Adding multi class classification and tuning for dense and dropout la…#208
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nsriharshavardhan wants to merge 11 commits intoDataBytes-Organisation:developfrom
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Adding multi class classification and tuning for dense and dropout la…#208nsriharshavardhan wants to merge 11 commits intoDataBytes-Organisation:developfrom
nsriharshavardhan wants to merge 11 commits intoDataBytes-Organisation:developfrom
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…yers including bert model Signed-off-by: nsriharshavardhan <121825733+nsriharshavardhan@users.noreply.github.com>
Signed-off-by: nsriharshavardhan <121825733+nsriharshavardhan@users.noreply.github.com>
The last dataset being used did not represent the data or the sentiments that are precisely required in applying the sentiment analysis yielding lower accuracy. This new BERT model implemented using pytorch rectifies this problem and increases the accuracy to 78%. Signed-off-by: nsriharshavardhan <121825733+nsriharshavardhan@users.noreply.github.com>
Signed-off-by: nsriharshavardhan <121825733+nsriharshavardhan@users.noreply.github.com>
… files, cleaned and documented Signed-off-by: nsriharshavardhan <121825733+nsriharshavardhan@users.noreply.github.com>
Signed-off-by: nsriharshavardhan <121825733+nsriharshavardhan@users.noreply.github.com>
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Samruddhi-ds
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Samruddhi-ds
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Hi,
You have an error while implementing BERT. Are you planning to resolve it and submit?
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Changed the sentiments from binary bad, good to multiclass bad, good and neutral adjusted the bidirectional LSTM model accordingly. Added hyperparameter tuning to the Dense and Dropout layers of the model and added couple of extra layers. Also tried BERT model at the end but the accuracy was not satisfactory and the model takes a long time to run.