---
license: bigscience-openrail-m
pipeline_tag: text-classification
base_model: albert-base-v2
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
datasets:
- dejanseo/sentiment
spaces:
- dejanseo/sentiment
---
Multi-label sentiment classification model developed by [Dejan Marketing](https://dejanmarketing.com/).

To see this model in action visit: [Sentiment Tool](https://dejanmarketing.com/tools/sentiment/)

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: "very positive",
    1: "positive",
    2: "somewhat positive",
    3: "neutral",
    4: "somewhat negative",
    5: "negative",
    6: "very negative"
}
```
# Sources of Training Data

Synthetic. Llama3.