/LSTM-Classification-Pytorch

Text classification based on LSTM on R8 dataset for pytorch implementation

Primary LanguagePython

Text Classification based on LSTM on R8 Dataset-[pytorch implementation]

1. Details of file fold:

  • data/
  • data/train_txt/*.txt
  • data/train_txt.txt
  • data/train_label.txt
  • data/test_txt/*.txt
  • data/test_txt.txt
  • data/test_label.txt

2. File description:

file description
data/train_txt/ training text fold
data/test_txt/ testing text fold
data/train_txt.txt file name list for training text
data/test_txt.txt file name list for testing text
data/train_label.txt label list for training text
data/test_label.txt label list for testing text

3. Running example:

Environment: python 3

Requirements:

pytorch

running example:

python main.py

output:

4. Dataset:

We use the following dataset for our example: R8 Dataset.link.

5. Notes:

  • This code is largely inspired from the original repo: jiangqy with a few modifications:
    • Use the Adam optimizer
    • Custom dataloader
    • Select the right output after the LSTM/GRU (masking)
    • Attention layer included, from here. The attention mechanism is not the full one though, it is a simplified version. No new parameters are learned in the process as it just combines the rnn output with the hidden states using matrix multiplications. This simplified attention mechanism still provides a significant improvements over no attention at all.
  • The entire sentences are used. If you want to cap the number of words, please, update the parameter sentence_len = None to sentence_len = n. You should get pretty descent results even with only 32 words max per sentence.
  • There is no early stopping. The code just runs for n epochs and saves the results at the end. But it is easy enough to select the best model based on the evaluation phase.