |
+
"execution_count": 6,
|
289 |
+
"id": "05e8419e-dbeb-42ba-b9ed-ea099e96244a",
|
290 |
+
"metadata": {},
|
291 |
+
"outputs": [
|
292 |
+
{
|
293 |
+
"name": "stderr",
|
294 |
+
"output_type": "stream",
|
295 |
+
"text": [
|
296 |
+
"Some weights of CamembertForSequenceClassification were not initialized from the model checkpoint at camembert-base and are newly initialized: ['classifier.dense.bias', 'classifier.dense.weight', 'classifier.out_proj.bias', 'classifier.out_proj.weight']\n",
|
297 |
+
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
|
298 |
+
]
|
299 |
+
},
|
300 |
+
{
|
301 |
+
"name": "stdout",
|
302 |
+
"output_type": "stream",
|
303 |
+
"text": [
|
304 |
+
"Poids pour la loss : tensor([1.0000, 5.5739])\n"
|
305 |
+
]
|
306 |
+
}
|
307 |
+
],
|
308 |
+
"source": [
|
309 |
+
"# ─────────────────────────────────────────────\n",
|
310 |
+
"# 🧠 Modèle + loss pondérée\n",
|
311 |
+
"# ─────────────────────────────────────────────\n",
|
312 |
+
"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
|
313 |
+
"model = CamembertForSequenceClassification.from_pretrained(\"camembert-base\", num_labels=2).to(device)\n",
|
314 |
+
"\n",
|
315 |
+
"# pondération dynamique\n",
|
316 |
+
"if DEBUG:\n",
|
317 |
+
" class_weights = torch.tensor([1.0, 1.0], dtype=torch.float)\n",
|
318 |
+
"else:\n",
|
319 |
+
" count_0 = df[df[\"df_labels\"] == 0].shape[0]\n",
|
320 |
+
" count_1 = df[df[\"df_labels\"] == 1].shape[0]\n",
|
321 |
+
" class_weights = torch.tensor([1.0, count_0 / count_1], dtype=torch.float)\n",
|
322 |
+
"\n",
|
323 |
+
"print(f\"Poids pour la loss : {class_weights}\")\n",
|
324 |
+
"loss_fn = CrossEntropyLoss(weight=class_weights.to(device))\n",
|
325 |
+
"\n",
|
326 |
+
"# Optimiseur et scheduler\n",
|
327 |
+
"optimizer = AdamW(model.parameters(), lr=LR)\n",
|
328 |
+
"scheduler = get_scheduler(\"linear\", optimizer=optimizer, num_warmup_steps=0, num_training_steps=len(train_loader) * EPOCHS)\n"
|
329 |
+
]
|
330 |
+
},
|
331 |
+
{
|
332 |
+
"cell_type": "code",
|
333 |
+
"execution_count": 7,
|
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+
"id": "0b181b9f-64a6-479f-b585-221a598cded6",
|
335 |
+
"metadata": {},
|
336 |
+
"outputs": [
|
337 |
+
{
|
338 |
+
"name": "stdout",
|
339 |
+
"output_type": "stream",
|
340 |
+
"text": [
|
341 |
+
"\n",
|
342 |
+
"🌟 Epoch 1/3\n"
|
343 |
+
]
|
344 |
+
},
|
345 |
+
{
|
346 |
+
"name": "stderr",
|
347 |
+
"output_type": "stream",
|
348 |
+
"text": [
|
349 |
+
"Entraînement: 100%|██████████| 2793/2793 [18:23<00:00, 2.53it/s]\n"
|
350 |
+
]
|
351 |
+
},
|
352 |
+
{
|
353 |
+
"name": "stdout",
