juenp/FrenchTextCategorizer

model_name: FrenchTextCategorizer language: French tags:

• text-classification
• fine-tuned
• french license: mit dataset: "French News Dataset"

📝 Usage

This model is a FLAUBERT fine-tuned version to categorize French texts into the following categories:

CULTURE, DEBATS_ET_OPINIONS, ECONOMIE, EDUCATION, FAIT_DIVERS, INTERNATIONAL, LIFESTYLE, NUMERIQUE, POLITIQUE, RELIGION, SANTE, SCIENCE_ET_ENVIRONNEMENT, SOCIETE, SPORT, INDEFINI

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🚀 Quick Start

from transformers import AutoModelForSequenceClassification

model = AutoModelForSequenceClassification.from_pretrained("juenp/FrenchTextCategorizer")
model.eval()

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🔎 Full Example (with Tokenizer, Prediction and Probabilities)

from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch
import torch.nn.functional as F

# Load model and tokenizer
model = AutoModelForSequenceClassification.from_pretrained("juenp/FrenchTextCategorizer")
tokenizer = AutoTokenizer.from_pretrained("juenp/FrenchTextCategorizer")
model.eval()

# Input text
text = "Ce film est un chef-d'œuvre incroyable, tout était parfait."

# Tokenize
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
inputs.pop("token_type_ids", None)

# Predict
with torch.no_grad():
    outputs = model(**inputs)

logits = outputs.logits
probs = F.softmax(logits, dim=-1)
predicted_class_idx = torch.argmax(probs, dim=-1).item()

# Decode predicted class from config
predicted_class = model.config.id2label[str(predicted_class_idx)]
prob_percentages = [round(p.item() * 100, 2) for p in probs[0]]

# Output
print(f"Texte : {text}")
print(f"Classe prédite : {predicted_class}")
print(f"Probabilités (%) : {prob_percentages}")

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📋 Notes

• model.config.id2label is automatically loaded from the model's configuration (config.json).
• If you want to process multiple texts at once, simply pass a list of texts to the tokenizer.

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✅ Ready for Inference!