license: bigscience-openrail-m
pipeline_tag: text-classification
widget:
- example_title: Very Positive
text: This product is absolutely amazing!
- example_title: Positive
text: I like the features of this app.
- example_title: Somewhat Positive
text: The service is pretty good.
- example_title: Neutral
text: It meets my expectations.
- example_title: Somewhat Negative
text: The experience was not as bad.
- example_title: Negative
text: I'm disappointed with the quality.
- example_title: Very Negative
text: This is the worst purchase I've ever made.
- example_title: Very Positive
text: >-
I recently bought a new sports jersey from this store, and it's absolutely
amazing! The fabric is high quality, the fit is perfect, and the design
looks even better in person. I'm thrilled with my purchase and highly
recommend this store to anyone looking for custom sports apparel.
- example_title: Positive
text: >-
I tried out the new fitness app, and I like its features. It has a
user-friendly interface, plenty of workout plans, and tracks my progress
effectively. It has definitely added value to my fitness routine, and I
find it motivating to use daily.
- example_title: Somewhat Positive
text: >-
The customer service at this cafe was pretty good. They were polite and
attended to my needs promptly. The coffee tasted nice, and the atmosphere
was cozy. I enjoyed my visit and would consider coming back, although
there is room for improvement in the menu variety.
- example_title: Neutral
text: >-
The smartphone I purchased meets my expectations. It has all the features
I need and performs adequately. The battery life is decent, and the camera
quality is average. Overall, it serves its purpose well without any
standout qualities or significant drawbacks.
- example_title: Somewhat Negative
text: >-
The hotel stay was not as bad as I had feared, but there were some issues.
The room was clean, but the service was slow, and the amenities were
limited. It wasn't a terrible experience, but I expected more for the
price I paid.
- example_title: Negative
text: >-
I'm disappointed with the quality of this product. It feels flimsy and
cheaply made. After only a few uses, it started showing signs of wear and
tear. I expected better durability and performance based on the reviews. I
wouldn't recommend this to others.
- example_title: Very Negative
text: >-
This is the worst purchase I've ever made. The item arrived broken, and
the customer service was unresponsive. It was a complete waste of money,
and I regret buying it. The overall experience was frustrating and
disappointing. Avoid this product at all costs.
Multi-label sentiment classification model developed by Dejan Marketing.
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 query processing?
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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.