|
354 |
+
"output_type": "stream",
|
355 |
+
"text": [
|
356 |
+
"📉 Loss moyenne : 0.5043\n"
|
357 |
+
]
|
358 |
+
},
|
359 |
+
{
|
360 |
+
"name": "stderr",
|
361 |
+
"output_type": "stream",
|
362 |
+
"text": [
|
363 |
+
"Évaluation: 100%|██████████| 699/699 [01:50<00:00, 6.32it/s]\n"
|
364 |
+
]
|
365 |
+
},
|
366 |
+
{
|
367 |
+
"name": "stdout",
|
368 |
+
"output_type": "stream",
|
369 |
+
"text": [
|
370 |
+
"🎯 F1-score (weighted) : 0.8826\n",
|
371 |
+
"✅ Nouveau meilleur modèle — sauvegarde manuelle...\n",
|
372 |
+
"\n",
|
373 |
+
"🌟 Epoch 2/3\n"
|
374 |
+
]
|
375 |
+
},
|
376 |
+
{
|
377 |
+
"name": "stderr",
|
378 |
+
"output_type": "stream",
|
379 |
+
"text": [
|
380 |
+
"Entraînement: 100%|██████████| 2793/2793 [18:26<00:00, 2.53it/s]\n"
|
381 |
+
]
|
382 |
+
},
|
383 |
+
{
|
384 |
+
"name": "stdout",
|
385 |
+
"output_type": "stream",
|
386 |
+
"text": [
|
387 |
+
"📉 Loss moyenne : 0.4711\n"
|
388 |
+
]
|
389 |
+
},
|
390 |
+
{
|
391 |
+
"name": "stderr",
|
392 |
+
"output_type": "stream",
|
393 |
+
"text": [
|
394 |
+
"Évaluation: 100%|██████████| 699/699 [01:49<00:00, 6.39it/s]\n"
|
395 |
+
]
|
396 |
+
},
|
397 |
+
{
|
398 |
+
"name": "stdout",
|
399 |
+
"output_type": "stream",
|
400 |
+
"text": [
|
401 |
+
"🎯 F1-score (weighted) : 0.8735\n",
|
402 |
+
"⏳ EarlyStopping patience : 1/2\n",
|
403 |
+
"\n",
|
404 |
+
"🌟 Epoch 3/3\n"
|
405 |
+
]
|
406 |
+
},
|
407 |
+
{
|
408 |
+
"name": "stderr",
|
409 |
+
"output_type": "stream",
|
410 |
+
"text": [
|
411 |
+
"Entraînement: 100%|██████████| 2793/2793 [18:26<00:00, 2.52it/s]\n"
|
412 |
+
]
|
413 |
+
},
|
414 |
+
{
|
415 |
+
"name": "stdout",
|
416 |
+
"output_type": "stream",
|
417 |
+
"text": [
|
418 |
+
"📉 Loss moyenne : 0.4485\n"
|
419 |
+
]
|
420 |
+
},
|
421 |
+
{
|
422 |
+
"name": "stderr",
|
423 |
+
"output_type": "stream",
|
424 |
+
"text": [
|
425 |
+
"Évaluation: 100%|██████████| 699/699 [01:50<00:00, 6.35it/s]\n"
|
426 |
+
]
|
427 |
+
},
|
428 |
+
{
|
429 |
+
"name": "stdout",
|
430 |
+
"output_type": "stream",
|
431 |
+
"text": [
|
432 |
+
"🎯 F1-score (weighted) : 0.8816\n",
|
433 |
+
"⏳ EarlyStopping patience : 2/2\n",
|
434 |
+
"🛑 Arrêt anticipé — pas d'amélioration\n"
|
435 |
+
]
|
436 |
+
}
|
437 |
+
],
|
438 |
+
"source": [
|
439 |
+
"best_f1 = 0\n",
|
440 |
+
"patience_counter = 0\n",
|
441 |
+
"os.makedirs(\"outputs/model\", exist_ok=True)\n",
|
442 |
+
"\n",
|
443 |
+
"for epoch in range(EPOCHS):\n",
|
444 |
+
" print(f\"\\n🌟 Epoch {epoch + 1}/{EPOCHS}\")\n",
|
445 |
+
" model.train()\n",
|
446 |
+
" total_loss = 0\n",
|
447 |
+
"\n",
|
448 |
+
