Sequence Model Build —LSTM or Transformer for Time Series & Text

I will build a sequence model for your time series prediction or text sequence task — implementing LSTM, GRU, or a lightweight Transformer architecture depending on your sequence length and dataset size, with appropriate sequence preprocessing, padding and masking, training with teacher forcing where applicable, and evaluation appropriate to your task type. Sequence tasks require more careful data preparation than tabular tasks — sequence length determination, windowing strategy for time series, and masking for variable-length sequences are each decisions with material impact on model quality that most deep learning tutorials skip over entirely.

The build covers sequence preprocessing (windowing for time series, tokenisation for text), LSTM/GRU or Transformer architecture implementation, padding and attention masking, training loop with gradient clipping, evaluation metrics appropriate to your task (RMSE for regression, F1 for classification), and a prediction function for new sequences.