Model Card for Model ID
This is a fine-tuned BERT-base-uncased model for sentiment analysis on Amazon reviews. The model classifies reviews as positive or negative based on their textual content. It was fine-tuned using 10,000 training samples and evaluated on 5,000 test samples from the Amazon Polarity dataset.
Model Details
Model Description
This is the model card for BERT-Amazon-Sentiment, a fine-tuned BERT-base-uncased model trained for binary sentiment classification on Amazon reviews. The model was fine-tuned using a subset of the Amazon Polarity dataset (10K training samples, 5K test samples) and can classify reviews as either positive or negative.
Developed by: Usman Ahmad
Funded by: Self-funded
Shared by: Usman Ahmad
Model type: Transformer-based BERT model for text classification
Language(s) (NLP): English
License: MIT License
Fine-tuned from model: bert-base-uncased
Model Sources [optional]
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Uses
Direct Use
This model can be used directly for sentiment analysis on English text, particularly for Amazon product reviews. It classifies text into positive or negative sentiment without any additional fine-tuning.
Downstream Use [optional]
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Out-of-Scope Use
This model is not suitable for:
Non-English text – It was trained only on English Amazon reviews.
Neutral sentiment detection – The model only classifies reviews as positive or negative.
Sarcasm and complex sentiment – May struggle with sarcastic or mixed-tone reviews.
Legal or medical advice – Not designed for professional or sensitive domains.
Hate speech detection – Not fine-tuned for offensive or harmful content moderation.
Misuse of this model for tasks outside its intended scope may lead to inaccurate results.
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Bias, Risks, and Limitations
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Recommendations
Bias Awareness – The model is trained on Amazon reviews, so it may not generalize well to other platforms or domains.
Domain Adaptation – Fine-tuning on domain-specific data (e.g., medical, legal) is recommended for better performance.
Handling Sarcasm & Mixed Sentiment – Additional preprocessing or ensemble methods may be needed to improve detection.
Use with Confidence Scores – Always consider model confidence scores to assess prediction reliability.
Ethical Use – Avoid using the model for critical decisions without human oversight
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
Load the fine-tuned model from Hugging Face
sentiment_pipeline = pipeline("text-classification", model="Asteroid-Destroyer/bert-amazon-sentiment")
Example review
review = "The product quality is amazing, and I love using it!"
result = sentiment_pipeline(review)
print(result)
Output: [{'label': 'POSITIVE', 'score': 0.98}]
Training Details
Training Data
The model was fine-tuned on a 10K subset of the Amazon Polarity dataset for training and a 5K subset for testing. The dataset consists of Amazon product reviews labeled as positive (1) or negative (0).
Dataset Source: Amazon Polarity
https://huggingface.co/datasets/fancyzhx/amazon_polarity on Hugging Face
Preprocessing:
Tokenized using BERT tokenizer (bert-base-uncased).
Max sequence length: 512 (truncated if longer).
Padding: Applied to match model input size.
Training Procedure
The model was fine-tuned using bert-base-uncased on a 10K subset of the Amazon Polarity dataset with the following setup:
Optimizer: AdamW
Learning Rate Scheduler: Linear decay with warm-up
Loss Function: CrossEntropyLoss
Batch Size: 16
Max Sequence Length: 512
Epochs: 3
Hardware Used: Google Colab with T4 GPU
Preprocessing [optional]
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Training Hyperparameters
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Speeds, Sizes, Times [optional]
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Evaluation
Testing Data, Factors & Metrics
Testing Data
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Factors
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Results
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Summary
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Environmental Impact
Carbon emissions can be estimated using the
Machine Learning Impact calculator presented in
Lacoste et al. (2019).
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Technical Specifications [optional]
Model Architecture and Objective
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