Model Card for d-80-vulcan

Model Details

Model Description

d-80-vulcan is a fine-tuned version of the DistilBERT model for intent classification in e-commerce platforms.
It is designed to understand user queries and map them to predefined intents, enabling intelligent product search, recommendation, and customer support automation.

• Developed by: Dharunpandi
• Model type: Transformer-based intent classification model
• Language(s): English
• License: MIT
• Finetuned from model: distilbert-base-uncased

Model Sources

• Repository: Hugging Face Model Hub - d-80-vulcan (link to be added after upload)
• Base Model: DistilBERT

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Uses

Direct Use

This model can be used to classify customer queries into various e-commerce intents such as:

• Product search (e.g., “Show me red sneakers under $50”)
• Order tracking (e.g., “Where is my package?”)
• Returns and refunds (e.g., “I want to return this item”)
• Payment issues (e.g., “My payment failed”)
• General queries (e.g., “Do you have discounts on laptops?”)

Developers can integrate the model directly into:

• Chatbots and virtual assistants
• Product recommendation systems
• Customer service automation pipelines

Downstream Use

It can also be fine-tuned further for domain-specific intent detection, e.g.:

• Fashion-focused e-commerce sites
• Grocery delivery platforms
• Travel and booking marketplaces

Out-of-Scope Use

• Not suitable for tasks outside intent classification.
• Not reliable for sensitive decision-making (e.g., financial risk assessment, medical queries).
• Performance may degrade with non-English inputs or ambiguous queries.

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Bias, Risks, and Limitations

• The model is trained on curated e-commerce data; performance on out-of-domain queries might be lower.
• May inherit biases present in the training data, e.g., certain product categories or language patterns.
• Handles short, intent-focused text best; long-form text may reduce accuracy.

Recommendations

• Always validate predictions for critical use cases.
• Periodically retrain with updated data to reduce domain drift.
• Use alongside fallback rule-based systems for edge cases.

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How to Get Started with the Model

from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline

model_name = "your-username/d-80-vulcan"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

nlp = pipeline("text-classification", model=model, tokenizer=tokenizer)

query = "I want to cancel my order"
result = nlp(query)
print(result)