| --- |
| language: |
| - fr |
| tags: |
| - camembert |
| - camembert-base |
| - chti |
| - ch'ti |
| - NLP |
| - sentiment-analysis |
| - text-classification |
| - fine-tuning |
| - text-classification |
| - pytorch |
| - safetensors |
| widget: |
| - text: "Mi, ch’est bin bon !" |
| license: mit |
| datasets: |
| - custom |
| model-index: |
| - name: NorBERT_Chti |
| results: |
| - task: |
| type: text-classification |
| name: Sentiment Analysis |
| dataset: |
| name: Chti Synthetic Dataset |
| type: custom |
| metrics: |
| - type: accuracy |
| value: 1.0 |
| - type: f1 |
| name: macro-F1 |
| value: 1.0 |
| --- |
| |
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| 🌐 **Looking for the English version?** Just scroll down — it's right below! 👇 |
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| 👉 [Jump to English version](#english-version) |
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| # NorBERT — Ch'ti Sentiment (camembert-base) |
|
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| ## 🇫🇷 Description (Français) |
|
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| **NorBERT** (= 'Nord' + 'BERT' ! 😊) est une version fine-tunée de `camembert-base` pour l’analyse de sentiments en **Ch'ti** (langue régionale du Nord de la France). |
| La tâche cible est la classification de séquence en **trois classes** : |
| - `negatif` |
| - `neutre` |
| - `positif` |
|
|
| ### 🔧 Protocole expérimental |
|
|
| - **Dataset** : jeu de données artificiel construit spécifiquement pour ce projet (phrases en Ch'ti avec annotation sentimentale). |
| - Taille : 167 exemples par classe (501 au total). |
| - Split : train / validation équilibré (2 fichiers CSV). |
| - Colonnes : `classe` (label), `phrase_chtimi` (texte). |
|
|
| - **Prétraitement** : |
| - Normalisation minimale des labels (`positif`, `neutre`, `negatif`). |
| - Tokenisation avec `camembert-base` (max_length=256, truncation). |
| - Gestion optionnelle du déséquilibre par pondération de la loss (ici inutile car dataset équilibré). |
| |
| - **Entraînement** : |
| - Backbone : `camembert-base` |
| - Fine-tuning complet (pas de LoRA) sur Google Colab Pro (GPU). |
| - Optimiseur : AdamW, learning_rate = 2e-5 |
| - Batch size : 16 (train) / 32 (eval) |
| - Epochs : 5 (early stopping patience=3) |
| - Loss : CrossEntropyLoss pondérée (robuste pour datasets déséquilibrés). |
| - Evaluation : accuracy, F1 macro, précision et rappel macro. |
|
|
| - **Évaluation (validation set)** : |
| - Accuracy : **1.0** |
| - F1-macro : **1.0** |
| - Confusion matrix parfaite |
| ⚠️ Résultats probablement biaisés par la proximité train/val → le modèle doit être testé sur des phrases inédites pour valider la généralisation. |
|
|
| **Explication** : les datasets sont des datasets de synthèse, générés par la combinaison de mots et des phrases parmi des listes combinées, train et validation se ressemblent donc fortement. |
| Ce modèle est un hommage poétique, culturel et une démonstration de compétences pour un portfolio, et bien entendu pas un produit à usage commercial. |
| D'autres modèles beaucoup plus proches d'un usage pro sont visibles sur mon GitHub ou par ailleurs sur mon Hugging Face (irrigation, immobilier). |
|
|
| - **Publication** : modèle et tokenizer poussés sur Hugging Face avec `trainer.push_to_hub()` et `tokenizer.push_to_hub()`. |
|
|
| --- |
| 👉 [Essayez la console de Chat (Space)](https://huggingface.co/spaces/jeromex1/NorBERT) avec des phrases telles que : |
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|
| - **"Ch’est l’baraki, les embouteillages sur la Grand-Place !"** |
|
|
| - **"Ch’est pas mal, la ducasse de Lille."** |
|
|
| - **"In est ravi : ce carbonade flamande !"** |
|
|
| --- |
|
|
|
|
| 📘 Découvrez mes **40 projets IA et sciences STEM** ici : |
| 👉 [github.com/Jerome-openclassroom](https://github.com/Jerome-openclassroom) |
