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Update app.py
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app.py
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@@ -60,7 +60,7 @@ title = "Extract relevant features from Real Estate Descriptions!"
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description = """
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<div style="margin-bottom: 1.5rem;">
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<div style="display: flex; justify-content: center; margin-bottom: 1.5rem">
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<img src="https://teara.govt.nz/files/c-21363-atl.jpg" width="300px" alt="Real Estate Image" style="border-radius: 8px;"/>
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</div>
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<div style="text-align: justify">
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Features with missing instances can negatively impact the performance of machine learning models. Information Extraction (IE) can improve the availability of tabular data by identifying relevant information from unstructured textual descriptions. This project demonstrated the application of IE on descriptions of online real estate listings, whereby the required missing values are retrieved from the text. Inspired by question-answering tasks, the aim was to recover these values by asking a set of questions. We tested two ways to achieve this goal. The first one focuses on a model specific to the language of the description (French) to perform IE, while the second translates the descriptions into English before IE. The project compared the performance of both approaches while delivering insights on how the formulation of the questions can impact the effectiveness of Q&A models.
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description = """
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<div style="margin-bottom: 1.5rem;">
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<div style="display: flex; justify-content: center; margin-bottom: 1.5rem">
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<img src="https://teara.govt.nz/sites/default/files/c-21363-atl.jpg" width="300px" alt="Real Estate Image" style="border-radius: 8px;"/>
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</div>
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<div style="text-align: justify">
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Features with missing instances can negatively impact the performance of machine learning models. Information Extraction (IE) can improve the availability of tabular data by identifying relevant information from unstructured textual descriptions. This project demonstrated the application of IE on descriptions of online real estate listings, whereby the required missing values are retrieved from the text. Inspired by question-answering tasks, the aim was to recover these values by asking a set of questions. We tested two ways to achieve this goal. The first one focuses on a model specific to the language of the description (French) to perform IE, while the second translates the descriptions into English before IE. The project compared the performance of both approaches while delivering insights on how the formulation of the questions can impact the effectiveness of Q&A models.
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