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# Predicting User Intents and Musical Attributes from Music Discovery Conversations
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Here is the implementation code and data for the paper titled **"Predicting User Intents and Musical Attributes from Music Discovery Conversations"** by Daeyong Kwon, SeungHeon Doh, Juhan Nam, 2024
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<b>Figure 1: Examples of user intents and musical attributes classifcation</b>
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## Setting
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The packages and version information required for the implementation are stored in the **requirements.txt** file.
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## Implementation
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Sparse representation, Word Embedding (Word2Vec), DistilBERT_Probing, DistilBERT_Finetune, and Llama are each implemented in separate .py files. The **functions.py** file should be placed in the same directory to import functions.
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Each .py file can be executed by running ```python filename.py```, and the resulting .csv files will be saved in the **"./results"** directory.
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For the concatenated setting, you can use ```concat_history``` function in **functions.py**.
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## Open Source Material
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- [Models](https://huggingface.co/Daeyongkwon98/Music_Conversation_Intent_Classifier/tree/main/models)
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- intent-classification
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- [Github Repository](https://github.com/DaeyongKwon98/Intent-Classification)
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# Predicting User Intents and Musical Attributes from Music Discovery Conversations
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Here is the implementation code and data for the paper titled **"Predicting User Intents and Musical Attributes from Music Discovery Conversations"** by Daeyong Kwon, SeungHeon Doh, Juhan Nam, 2024
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<b>Figure 1: Examples of user intents and musical attributes classifcation</b>
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</p>
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## Open Source Material
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- [Models](https://huggingface.co/Daeyongkwon98/Music_Conversation_Intent_Classifier/tree/main/models)
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