| --- |
| 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. |
|
|
