bert-uncased-keyword-extractor
Maintainer: yanekyuk
44
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| 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 bert-uncased-keyword-extractor is a fine-tuned version of the bert-base-uncased model, developed by the maintainer yanekyuk. This model achieves strong performance on the evaluation set, with a loss of 0.1247, precision of 0.8547, recall of 0.8825, accuracy of 0.9741, and an F1 score of 0.8684.
Similar models include the finbert-tone-finetuned-finance-topic-classification model, which is a fine-tuned version of yiyanghkust/finbert-tone on the Twitter Financial News Topic dataset. It achieves an accuracy of 0.9106 and F1 score of 0.9106 on the evaluation set.
Model inputs and outputs
Inputs
- Text: The
bert-uncased-keyword-extractormodel takes in text as its input.
Outputs
- Keywords: The model outputs a set of keywords extracted from the input text.
Capabilities
The bert-uncased-keyword-extractor model is capable of extracting relevant keywords from text. This can be useful for tasks like content summarization, topic modeling, and document classification. By identifying the most important words and phrases in a piece of text, this model can help surface the key ideas and themes.
What can I use it for?
The bert-uncased-keyword-extractor model could be used in a variety of applications that involve processing and understanding text data. For example, it could be integrated into a content management system to automatically generate tags and metadata for articles and blog posts. It could also be used in a search engine to improve the relevance of search results by surfacing the most important terms in a user's query.
Things to try
One interesting thing to try with the bert-uncased-keyword-extractor model is to experiment with different types of text data beyond the original training domain. For example, you could see how well it performs on extracting keywords from scientific papers, social media posts, or creative writing. By testing the model's capabilities on a diverse range of text, you may uncover new insights or limitations that could inform future model development.
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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