---
license: apache-2.0
datasets:
- McAuley-Lab/Amazon-Reviews-2023
language:
- en
metrics:
- accuracy
base_model:
- microsoft/deberta-v3-base
pipeline_tag: text-classification
widget:
  - text: The product didn't arrive on time and was damaged.
library_name: transformers
safetensors: true
---

### 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)