Yanekyuk
Models by this creator
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bert-uncased-keyword-extractor
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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-extractor model 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.
Updated 9/6/2024
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bert-keyword-extractor
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The bert-keyword-extractor model is a fine-tuned version of the bert-base-cased model that has been trained on an unknown dataset. It achieves strong performance on the evaluation set, with a loss of 0.1341, precision of 0.8565, recall of 0.8874, accuracy of 0.9738, and an F1 score of 0.8717. This model is similar to the bert-uncased-keyword-extractor model, which is a fine-tuned version of the bert-base-uncased model. Model inputs and outputs The bert-keyword-extractor model takes text as input and outputs keywords or key phrases extracted from the text. Inputs Text data Outputs Keyword/key phrase extractions from the input text Capabilities The bert-keyword-extractor model is capable of accurately extracting relevant keywords and key phrases from text. This could be useful for tasks like content summarization, search relevance, and document categorization. What can I use it for? The bert-keyword-extractor model could be used in a variety of applications that require keyword or key phrase extraction, such as: Powering a search engine to improve query relevance Automatically summarizing the content of documents or articles Categorizing text-based content into relevant topics or themes Things to try You could try using the bert-keyword-extractor model to extract keywords from a variety of text sources, such as news articles, blog posts, or product descriptions. This could provide valuable insights for content analysis, topic modeling, or search engine optimization.
Updated 9/6/2024