Modelpublic

dnzblgn/Sentiment-Analysis-Customer-Reviews

sourceHugging Faceapache-2.0updated 2y agoView on Hugging Face

3likes22downloads

Model Card

sentiment_mapping = {1: "Negative", 0: "Positive"}

Training Details

The model was trained on the McAuley-Lab/Amazon-Reviews-2023 dataset. This dataset contains labeled customer reviews from Amazon, focusing on two primary categories: Positive and Negative.

Training Hyperparameters

—Model: microsoft/deberta-v3-base
—Learning Rate: 3e-5
—Epochs: 6
—Train Batch Size: 16
—Gradient Accumulation Steps: 2
—Weight Decay: 0.015
—Warm-up Ratio: 0.1

Evaluation

The model was evaluated using a subset of the Amazon reviews dataset, focusing on the binary classification of text as either positive or negative.

Metrics

Accuracy: 0.98

Precision: 0.98

Recall: 0.99

F1-Score: 0.98

python

from transformers import pipeline

classifier = pipeline("text-classification", model="dnzblgn/Sentiment-Analysis-Customer-Reviews")
result = classifier("The product didn't arrive on time and was damaged.")
print(result)