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
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)
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