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)
- Downloads last month
- 54
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.
Model tree for dnzblgn/Sentiment-Analysis-Customer-Reviews
Base model
microsoft/deberta-v3-base