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Deep-Q-Rank
sharmabhi/Deep-Q-Rank
Deep-Q-Rank is a machine learning model from sharmabhi. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
Learning to Rank is the problem involved with ranking a sequence of documents based on their relevance to a given query. Deep Q-Learning has been shown to be a useful method for training an agent in sequential decisio…
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Updated Sep 30, 2023
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At a glance
- License
- mit
- Access
- Public
- Created
- Sep 30, 2023
- Updated
- Sep 30, 2023
- SHA
- f6ddba87
Model card
From the Hugging Face model README
Deep-Q-Rank
<!-- Provide a quick summary of what the model is/does. -->Learning to Rank is the problem involved with ranking a sequence of documents based on their relevance to a given query. Deep Q-Learning has been shown to be a useful method for training an agent in sequential decision making.
DeepQRank, our deep q-learning to rank agent, demonstrates performance that can be considered state-of-the-art. Though less computationally efficient than a supervised learning approach such as linear regression, our agent has fewer limitations in terms of which format of data it can use for training and evaluation.
Model Details
Model Description
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Model Sources [optional]
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- Paper [https://arxiv.org/abs/2002.07651]: [More Information Needed]
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Uses
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Out-of-Scope Use
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Bias, Risks, and Limitations
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Recommendations
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How to Get Started with the Model
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Training Details
Training Data
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Training Procedure
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Training Hyperparameters
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Speeds, Sizes, Times [optional]
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Evaluation
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Testing Data
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Factors
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Metrics
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Results
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Summary
Model Examination [optional]
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Environmental Impact
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Technical Specifications [optional]
Model Architecture and Objective
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Citation [optional]
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- License
- mit
- Languages
- en
- Created
- Sep 30, 2023
- Updated
- Sep 30, 2023