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--- |
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license: apache-2.0 |
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datasets: |
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- McAuley-Lab/Amazon-Reviews-2023 |
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language: |
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- en |
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metrics: |
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- accuracy |
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base_model: |
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- microsoft/deberta-v3-base |
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pipeline_tag: text-classification |
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widget: |
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- text: The product didn't arrive on time and was damaged. |
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library_name: transformers |
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safetensors: true |
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--- |
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### sentiment_mapping = {1: "Negative", 0: "Positive"} |
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### Training Details |
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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. |
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### Training Hyperparameters |
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* Model: microsoft/deberta-v3-base |
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* Learning Rate: 3e-5 |
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* Epochs: 6 |
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* Train Batch Size: 16 |
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* Gradient Accumulation Steps: 2 |
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* Weight Decay: 0.015 |
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* Warm-up Ratio: 0.1 |
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### Evaluation |
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The model was evaluated using a subset of the Amazon reviews dataset, focusing on the binary classification of text as either positive or negative. |
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### Metrics |
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Accuracy: 0.98 |
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Precision: 0.98 |
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Recall: 0.99 |
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F1-Score: 0.98 |
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```python |
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from transformers import pipeline |
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classifier = pipeline("text-classification", model="dnzblgn/Sentiment-Analysis-Customer-Reviews") |
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result = classifier("The product didn't arrive on time and was damaged.") |
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print(result) |