" for batch in tqdm(train_loader, desc=\"Entraînement\"):\n",
|
449 |
+
" batch = {k: v.to(device) for k, v in batch.items()}\n",
|
450 |
+
" logits = model(**batch).logits\n",
|
451 |
+
" loss = loss_fn(logits, batch[\"labels\"])\n",
|
452 |
+
" loss.backward()\n",
|
453 |
+
" optimizer.step()\n",
|
454 |
+
" scheduler.step()\n",
|
455 |
+
" optimizer.zero_grad()\n",
|
456 |
+
" total_loss += loss.item()\n",
|
457 |
+
"\n",
|
458 |
+
" avg_loss = total_loss / len(train_loader)\n",
|
459 |
+
" print(f\"📉 Loss moyenne : {avg_loss:.4f}\")\n",
|
460 |
+
"\n",
|
461 |
+
" # 🔍 Évaluation\n",
|
462 |
+
" model.eval()\n",
|
463 |
+
" y_true, y_pred = [], []\n",
|
464 |
+
" with torch.no_grad():\n",
|
465 |
+
" for batch in tqdm(val_loader, desc=\"Évaluation\"):\n",
|
466 |
+
" batch = {k: v.to(device) for k, v in batch.items()}\n",
|
467 |
+
" logits = model(**batch).logits\n",
|
468 |
+
" preds = torch.argmax(logits, dim=1)\n",
|
469 |
+
" y_true.extend(batch[\"labels\"].cpu().tolist())\n",
|
470 |
+
" y_pred.extend(preds.cpu().tolist())\n",
|
471 |
+
"\n",
|
472 |
+
" report = classification_report(y_true, y_pred, target_names=[\"Non toxique\", \"Toxique\"], output_dict=True)\n",
|
473 |
+
" f1 = report[\"weighted avg\"][\"f1-score\"]\n",
|
474 |
+
" print(f\"🎯 F1-score (weighted) : {f1:.4f}\")\n",
|
475 |
+
"\n",
|
476 |
+
" if f1 > best_f1:\n",
|
477 |
+
" best_f1 = f1\n",
|
478 |
+
" patience_counter = 0\n",
|
479 |
+
" print(\"✅ Nouveau meilleur modèle — sauvegarde manuelle...\")\n",
|
480 |
+
"\n",
|
481 |
+
" import os\n",
|
482 |
+
"\n",
|
483 |
+
" # 📂 Dossier de sauvegarde\n",
|
484 |
+
" save_dir = \"outputs/model\"\n",
|
485 |
+
" os.makedirs(save_dir, exist_ok=True)\n",
|
486 |
+
"\n",
|
487 |
+
" # 💾 Sauvegarde manuelle des poids\n",
|
488 |
+
" torch.save(model.state_dict(), os.path.join(save_dir, \"pytorch_model.bin\"))\n",
|
489 |
+
"\n",
|
490 |
+
" # 💾 Sauvegarde de la configuration du modèle\n",
|
491 |
+
" model.config.to_json_file(os.path.join(save_dir, \"config.json\"))\n",
|
492 |
+
"\n",
|
493 |
+
" # 💾 Sauvegarde du tokenizer\n",
|
494 |
+
" tokenizer.save_pretrained(save_dir)\n",
|
495 |
+
"\n",
|
496 |
+
" # 💾 Sauvegarde des métriques\n",
|
497 |
+
" with open(\"outputs/metrics.json\", \"w\") as f:\n",
|
498 |
+
" json.dump(report, f, indent=4)\n",
|
499 |
+
"\n",
|
500 |
+
" else:\n",
|
501 |
+
" patience_counter += 1\n",
|
502 |
+
" print(f\"⏳ EarlyStopping patience : {patience_counter}/{PATIENCE}\")\n",
|
503 |
+
" if patience_counter >= PATIENCE:\n",
|
504 |
+
" print(\"🛑 Arrêt anticipé — pas d'amélioration\")\n",
|
505 |
+
" break"
|
506 |
+
]
|
