Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement!
Modules can hold parameters of different types on different devices, so it's not always possible to unambiguously determine the device. The recommended workflow in PyTorch is to create the device object …
Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement!
nltk.util.ngrams nltk.bigrams nltk.PorterStemmer
from nltk.util import ngrams
sentence = 'this is a foo bar sentences and i want to ngramize it'
n = 6
sixgrams = ngrams(sentence.split …Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement!
In statistics, a unit root test tests whether a time series variable is non-stationary using an autoregressive model. A well-known test that is valid in large samples is the augmented Dickey …
Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement!
existing stemming method such as NLTK.PorterStem, etc.
didn't -> did not, there's -> there is, etc. Mr. -> Mister Mrs. -> ... Ms. -> ...
it seems that it is hard to get …
If your data can be fit into the CPU memory,
it is a good practice to save your data into one pickle file
(or other format that you know how to deserialize).
This comes with several advantages.
First, it is easier and faster to read from a single big file rather than many small files.
Second, it avoids the possible system error of openning too many files
(even though avoiding lazying data loading is another way to fix the issue).
Some example datasets (e.g., MNIST)
have separate training and testing files (i.e., 2 pickle files),
so that research work based on it can be easily reproduced.
I personally suggest that you keep only 1 file containing all data
when implementing your own Dataset class.
You can always use the function torch.utils.data.random_split