andyfe/siena-sentiment

Siena Sentiment Analysis Model

This model is a fine-tuned version of distilbert-base-uncased for sentiment analysis on customer support tickets, capable of classifying text into five sentiment categories.

Model Description

• Model Architecture: DistilBERT (66M parameters)
• Task: Multi-class Sentiment Classification
• Language: English
• Training Data: 5,000 synthetic customer support tickets generated using GPT-4
• Input: Customer support tickets or similar text (50-200 words)
• Output: Sentiment classification into one of five categories:
  • Strong Negative (0)
  • Mild Negative (1)
  • Neutral (2)
  • Mild Positive (3)
  • Strong Positive (4)

Limitations

• Model is trained on synthetic data, which may not capture all real-world nuances
• Best suited for customer support context; may not generalize well to other domains
• Input text should be between 50-200 words for optimal performance

Training Procedure

Training Data

The model was trained on 5,000 synthetic customer support tickets:

• 1,000 samples per sentiment category
• Generated using GPT-4o-mini for balanced representation
• Text length between 50-200 words
• Focus on product and service-related issues

Training Hyperparameters

• Optimizer: AdamW
• Learning rate: 2e-5
• Batch size: 16
• Training epochs: 3
• Max sequence length: 128 tokens

How to Use

Here's how to use the model with the Transformers library:

from transformers import pipeline

# Load the sentiment analysis pipeline
classifier = pipeline("text-classification", model="andyfe/siena-sentiment")

# Example text
text = """I am extremely disappointed with the customer service I received today. I've been waiting for a response for over a week, and when I finally got one, it didn't address my issue at all. This is unacceptable."""

# Get prediction
result = classifier(text)
print(result)

For more detailed usage with the model directly:

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("andyfe/siena-sentiment")
model = AutoModelForSequenceClassification.from_pretrained("andyfe/siena-sentiment")

# Prepare input text
text = "Your customer service team was incredibly helpful and resolved my issue quickly!"
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=128)

# Get prediction
outputs = model(**inputs)
predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
predicted_label = torch.argmax(predictions).item()

# Map prediction to sentiment label
id2label = {
    0: "Strong Negative",
    1: "Mild Negative",
    2: "Neutral",
    3: "Mild Positive",
    4: "Strong Positive"
}

print(f"Predicted sentiment: {id2label[predicted_label]}")