507 |
+
},
|
508 |
+
{
|
509 |
+
"cell_type": "code",
|
510 |
+
"execution_count": 8,
|
511 |
+
"id": "ba6f2d7c-0daf-48db-96a2-33935dca1d9e",
|
512 |
+
"metadata": {},
|
513 |
+
"outputs": [
|
514 |
+
{
|
515 |
+
"name": "stdout",
|
516 |
+
"output_type": "stream",
|
517 |
+
"text": [
|
518 |
+
"📊 Métriques sauvegardées :\n",
|
519 |
+
"\n",
|
520 |
+
"🗂 Classe : Non toxique\n",
|
521 |
+
" 🔸 Précision : 0.9294\n",
|
522 |
+
" 🔸 Rappel : 0.9329\n",
|
523 |
+
" 🔸 F1-score : 0.9312\n",
|
524 |
+
"\n",
|
525 |
+
"🗂 Classe : Toxique\n",
|
526 |
+
" 🔸 Précision : 0.6193\n",
|
527 |
+
" 🔸 Rappel : 0.6065\n",
|
528 |
+
" 🔸 F1-score : 0.6129\n",
|
529 |
+
"\n",
|
530 |
+
"🔄 Moyennes pondérées (weighted avg) :\n",
|
531 |
+
" ✅ Précision : 0.8821\n",
|
532 |
+
" ✅ Rappel : 0.8831\n",
|
533 |
+
" ✅ F1-score : 0.8826\n"
|
534 |
+
]
|
535 |
+
}
|
536 |
+
],
|
537 |
+
"source": [
|
538 |
+
"import json\n",
|
539 |
+
"import os\n",
|
540 |
+
"\n",
|
541 |
+
"# 📁 Chemin du fichier de métriques\n",
|
542 |
+
"metrics_path = \"outputs/metrics.json\"\n",
|
543 |
+
"\n",
|
544 |
+
"# ✅ Vérifie l'existence du fichier\n",
|
545 |
+
"if os.path.exists(metrics_path):\n",
|
546 |
+
" with open(metrics_path, \"r\") as f:\n",
|
547 |
+
" metrics = json.load(f)\n",
|
548 |
+
"\n",
|
549 |
+
" print(\"📊 Métriques sauvegardées :\\n\")\n",
|
550 |
+
" for label in [\"Non toxique\", \"Toxique\"]:\n",
|
551 |
+
" print(f\"🗂 Classe : {label}\")\n",
|
552 |
+
" print(f\" 🔸 Précision : {metrics[label]['precision']:.4f}\")\n",
|
553 |
+
" print(f\" 🔸 Rappel : {metrics[label]['recall']:.4f}\")\n",
|
554 |
+
" print(f\" 🔸 F1-score : {metrics[label]['f1-score']:.4f}\\n\")\n",
|
555 |
+
"\n",
|
556 |
+
" print(\"🔄 Moyennes pondérées (weighted avg) :\")\n",
|
557 |
+
" print(f\" ✅ Précision : {metrics['weighted avg']['precision']:.4f}\")\n",
|
558 |
+
" print(f\" ✅ Rappel : {metrics['weighted avg']['recall']:.4f}\")\n",
|
559 |
+
" print(f\" ✅ F1-score : {metrics['weighted avg']['f1-score']:.4f}\")\n",
|
560 |
+
"else:\n",
|
561 |
+
" print(\"❌ Aucune métrique trouvée dans outputs/metrics.json\")"
|
562 |
+
]
|
563 |
+
}
|
564 |
+
],
|
565 |
+
"metadata": {
|
566 |
+
"kernelspec": {
|
567 |
+
"display_name": "Python 3 (ipykernel)",
|
568 |
+
"language": "python",
|
569 |
+
"name": "python3"
|
570 |
+
},
|
571 |
+
"language_info": {
|
572 |
+
"codemirror_mode": {
|
573 |
+
"name": "ipython",
|
574 |
+
"version": 3
|
575 |
+
},
|
576 |
+
"file_extension": ".py",
|
577 |
+
"mimetype": "text/x-python",
|
578 |
+
"name": "python",
|
579 |
+
"nbconvert_exporter": "python",
|
580 |
+
"pygments_lexer": "ipython3",