| --- |
|
|
| <a name="english-version"></a> |
| # 🌍 English Version |
|
|
| ## Description |
|
|
| **NorBERT** is a fine-tuned version of `camembert-base` for sentiment analysis in **Ch'ti**, a regional language from Northern France. |
| The task is **sequence classification** with three labels: |
| - `negatif` |
| - `neutre` |
| - `positif` |
|
|
| ### 🔧 Experimental protocol |
|
|
| - **Dataset** : synthetic dataset created for this project (Ch'ti sentences annotated with sentiment). |
| - Size : 167 examples per class (501 total). |
| - Balanced train/validation split (CSV files). |
| - Columns : `classe` (label), `phrase_chtimi` (text). |
|
|
| - **Preprocessing** : |
| - Label normalization (`positif`, `neutre`, `negatif`). |
| - Tokenization with `camembert-base` (max_length=256, truncation). |
| - Optional class-weighted loss (not needed here since balanced dataset). |
| |
| - **Training** : |
| - Backbone : `camembert-base` |
| - Full fine-tuning (no LoRA) on Google Colab Pro (GPU). |
| - Optimizer : AdamW, learning_rate = 2e-5 |
| - Batch size : 16 (train) / 32 (eval) |
| - Epochs : 5 (early stopping patience=3) |
| - Loss : Weighted CrossEntropyLoss (robust for class imbalance). |
| - Metrics : accuracy, macro F1, macro precision/recall. |
|
|
| - **Evaluation (validation set)** : |
| - Accuracy : **1.0** |
| - F1-macro : **1.0** |
| - Perfect confusion matrix |
| ⚠️ Likely overestimation due to similarity between train/val → further testing needed on unseen sentences. |
|
|
| **Explanation**: the datasets are synthetic, generated by combining words and sentences from predefined lists, so the train and validation sets are very similar. |
| This model is a poetic and cultural tribute, as well as a demonstration of technical skills for a portfolio, and of course not a product intended for commercial use. |
| Other models much closer to professional applications can be found on my GitHub or also on my Hugging Face profile (e.g., irrigation, real estate). |
|
|
| - **Publication** : model and tokenizer pushed to Hugging Face using `trainer.push_to_hub()` and `tokenizer.push_to_hub()`. |
|
|
| --- |
|
|
| ## 🚀 Usage |
|
|
| ```python |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification |
| import torch |
| |
| repo = "jeromex1/NorBERT_Chti" |
| tok = AutoTokenizer.from_pretrained(repo) |
| mdl = AutoModelForSequenceClassification.from_pretrained(repo) |
| |
| txt = "In est fier de le marché du dimanche" |
| enc = tok(txt, return_tensors="pt") |
| with torch.no_grad(): |
| probs = mdl(**enc).logits.softmax(-1).squeeze() |
| |
| print({mdl.config.id2label[i]: float(probs[i]) for i in range(len(probs))}) |
| ``` |
| --- |
| 👉 [Try the Chat Console (Space)](https://huggingface.co/spaces/jeromex1/NorBERT) with sample phrases such as: |
|
|
| - **"Ch’est l’baraki, les embouteillages sur la Grand-Place !"** |
| *(It’s a mess, traffic jams on the Grand-Place! — “baraki” is a slang term meaning chaotic or low-class)* |
|
|
| - **"Ch’est pas mal, la ducasse de Lille."** |
| *(Not bad at all, the Lille fair. — “ducasse” is a traditional fair in Northern France)* |
|
|
| - **"In est ravi : ce carbonade flamande !"** |
| *(We’re delighted — this Flemish stew is amazing! — “Carbonade flamande” is a regional beef dish with beer sauce)* |
| --- |
|
|
| ## 🎯 Why this project matters |
|
|
| NorBERT_Chti is not just a technical demo — it’s a tribute to regional language, cultural identity, and the expressive power of NLP. |
| It showcases how synthetic data generation, fine-tuning, and deployment can be combined to build a playful yet robust sentiment classifier. |
| Made for demonstrating end-to-end AI skills in a portfolio. |
| |
| |
| 📘 Discover my **40 AI and STEM** science projects here : |
| |
| 👉 [github.com/Jerome-openclassroom](https://github.com/Jerome-openclassroom) |
| --- |
