Bduenas/sentiate-sentiment-classifier

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

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()