tweet-topic-21-multi
Maintainer: cardiffnlp
62
📶
| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
The tweet-topic-21-multi model is a multilingual language model based on the TimeLMs architecture. It was trained on 124 million tweets from January 2018 to December 2021 and fine-tuned for multi-label topic classification on a corpus of 11,267 tweets. The model is suitable for English text and can classify tweets into 19 different topics, including arts & culture, business, celebrity & pop culture, diaries & daily life, family, fashion & style, film/TV & video, fitness & health, food & dining, gaming, learning & educational, music, news & social concern, relationships, science & technology, sports, travel & adventure, and youth & student life.
Model inputs and outputs
Inputs
- English text, such as tweets or short social media posts
Outputs
- A list of topics that the input text is classified as, with each topic represented as a binary 0/1 value indicating whether the text belongs to that topic or not.
Capabilities
The tweet-topic-21-multi model is capable of accurately classifying short English text into multiple relevant topics. For example, the input text "It is great to see athletes promoting awareness for climate change." would be classified as belonging to the "news & social concern" and "sports" topics.
What can I use it for?
The tweet-topic-21-multi model can be used for a variety of applications, such as:
- Content Categorization: Automatically organizing and indexing large collections of social media posts, news articles, or other short-form text based on their topical content.
- Trend Analysis: Monitoring social media conversations to detect emerging trends and topics of interest.
- Personalization: Tailoring content recommendations or marketing messages based on a user's predicted interests and preferences.
Things to try
One interesting aspect of the tweet-topic-21-multi model is its ability to handle multi-label classification. This means that a single input text can be assigned to multiple topics simultaneously, reflecting the diverse and overlapping nature of real-world content. Researchers and developers could explore how this capability can be leveraged to build more sophisticated text understanding and analysis applications.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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