msubhash

+---------------------------------------------------------------+
|                                                               |
+---------------------------------------------------------------+
| English Translation                                           |
| Sanskrit Sentence                                             |
| Word-by-Word Meaning                                          |
| I go                                                          |
| अहं गच्छामि                                                   |
| अहं (I), गच्छामि (go)                                         |

msubhash

https://www.meetup.com/Bangalore-Artificial-Intelligence-Deep-Learning/
https://www.meetup.com/Bangalore-Hadoop-Meetups/
https://www.meetup.com/Bangalore-Apache-Spark-Meetup/
https://www.meetup.com/CloudxLab/
https://www.meetup.com/Analytics-Data-Science-R-Tableau-Power-BI/
https://www.meetup.com/Bangalore-Spark-Enthusiasts/
https://www.meetup.com/Machine-Learning-India-Bangalore/
https://www.meetup.com/Apache-Hadoop-Ecosystem/
https://www.meetup.com/BangPypers/

msubhash

return {
  fields = {
    host = { required = true, type = "string" },
    port = { required = true, type = "number" },
    timeout = {default = 10000, type = "number"},
    keepalive = {default = 30, type = "number"},
  }
}

msubhash

local cjson = require "cjson"
local url = require "socket.url"
local BasePlugin = require "kong.plugins.base_plugin"
local basic_serializer = require "kong.plugins.log-serializers.basic"

local _M = {}

local HTTPS = "https"
local ngx_log = ngx.log
local ngx_log_ERR = ngx.ERR

msubhash

local tcp_body_log_serializer = require "kong.plugins.log-serializers.runscope"
local BasePlugin = require "kong.plugins.base_plugin"
local log = require "kong.plugins.tcp-body-log.log"
local public_utils = require "kong.tools.public"

local string_find = string.find
local req_read_body = ngx.req.read_body
local req_get_headers = ngx.req.get_headers
local req_get_body_data = ngx.req.get_body_data

msubhash

from gensim.models import Phrases
from gensim.models import Word2Vec
from nltk.tokenize import RegexpTokenizer
from nltk.corpus import stopwords
import re

sentences = []
fname = "model.mm"
input_file="stories_15k.txt"