Distilbert Base Uncased Finetuned Amazon Food Reviews

🚀 Amazon-Food-Reviews-distilBERT-base for Sentiment Analysis

This model is designed for sentiment analysis of Amazon food product reviews, leveraging the fine - tuned DistilBERT model to provide accurate sentiment classification.

🚀 Quick Start

This README provides a detailed introduction to the Amazon-Food-Reviews-distilBERT-base model for sentiment analysis, including model details, uses, risks, limitations, biases, and training information.

📚 Documentation

🔍 Model Details

• Model Description: This model is a fine - tuned version of [distilbert - base - uncased](https://huggingface.co/distilbert - base - uncased) on this [Amazon food reviews dataset](https://huggingface.co/datasets/jhan21/amazon - food - reviews - dataset).
  • It achieves the following results on the evaluation set:
    • Loss: 0.08
    • Accuracy: 0.87
    • Precision: 0.71
    • Recall: 0.77
    • F1: 0.73
• Developed by: Jiali Han
• Model Type: Text Classification
• Language(s): English
• License: Apache - 2.0
• Parent Model: For more details about DistilBERT, please check out [this model card](https://huggingface.co/distilbert - base - uncased).
• Resources for more information:
  • Model Documentation
  • DistilBERT paper

💡 Uses

📌 Direct Use

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

⚠️ Misuse and Out - of - scope Use

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

Based on a few experimentations, we observed that this model could produce biased predictions that target underrepresented populations.

We strongly advise users to thoroughly probe these aspects of their usecases to evaluate this model's risks. We recommend looking at the following bias evaluation datasets as a place to start: WinoBias, WinoGender, Stereoset.

🔧 Training

📊 Training Data

The author uses the [Amazon food reviews dataset](https://huggingface.co/datasets/jhan21/amazon - food - reviews - dataset) for the model.

⚙️ Fine - tuning hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e - 5
• train_batch_size: 8
• eval_batch_size: 8
• seed: 0
• optimizer: Adam with betas=(0.9,0.999) and epsilon = 1e - 08
• lr_scheduler_type: linear
• num_epochs: 5

📈 Training process

📦 Framework versions

• Transformers 4.35.2
• Pytorch 2.1.0+cu118
• Tokenizers 0.15.0

📄 License

This model is licensed under the Apache - 2.0 license.