mDeBERTa-v3-base Fine-tuned on EuroChef+ Customer Support

This model is a fine-tuned version of microsoft/mdeberta-v3-base on the BenTouss/eurochef-cs dataset.

Model Description

This model performs multi-label text classification on customer support messages, identifying:

  • Problem categories: technical_issue, billing, account_management, content_request, feature_request, content_quality
  • Urgency levels: urgent, normal, low_priority
  • Customer types: free_user, premium_user, enterprise
  • Emotional states: frustrated, aggressive
  • Status flags: refund_request

Training Details

Training Data

The model was trained on the EuroChef+ customer support dataset containing synthetic multilingual customer support messages.

Usage

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)

Limitations and Bias

  • The model was trained on synthetic data and may not generalize perfectly to real-world customer support scenarios
  • Performance may vary across different languages
  • The model reflects patterns in the training data which may contain biases

Citation

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}}}}}
}}

Model Card Authors

BenTouss

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