martinthenext

# Smart pane switching with awareness of Vim splits.
# See: https://github.com/christoomey/vim-tmux-navigator
is_vim="ps -o state= -o comm= -t '#{pane_tty}' \
    | grep -iqE '^[^TXZ ]+ +(\\S+\\/)?g?(view|n?vim?x?)(diff)?$'"
bind-key -n 'C-h' if-shell "$is_vim" 'send-keys C-h'  'select-pane -L'
bind-key -n 'C-j' if-shell "$is_vim" 'send-keys C-j'  'select-pane -D'
bind-key -n 'C-k' if-shell "$is_vim" 'send-keys C-k'  'select-pane -U'
bind-key -n 'C-l' if-shell "$is_vim" 'send-keys C-l'  'select-pane -R'
tmux_version='$(tmux -V | sed -En "s/^tmux ([0-9]+(.[0-9]+)?).*/\1/p")'
if-shell -b '[ "$(echo "$tmux_version < 3.0" | bc)" = 1 ]' \

martinthenext

filetype off                  " required

" set the runtime path to include Vundle and initialize
set rtp+=~/.vim/bundle/Vundle.vim
call vundle#begin()

" let Vundle manage Vundle, required
Plugin 'VundleVim/Vundle.vim'

" The following are examples of different formats supported.

martinthenext

#!/usr/bin/env python
# -*- coding: utf-8 -*-

from telegram import Update
from telegram.ext import Updater, CommandHandler, MessageHandler, Filters, CallbackContext
import logging
from pymongo import MongoClient, TEXT, DESCENDING
from datetime import datetime, timedelta
import settings
import re

martinthenext

filetype off                  " required

" set the runtime path to include Vundle and initialize
set rtp+=~/.vim/bundle/Vundle.vim
call vundle#begin()

" let Vundle manage Vundle, required
Plugin 'VundleVim/Vundle.vim'

" The following are examples of different formats supported.

martinthenext

from sklearn.metrics.pairwise import cosine_similarity

similarity = cosine_similarity(question_repr, sentence_repr)
ranks = (-similarity).argsort(axis=None)
context_sentences[ranks[0]]

'During the middle of 2010, iPhone sales overtook those of the iPod.'

martinthenext

from sklearn.feature_extraction.text import TfidfVectorizer

sentences = [sentence
             for datum in squad['data']
             for paragraph in datum['paragraphs']
             for sentence in paragraph['context'].split('. ')
            ]

tf_idf_vect = TfidfVectorizer(tokenizer=tokenize_and_stem)
tf_idf_vect.fit(sentences)

martinthenext

question = squad['data'][3]['paragraphs'][3]['qas'][3]['question']
tokenize_and_stem(question)

['In',
 'what',
 'year',
 'did',
 'iphon',
 'sale',
 'surpass',

martinthenext

squad['data'][3]['paragraphs'][3]['qas'][3]

{'question': 'In what year did iPhone sales surpass those of iPods?',
 'id': '56ce73d1aab44d1400b887ac',
 'answers': [{'text': '2010', 'answer_start': 444}],
 'is_impossible': False}

martinthenext

import json

with open('data/train-v2.0.json') as squad_file:
    squad = json.load(squad_file)
    
context = squad['data'][3]['paragraphs'][3]['context']
context

'''Before the release of iOS 5, the iPod branding was used for the media player
included with the iPhone and iPad, a combination of the Music and Videos apps on

martinthenext

def read_csv_to_dicts(filename):
    with open(filename) as csvfile:
        reader = csv.reader(csvfile, dialect='excel')
        header = transform_header(next(reader))

        if len(header) != len(set(header)):
            raise ValueError("Duplicate column names in CSV.")

        data = [{field: value for field, value in zip(header, row)}
                for row in reader]