| | --- |
| | license: apache-2.0 |
| | language: en |
| | tags: |
| | - sentiment |
| | - roberta |
| | - amazon |
| | - 4-class |
| | - text-classification |
| | datasets: |
| | - amazon_polarity |
| | metrics: |
| | - accuracy |
| | - f1 |
| | pipeline_tag: text-classification |
| | base_model: roberta-base |
| | --- |
| | |
| | # Sentiate: Amazon Review Sentiment Classifier (4-Class, RoBERTa) |
| |
|
| | `sentiate-sentiment-classifier` is a fine-tuned RoBERTa model built to classify **Amazon Electronics product reviews** into one of **four sentiment classes**: |
| |
|
| | - **0 — Low Sentiment** (strongly negative) |
| | - **1 — Medium-Low** (somewhat negative/mixed) |
| | - **2 — Medium-High** (somewhat positive) |
| | - **3 — High Sentiment** (strongly positive) |
| |
|
| | ## 🔍 Use Cases |
| | - eCommerce product research |
| | - Dropshipping product analysis |
| | - Brand sentiment tracking |
| | - Batch review scoring at scale |
| |
|
| | ## 🧠 Model Details |
| | - Base: `roberta-base` |
| | - Trained on: 394,000 Amazon Electronics reviews |
| | - Framework: Hugging Face Transformers |
| | - Classes: 4-class multi-class sentiment |
| | - Evaluation Accuracy: ~81.9% |
| | - F1 Score: ~0.80 (weighted) |
| |
|
| | ## 🚀 How to Use |
| |
|
| | ```python |
| | from transformers import AutoTokenizer, AutoModelForSequenceClassification |
| | |
| | model = AutoModelForSequenceClassification.from_pretrained("your-username/sentiate-sentiment-classifier") |
| | tokenizer = AutoTokenizer.from_pretrained("your-username/sentiate-sentiment-classifier") |
| | |
| | text = "This charger broke after one week. I'm disappointed." |
| | inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True) |
| | outputs = model(**inputs) |
| | sentiment = outputs.logits.argmax().item() |
| | |
