Hi, I am wondering how I could perform mean pooling within a given range of word embeddings.
For example, I have a list of word embeddings with length 100. I would like to perform a mean averaging on the 0-35 words and 36-100 words individually. How could I perform this given this list and an index list [0, 35, 100]?
Will be appreciating for any help…
The current way I have done is to:
def create_sentence_embedding(self, encoder_states, sentence_indices, sentence_index_num):
xxs=[torch.div(torch.sum(encoder_states[sentence_index:sentence_indices[i+1]], dim=0), len(encoder_states[sentence_index:sentence_indices[i+1]])) for i, sentence_index in enumerate(sentence_indices[:-1])]
xxs = [self.create_sentence_embedding(encoder_state, yys[i], sentence_index_num[i]) for i, encoder_state in enumerate(encoder_states)]
xxs = rnn_utils.pad_sequence(xxs, batch_first=True)
# pass xxs into a sentence transformer
xxs, mask, seq_len = self.sentence_encoder(xxs)
# pass xxs into a feed-forward position-wise network
xxs = self.sentence_ordering_decoder(xxs)
However, after doing this, my network weight does not update… So I think there is some problem with the way I used above.