Model Experimentation For Text Classification

Purpose Of Experiment - To understand how RNN Architecture helps in classification task of temporal data like Text.

Experiment done in Month - August

Models-

1. Model0 - Basic DAN kind of structure
2. Model1 - Modified Model0 architecture to use RNN
3. Model2 - Use packed padded sequence to avoid giving padded word vectors as output.

Dataset-

1. Data0 -
   i. Train.neg - Contain Negative sentiments
   ii. Train.pos - Contain Positive Sentiments
   iii. TestData - Contain Test File having first half positive and second half negative sentiments
2. Data1 -
   Imdb Dataset divided into training and testing dataset.

Commands-

usage:
python main.py
[--batchsize BATCHSIZE]
[--seqlen SEQLEN]
[--glovepath GLOVEPATH]
[--dnum DNUM]
[--cuda_num CUDA_NUM]
[--epochs EPOCHS]
[--model MODEL]
[--patience PATIENCE]

arguments:
-h, --help          show this help message and exit
--batchsize         Batch Size
--seqlen            Seq len of each word vector after padding and truncating
--glovepath         Path to Glove Embedding file
--dnum              Dataset are numbered. This take which dataset I want to use
--cuda_num          device number
--epochs            Number of Epochs
--model             Models are numbered. Select the appropriate model number.
--patience          How much you want to wait before early stopping