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--- |
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license: bigscience-openrail-m |
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pipeline_tag: text-classification |
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base_model: albert-base-v2 |
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widget: |
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- example_title: Example 1 |
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text: >- |
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The concert last night was an unforgettable experience filled with amazing |
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performances. |
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- example_title: Example 2 |
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text: >- |
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I found the book to be quite insightful and it provided a lot of valuable |
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information. |
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- example_title: Example 3 |
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text: The weather today is pretty average, not too hot and not too cold. |
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- example_title: Example 4 |
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text: >- |
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Although the service was slow, the food at the restaurant was quite |
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enjoyable. |
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- example_title: Example 5 |
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text: The new software update has caused more problems than it fixed. |
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- example_title: Example 6 |
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text: The customer support team was unhelpful and I had a frustrating experience. |
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- example_title: Example 7 |
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text: I had a fantastic time exploring the city and discovering new places. |
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- example_title: Example 8 |
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text: The meeting was very productive and we accomplished all our goals. |
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- example_title: Example 9 |
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text: This is the worst purchase I've ever made and I regret buying it. |
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- example_title: Example 10 |
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text: >- |
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I am extremely pleased with the results of the project and how smoothly |
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everything went. |
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language: |
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- en |
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datasets: |
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- dejanseo/sentiment |
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spaces: |
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- dejanseo/sentiment |
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--- |
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Multi-label sentiment classification model developed by [Dejan Marketing](https://dejanmarketing.com/). |
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To see this model in action visit: [Sentiment Tool](https://dejanmarketing.com/tools/sentiment/) |
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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. |
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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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# Engage Our Team |
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Interested in using this in an automated pipeline for bulk URL and text processing? |
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Please [book an appointment](https://dejanmarketing.com/conference/) to discuss your needs. |
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# Base Model |
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albert/albert-base-v2 |
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## Labels |
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```py |
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sentiment_labels = { |
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0: "very positive", |
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1: "positive", |
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2: "somewhat positive", |
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3: "neutral", |
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4: "somewhat negative", |
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5: "negative", |
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6: "very negative" |
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} |
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``` |
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# Sources of Training Data |
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Synthetic. Llama3. |
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