Model Card for qxswy/deberta-card-sentiment

A fine-tuned sentiment analysis model for classifying product reviews, particularly focused on board and card games, as either POSITIVE or NEGATIVE.

Model Details

Model Description

This model is a fine-tuned version of DeBERTa-v3-base that performs binary sentiment classification on product reviews, specifically optimized for board and card game reviews. It was developed as part of research on value proposition analysis through consumer reviews.

• Model type: Sentiment Analysis (Text Classification)
• Language(s) (NLP): English
• Finetuned from model: microsoft/deberta-v3-base

Model Sources

• Repository: https://huggingface.co/qxswy/deberta-card-sentiment

Uses

Direct Use

This model can be used directly for sentiment analysis of product reviews, particularly for board and card games. It provides binary classification (positive/negative) with high accuracy, making it suitable for product analysis, customer feedback processing, and market research.

Downstream Use

The model can be integrated into:

• Customer feedback analysis systems
• Product development feedback loops
• Recommendation systems
• Value proposition analysis frameworks
• E-commerce review filtering systems

Bias, Risks, and Limitations

• The model was trained primarily on Amazon reviews, which may not represent all consumer opinions
• Significant class imbalance in training data (more positive than negative reviews)
• May struggle with sarcasm, irony, or complex expressions of mixed sentiment
• Limited to English language content
• Performance may degrade for product categories outside board/card games
• Not suitable for sentiment analysis of specialized technical content

Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

from transformers import pipeline

sentiment_pipe = pipeline(
    "text-classification",
    model="qxswy/deberta-card-sentiment",
    tokenizer="qxswy/deberta-card-sentiment"
)

reviews = ["This game is absolutely hilarious!", 
           "I regret purchasing this, it wasn't fun at all."]
           
results = sentiment_pipe(reviews)
print(results)