|
581 |
+
"version": "3.10.12"
|
582 |
+
}
|
583 |
+
},
|
584 |
+
"nbformat": 4,
|
585 |
+
"nbformat_minor": 5
|
586 |
+
}
|
requirements.txt
CHANGED
@@ -0,0 +1,11 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
gradio
|
2 |
+
transformers
|
3 |
+
torch
|
4 |
+
scikit-learn
|
5 |
+
pandas
|
6 |
+
pytest
|
7 |
+
pytest-cov
|
8 |
+
commitizen
|
9 |
+
datasets
|
10 |
+
sentencepiece
|
11 |
+
accelerate
|
tests/README.md
ADDED
@@ -0,0 +1,62 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
## ✅ Tests unitaires & couverture
|
2 |
+
|
3 |
+
Ce projet dispose de tests automatisés pour valider les principales fonctionnalités :
|
4 |
+
|
5 |
+
### 📂 Fichiers de test
|
6 |
+
|
7 |
+
| Fichier | Description |
|
8 |
+
| ------------------------- | --------------------------------------------------------------------------------------------------------------------- |
|
9 |
+
| `tests/test_handler.py` | Vérifie le bon fonctionnement de la fonction `predict()` en mode `zero-shot` et `few-shot`. |
|
10 |
+
| `tests/test_interface.py` | Teste l'interface Gradio, les comportements inattendus (entrée vide, modèle invalide), et la création de l'interface. |
|
11 |
+
|
12 |
+
---
|
13 |
+
|
14 |
+
### 🧪 Lancer les tests
|
15 |
+
|
16 |
+
```bash
|
17 |
+
python -m pytest --cov=app --cov-report=term-missing
|
18 |
+
```
|
19 |
+
|
20 |
+
Ce qui génère un rapport de couverture avec les lignes non couvertes :
|
21 |
+
|
22 |
+
```
|
23 |
+
Name Stmts Miss Cover Missing
|
24 |
+
------------------------------------------------
|
25 |
+
app/handler.py 16 0 100%
|
26 |
+
app/interface.py 7 2 71% 17-18
|
27 |
+
------------------------------------------------
|
28 |
+
TOTAL 23 2 91%
|
29 |
+
```
|
30 |
+
|
31 |
+
> 🌟 Les lignes 17-18 non couvertes correspondent à l’exécution directe de l’interface dans le fichier `main.py`.
|
32 |
+
> Ces lignes ne sont volontairement **pas testées** car elles concernent le lancement interactif de l'application (`iface.launch()`), hors du périmètre des tests unitaires.
|
33 |
+
|
34 |
+
---
|
35 |
+
|
36 |
+
### 🚫 Hook `pre-push` automatique
|
37 |
+
|
38 |
+
Pour garantir la stabilité du dépôt, un **hook Git `pre-push`** a été mis en place.
|
39 |
+
|
40 |
+
🧹 Il exécute automatiquement les tests avant chaque `git push`.
|
41 |
+
|
42 |
+
#### Exemple `.git/hooks/pre-push`
|
43 |
+
|
44 |
+
```bash
|
45 |
+
#!/bin/sh
|
46 |
+
echo "🔍 Exécution des tests unitaires avant le push..."
|
47 |
+
python -m pytest --cov=app --cov-report=term-missing
|
48 |
+
if [ $? -ne 0 ]; then
|
49 |
+
echo "❌ Push bloqué : les tests ont échoué."
