Hmm isn’t that strange?..I mean I have a training iterator that has batches of different shape. First batch has shape [32, 13], next batch has shape [32, 7], next batch has shape [32,11] etc. So based on the solutions I followed that use rnn.pack_padded_sequence(), the packed_embedded was a tensor with [32x11,100], [32x13,100], [32x7, 100]…etc So, I have used the .mean() method to bring the tensor dimensions down to 32x1 (averaged). This is the solution I followed. Do you know any alternative solution to apply multi-class text classification with my own embeddings? Would appreciate your help.