copypasta-data

I provide scripts to scrape copypastas from:

• https://twitch.gimu.org/
• https://www.twitchquotes.com/copypastas
• https://www.reddit.com/r/copypasta/top/?sort=top&t=all

as well as their output.

They print the copypastas written with only ASCII characters to limit the dimension of the dataset.

For an unknown reason, lxml cannot parse very well the html from twitchquotes.com, and it is very slow with html5lib, so the script takes about 2 minutes. twitch.gimu.org is scraped in 15 seconds thanks to parallel download. I could not find more than 39 pages of /r/copypasta, they were downloaded in less than 3 minutes.

I also provide their output as it was generated today with

./twitch_copypasta_async.py > copypasta.txt
./twitchquotes.py > copypasta2.txt
./copypasta_reddit.py -1 > reddit.txt

LSTM for copypasta generation

I modified the keras example lstm_text_generation.py available here to run on our dataset.

I use two layers of Bidirectional GRU (Gated Recurrent Units) instead of LSTM, with .5 Dropout layers between them. Here is the summary:

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_1 (InputLayer)         (None, 40, 92)            0         
_________________________________________________________________
bidirectional_1 (Bidirection (None, 40, 256)           169728    
_________________________________________________________________
dropout_1 (Dropout)          (None, 40, 256)           0         
_________________________________________________________________
bidirectional_2 (Bidirection (None, 128)               123264    
_________________________________________________________________
dropout_2 (Dropout)          (None, 128)               0         
_________________________________________________________________
dense_1 (Dense)              (None, 128)               16512     
_________________________________________________________________
dropout_3 (Dropout)          (None, 128)               0         
_________________________________________________________________
dense_2 (Dense)              (None, 92)                11868     
_________________________________________________________________
activation_1 (Activation)    (None, 92)                0         
=================================================================
Total params: 321,372
Trainable params: 321,372
Non-trainable params: 0
_________________________________________________________________

If you have keras installed, just run ./copypasta.py

References

• Andrej Karpathy, The Unreasonable Effectiveness of Recurrent Neural Networks

• Alex Graves, Generating Sequences With Recurrent Neural Networks