| --- |
| language: |
| - en |
| license: mit |
| tags: |
| - text-classification |
| - sentiment-analysis |
| - distilbert |
| - amazon-reviews |
| - nlp |
| datasets: |
| - amazon_polarity |
| metrics: |
| - accuracy |
| - f1 |
| --- |
| # 🛍️ pranalyzer — Sentiment Analysis Model |
|
|
| Fine-tuned **DistilBERT** on Amazon product reviews for binary sentiment classification (POSITIVE / NEGATIVE). |
|
|
| Part of the [pranalyzer](https://github.com/Vedant-Nagarkar/product-review-analyzer) end-to-end NLP pipeline. |
|
|
| --- |
|
|
| ## 📊 Model Performance |
|
|
| | Metric | Score | |
| |---|---| |
| | Accuracy | 93.00% | |
| | F1 Score | 0.9299 | |
| | Loss | 0.1923 | |
|
|
| - **Dataset**: `amazon_polarity` (5,000 train / 1,000 test samples) |
| - **Hardware**: T4 GPU (Google Colab) |
| - **Epochs**: 3 |
| - **Batch size**: 32 |
| - **Learning rate**: 2e-5 |
|
|
| --- |
|
|
| ## 🚀 Quick Start |
| ```python |
| from transformers import pipeline |
| |
| classifier = pipeline( |
| "text-classification", |
| model="Ved2001/pranalyzer" |
| ) |
| |
| result = classifier("This product is absolutely amazing!") |
| print(result) |
| # [{'label': 'POSITIVE', 'score': 0.98}] |
| ``` |
|
|
| --- |
|
|
| ## 🏋️ Training Details |
|
|
| **Base model**: `distilbert-base-uncased` |
| **Task**: Binary sentiment classification |
| **Labels**: `NEGATIVE (0)`, `POSITIVE (1)` |
| **Dataset**: Amazon Polarity — 3.6M reviews (sampled 5K for fine-tuning) |
|
|
| --- |
|
|
| ## 🔍 Part of pranalyzer Pipeline |
|
|
| This model is the sentiment component of a 4-model pipeline: |
|
|
| | Task | Model | |
| |---|---| |
| | **Sentiment** | `Ved2001/pranalyzer` (this model) | |
| | Category | `facebook/bart-large-mnli` | |
| | Aspects | `cross-encoder/nli-roberta-base` | |
| | Summary | `facebook/bart-large-xsum` | |
|
|
| --- |
|
|
| ## 👤 Author |
|
|
| **Vedant Nagarkar** |
| [GitHub](https://github.com/Vedant-Nagarkar) • [HuggingFace](https://huggingface.co/Ved2001) |
