thomwolf

# 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 torch.utils.data import DataLoader
from ignite.engine import Engine, Events

bvasiles

%% Saved with string encoding Unicode (UTF-8) 

@inproceedings{Gkotsis2014Content,
	title={It's all in the content: state of the art best answer prediction based on discretisation of shallow linguistic features},
	author={Gkotsis, George and Stepanyan, Karen and Pedrinaci, Carlos and Domingue, John and Liakata, Maria},
	booktitle={Proceedings of the 2014 ACM Conference on Web Science (WebSci)},
	pages={202--210},
	year={2014},
	organization={ACM}
}

abelsonlive

# 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