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sajidrahman /
Created Jul 31, 2019 — forked from thomwolf/
A very small and self-contained gist to train a GPT-2 transformer model on wikitext-103
# Copyright (c) 2019-present, Thomas Wolf.
# All rights reserved. This source code is licensed under the MIT-style license.
""" A very small and self-contained gist to train a GPT-2 transformer model on wikitext-103 """
import os
from collections import namedtuple
from tqdm import tqdm
import torch
import torch.nn as nn
from import DataLoader
from ignite.engine import Engine, Events
sajidrahman / google-playstore-scraper.js
Created Jun 11, 2019
A sample code snippet to use google-play-scraper API with 'Promise' values.
View google-playstore-scraper.js
Author: Sajid
A sample code snippet to use google play scraper API
with Promise values.
Prerequisite: please install the following libraries first
before testing the code:
npm install franc
sajidrahman /
Last active Oct 17, 2018
How to access Tomcat Manager Web Interface

Tomcat users are defined in the file $TOMCAT_HOME/conf/tomcat-users.xml, by default, there is NO user, it means no one can access the Tomcat manager page.

To enable users to access the Tomcat manager page, add a user as the role manager-gui.

Step 0. Shutdown Tomcat if it is running. Stop Java process as well, just to be safe.

Step 1. Open tomcat-users.xml file, originally it should contain a commented out part for tomcat-users.

sajidrahman /
Created Apr 4, 2017
Read, cleanup, stem Stack Overflow posts from csv file
import java.util.HashSet;
import java.util.Scanner;
import java.util.Set;
sajidrahman / lda.R
Created Oct 30, 2016 — forked from abelsonlive/lda.R
topic modeling in R
View lda.R
# Brian Abelson @brianabelson
# Harmony Institute
# December 5, 2012
# lda is a wrapper for lda.collapsed.gibbs.sampler in the "lda" package
# it fits topic models using latent dirichlet allocation
# it provides arguments for cleaning the input text and tuning the parameters of the model
# it also returns alot of useful information about the topics/documents in a format that you can easily join back to your original data
# this allows you to easily model outcomes based on the distribution of topics within a collection of texts
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