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good-vibes

Text Classification
Transformers
Safetensors
English
albert
Inference Endpoints
Model card Files Community
good-vibes
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---
license: other
license_name: link-attribution
license_link: https://dejanmarketing.com/link-attribution/
base_model: albert-base-v2

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](https://dejanmarketing.com/).

To see this model in action visit: [Good Vibes Tool](https://dejanmarketing.com/tools/good-vibes/)

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](https://dejanmarketing.com/conference/) to discuss your needs.

# Base Model

albert/albert-base-v2

## Labels
```py
sentiment_labels = {
    0: "Good Vibes",
    1: "No Vibes",
    2: "Bad Vibes"
}
```
# Sources of Training Data

Synthetic. Mistral.