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good-vibes

Text Classification
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albert
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metadata
license: other
license_name: link-attribution
license_link: https://dejanmarketing.com/link-attribution/
datasets:
  - dejanseo/good-vibes
widget:
  - example_title: Example 1
    text: >-
      The concert last night was an unforgettable experience filled with amazing
      performances.
  - example_title: Example 2
    text: >-
      I found the book to be quite insightful and it provided a lot of valuable
      information.
  - example_title: Example 3
    text: The weather today is pretty average, not too hot and not too cold.
  - example_title: Example 4
    text: >-
      Although the service was slow, the food at the restaurant was quite
      enjoyable.
  - example_title: Example 5
    text: The new software update has caused more problems than it fixed.
  - example_title: Example 6
    text: >-
      The customer support team was unhelpful and I had a frustrating
      experience.
  - example_title: Example 7
    text: I had a fantastic time exploring the city and discovering new places.
  - example_title: Example 8
    text: The meeting was very productive and we accomplished all our goals.
  - example_title: Example 9
    text: This is the worst purchase I've ever made and I regret buying it.
  - example_title: Example 10
    text: >-
      I am extremely pleased with the results of the project and how smoothly
      everything went.
language:
  - en
pipeline_tag: text-classification

Multi-label sentiment classification model developed by Dejan Marketing.

To see this model in action visit: Good Vibes 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.

Engage Our Team

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: "Good Vibes",
    1: "No Vibes",
    2: "Bad Vibes"
}

Sources of Training Data

Synthetic. Mistral.