Recurrent NN:

Implement a more complex Siamese neural network that is composed of the following layers:

An embedding layer that generates embedding vectors of the sentence text with $35$ dimensions.

A LSTM layer. You need to determine the size of this LSTM layer, and the text length limit $($if needed$).$

$3$ hidden layers and a relu activation function. You need to determine the size of the hidden layers.

Train the model with the training data, use the dev$\_$test set to determine a good size of the LSTM layer and an appropriate length limit $($if needed$),$ and report the final results using the test set. Again, remember to use the test set only after you have determined the optimal parameters of the LSTM layer.

Based on your experiments, comment on whether this system is better than the systems developed in the previous tasks.

Important Points:

The NN model has the correct layers, the correct activation functions, and the correct loss function.

The code passes the sentence text to the model correctly. The documentation needs to explain what decisions had to be made to process long sentences. In particular, did you need to truncate the input text, and how did you determine the length limit$?$

The code returns the IDs of the n sentences that have the highest prediction score in the given question.

The notebook reports the F$1$ scores of the test sets and comments on the results.

For good coding and documentation in this task. In particular, the code and results must include evidence that shows your choice of best size of the LSTM layer $($and length limit$)$ and hidden layers. The explanations must be clear and concise. To make this task less time$-$consuming, use n$=1.$