| --- |
| library_name: transformers |
| license: apache-2.0 |
| datasets: |
| - amazon-sales-dataset |
| language: |
| - en |
| metrics: |
| - accuracy |
| - f1 |
| tags: |
| - text-classification |
| - sentiment-analysis |
| - ecommerce |
| - pytorch |
| - distilbert |
| --- |
| |
| # Model Card for Model ID |
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| <!-- Provide a quick summary of what the model is/does. --> |
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| ## Model Details |
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| ### Model Description |
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| This model is a fine-tuned version of distilbert-base-uncased on an Amazon product reviews dataset. It classifies customer reviews into two sentiment categories: |
| Negative (label 0): rating < 3.5 |
| Positive (label 1): rating ≥ 3.5 |
| The model is designed to support automated customer service systems by providing real-time sentiment analysis. |
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| - **Developed by:** Estella |
| - **Model type:** Text Classification |
| - **Language(s) (NLP):** English |
| - **License:** apache-2.0 |
| - **Number of Classes:** 2 |
| - 0: Negative |
| - 1: Positive |
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| ## Intended Uses & Limitations |
| **Intended Use:** |
| - Sentiment analysis for e-commerce customer reviews |
| - Pre-processing step for automated reply generation or customer feedback dashboard |
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| **Limitations:** |
| - The model was trained on product reviews from Amazon (electronics, cables, TVs, etc.). Performance on other domains (e.g., clothing, books) may vary. |
| - It does not detect neutral sentiment; reviews with rating 3.5 are considered positive by the chosen threshold. |
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| ## How to Use the Model |
| You can use this model directly with the Transformers pipeline for text classification. |
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| ```python |
| from transformers import pipeline |
| |
| classifier = pipeline("text-classification", model="your_username/amazon-sentiment-distilbert") |
| result = classifier("This product is amazing!") |
| print(result) # [{'label': 'POSITIVE', 'score': 0.99}] |
