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@@ -10,4 +10,6 @@ This dataset was made after reviewing multiple finetuned models ranging from 135
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  Now, I have standardised every input type to have the same number of rows for (hopefully) less biased training and outputting values more accurately. Since the BGL dataset only have 150 rows, its the main limitting factor, so I had to set the dataset limit to 150 rows for the rest, which resulted in 750 rows in total.
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- One thing I have to mention, 4/5 of the dataset is classified under advertisement tag, so I'm going to expect the model to be more biased to responding the input_type as advertisement more often. If this hypothesis is true, then I will have to increase the chat data by 300 to balance out the classification data ratio (v0.1.1)
 
 
 
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  Now, I have standardised every input type to have the same number of rows for (hopefully) less biased training and outputting values more accurately. Since the BGL dataset only have 150 rows, its the main limitting factor, so I had to set the dataset limit to 150 rows for the rest, which resulted in 750 rows in total.
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+ One thing I have to mention, 4/5 of the dataset is classified under advertisement tag, so I'm going to expect the model to be more biased to responding the input_type as advertisement more often. If this hypothesis is true, then I will have to increase the chat data by 300 to balance out the classification data ratio (v0.1.1)
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+ Also, I noticed that WL currency is denoted as a discord emoji with custom ID, which can affect model output quality post-training, so for next dataset iteration (v0.1.1) I have to replace the discord emoji for WL e.g. <:WL:1234> to just WL