Since Chrome apps are now being deprecated. Download postman from https://dl.pstmn.io/download/latest/linux
Although I highly recommend using a snap
sudo snap install postman
tar -xzf Postman-linux-x64-5.3.2.tar.gz
import findspark | |
findspark.init("[spark install location]") | |
import pyspark | |
import string | |
from pyspark import SparkContext | |
from pyspark.sql import SQLContext | |
from pyspark.mllib.util import MLUtils | |
from pyspark.sql.types import * | |
from pyspark.ml.feature import CountVectorizer, CountVectorizerModel, Tokenizer, RegexTokenizer, StopWordsRemover |
FROM python:3.6 | |
WORKDIR /app | |
ADD . /app | |
RUN pip install -r requirements.txt | |
RUN python setup.py build_ext --inplace | |
ENTRYPOINT ["python"] | |
CMD ["app.py"] |
Since Chrome apps are now being deprecated. Download postman from https://dl.pstmn.io/download/latest/linux
Although I highly recommend using a snap
sudo snap install postman
tar -xzf Postman-linux-x64-5.3.2.tar.gz
import torch | |
import torch.nn as nn | |
from torch.autograd import Variable | |
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence | |
import itertools | |
import string | |
""" | |
Since character embeddings are a bit weak in pytorch 3, this will hopefully help out | |
I think these should be trainable and also, invertable! |
# if input image is in range 0..1, please first multiply img by 255 | |
# assume image is ndarray of shape [height, width, channels] where channels can be 1, 3 or 4 | |
def imshow(img): | |
import cv2 | |
import IPython | |
_,ret = cv2.imencode('.jpg', img) | |
i = IPython.display.Image(data=ret) | |
IPython.display.display(i) |
''' Script for downloading all GLUE data. | |
Note: for legal reasons, we are unable to host MRPC. | |
You can either use the version hosted by the SentEval team, which is already tokenized, | |
or you can download the original data from (https://download.microsoft.com/download/D/4/6/D46FF87A-F6B9-4252-AA8B-3604ED519838/MSRParaphraseCorpus.msi) and extract the data from it manually. | |
For Windows users, you can run the .msi file. For Mac and Linux users, consider an external library such as 'cabextract' (see below for an example). | |
You should then rename and place specific files in a folder (see below for an example). | |
mkdir MRPC | |
cabextract MSRParaphraseCorpus.msi -d MRPC |
If you haven’t worked with JavaScript in the last few years, these three points should give you enough knowledge to feel comfortable reading the React documentation:
let
and const
statements. For the purposes of the React documentation, you can consider them equivalent to var
.class
keyword to define JavaScript classes. There are two things worth remembering about them. Firstly, unlike with objects, you don't need to put commas between class method definitions. Secondly, unlike many other languages with classes, in JavaScript the value of this
in a method [depends on how it is called](https://developer.mozilla.org/en-US/docs/Web/Jav#deb cdrom:[Ubuntu 18.04 LTS _Bionic Beaver_ - Release amd64 (20180426)]/ bionic main restricted | |
# See http://help.ubuntu.com/community/UpgradeNotes for how to upgrade to | |
# newer versions of the distribution. | |
deb http://us.archive.ubuntu.com/ubuntu/ bionic main restricted | |
# deb-src http://us.archive.ubuntu.com/ubuntu/ bionic main restricted | |
## Major bug fix updates produced after the final release of the | |
## distribution. | |
deb http://us.archive.ubuntu.com/ubuntu/ bionic-updates main restricted |
##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ | |
## Created by: Hang Zhang, Rutgers University, Email: [email protected] | |
## Modified by Thomas Wolf, HuggingFace Inc., Email: [email protected] | |
## Copyright (c) 2017-2018 | |
## | |
## This source code is licensed under the MIT-style license found in the | |
## LICENSE file in the root directory of this source tree | |
##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ | |
"""Encoding Data Parallel""" |