BenTouss/eurochef-cs
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This model is a fine-tuned version of microsoft/mdeberta-v3-base on the BenTouss/eurochef-cs dataset.
This model performs multi-label text classification on customer support messages, identifying:
The model was trained on the EuroChef+ customer support dataset containing synthetic multilingual customer support messages.
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("{hub_model_id}")
model = AutoModelForSequenceClassification.from_pretrained("{hub_model_id}")
# Prepare input
text = "I need help with my billing issue urgently!"
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=256)
# Get predictions
with torch.no_grad():
outputs = model(**inputs)
probs = torch.sigmoid(outputs.logits)[0]
# Get predicted labels (threshold = 0.5)
predicted_labels = []
for idx, prob in enumerate(probs):
if prob > 0.5:
label = model.config.id2label[idx]
predicted_labels.append((label, prob.item()))
print(predicted_labels)
If you use this model, please cite:
@misc{{mdeberta-eurochef-2026,
author = {{BenTouss}},
title = {{mDeBERTa-v3-base Fine-tuned on EuroChef+ Customer Support}},
year = {{2026}},
publisher = {{Hugging Face}},
howpublished = {{\\url{{https://huggingface.co/{hub_model_id}}}}}
}}
BenTouss
Base model
microsoft/deberta-v3-base