Amazon Reviews Sentiment Distilbert Base Uncased

🚀 Amazon-Beauty-Product-Reviews-distilBERT-base for Sentiment Analysis

This model is fine-tuned on Amazon beauty product reviews for sentiment analysis, offering high accuracy and performance.

🚀 Quick Start

This section is not provided in the original README, so it is skipped.

✨ Features

This model can accurately perform sentiment analysis on Amazon beauty product reviews, with good performance in terms of accuracy, precision, recall, and F1 score.

📚 Documentation

🔍 Model Details

Model Description

This model is a fine-tuned version of distilbert-base-uncased on a balanced subset of Amazon beauty reviews dataset.

It achieves the following results on the evaluation set:

• Loss: 0.5171
• Accuracy: 0.7862
• Precision: 0.7876
• Recall: 0.7860
• F1: 0.7867

Developer Information

💡 Uses

Direct Application

This model can be used for sentiment analysis on Amazon beauty product reviews.

Misuse and Out-of-scope Use

⚠️ Important Note

The model should not be used to create hostile or alienating environments for people intentionally. In addition, the model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.

⚠️ Risks, Limitations and Biases

The model may produce biased predictions, particularly impacting underrepresented groups.

Users should evaluate the model’s risks for their specific use cases.

For further bias evaluation, consider datasets such as:

• WinoBias
• WinoGender
• Stereoset.

📈 Training and Evaluation

Training Data

The author uses the Amazon beauty reviews dataset, which has been balanced to address class imbalance issues.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 0
• optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 1

Training results

For detailed training logs, please refer to the Tensorboard page.

Evaluation Results

The fine-tuned DistilBERT model was evaluated on a dataset with the following splits:

• Training Samples: 133,665
• Validation Samples: 33,417

The evaluation was conducted on a three-class sentiment classification task. Below are the detailed results:

Classification Report

Confusion Matrix

Framework versions

• Transformers 4.50.3
• Pytorch 2.6.0+cu124
• Tokenizers 0.21.1

📄 License

This model is released under the Apache-2.0 license.