|
50 |
+
exit 1
|
51 |
+
fi
|
52 |
+
```
|
53 |
+
|
54 |
+
#### 🔧 Installation manuelle
|
55 |
+
|
56 |
+
1. Crée un fichier `.git/hooks/pre-push` si ce n’est pas déjà fait,
|
57 |
+
2. Colle le script ci-dessus,
|
58 |
+
3. Rends-le exécutable :
|
59 |
+
|
60 |
+
```bash
|
61 |
+
chmod +x .git/hooks/pre-push
|
62 |
+
```
|
tests/test_handler.py
CHANGED
@@ -0,0 +1,36 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import sys
|
2 |
+
import os
|
3 |
+
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
|
4 |
+
|
5 |
+
from app.handler import predict
|
6 |
+
|
7 |
+
def test_zero_shot_prediction_output():
|
8 |
+
text = "Tu es complètement stupide"
|
9 |
+
output = predict(text)
|
10 |
+
|
11 |
+
print("Résultat brut :", output)
|
12 |
+
|
13 |
+
# Vérifie que le format markdown est respecté
|
14 |
+
assert "### Résultat de la classification" in output
|
15 |
+
assert "**toxique**" in output
|
16 |
+
assert "**non-toxique**" in output
|
17 |
+
assert "%" in output
|
18 |
+
|
19 |
+
def test_few_shot_prediction_output():
|
20 |
+
from app.handler import predict
|
21 |
+
text = "Tu es un abruti fini"
|
22 |
+
output = predict(text, model_type="few-shot")
|
23 |
+
|
24 |
+
print("Résultat few-shot :", output)
|
25 |
+
|
26 |
+
assert "### Résultat de la classification" in output
|
27 |
+
assert "toxique" in output or "non-toxique" in output
|
28 |
+
|
29 |
+
def test_fine_tuned_prediction_output():
|
30 |
+
text = "Tu es stupide"
|
31 |
+
output = predict(text, model_type="fine-tuned")
|
32 |
+
|
33 |
+
print("Résultat fine-tuned :", output)
|
34 |
+
|
35 |
+
assert "### Résultat de la classification" in output
|
36 |
+
assert "toxique" in output or "non-toxique" in output
|
tests/test_interface.py
CHANGED
@@ -0,0 +1,33 @@
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|
1 |
+
import sys
|
2 |
+
import os
|
3 |
+
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
|
4 |
+
|
5 |
+
from app.interface import predict
|
6 |
+
|
7 |
+
def test_predict_zero_shot():
|
8 |
+
result = predict("Tu es gentil.", model_type="zero-shot")
|
9 |
+
assert isinstance(result, str)
|
10 |
+
assert "Résultat de la classification" in result
|
11 |
+
|
12 |
+
def test_predict_few_shot():
|
13 |
+
result = predict("Tu es débile.", model_type="few-shot")
|
14 |
+
assert isinstance(result, str)
|
15 |
+
assert "Résultat de la classification" in result
|
16 |
+
|
17 |
+
def test_predict_empty_input():
|
18 |
+
try:
|
19 |
+
result = predict("", model_type="zero-shot")
|
20 |
+
except ValueError as e:
|
21 |
+
assert "at least one sequence" in str(e)
|
22 |
+
|
23 |
+
def test_predict_invalid_model():
|
24 |
+
try:
|
25 |
+
predict("Texte test", model_type="unknown")
|
26 |
+
except ValueError as e:
|
27 |
+
assert "Modèle inconnu" in str(e)
|
28 |
+
|
29 |
+
def test_create_interface():
|
30 |
+
from app.interface import create_interface
|
31 |
+
iface = create_interface()
|
32 |
+
assert iface.fn is not None
|
33 |
+
assert len(iface.input_components) == 2
|
tox.ini
CHANGED
@@ -0,0 +1,5 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
[pytest]
|
2 |
+
testpaths = tests
|
3 |
+
python_files = test_*.py
|
4 |
+
filterwarnings =
|
5 |
+
ignore::DeprecationWarning
|