Multi-label sentiment classification model developed by Dejan Marketing.

To see this model in action visit: Sentiment Tool

The model is designed to be deployed in an automated pipeline capable of classifying text sentiment for thousands (or even millions) of text chunks or as a part of a scraping pipeline.

This is a demo model which may occassionally misclasify some texts. In a typical commercial project, a larger model is deployed for the task, and in special cases, a domain-specific model is developed for the client.

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Interested in using this in an automated pipeline for bulk URL and text processing?

Please book an appointment to discuss your needs.

Base Model

albert/albert-base-v2

Labels

sentiment_labels = {
    0: "very positive",
    1: "positive",
    2: "somewhat positive",
    3: "neutral",
    4: "somewhat negative",
    5: "negative",
    6: "very negative"
}

Sources of Training Data

Synthetic. Llama3.

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