1622708368
How VSpeech.ai’s ML Model Understands Mixed Language Inputs
Ahmedabad-based VSpeech.ai was founded in 2015. The startup sensed an opportunity while working with Interactive Voice Response (IVR) call centres, and soon pivoted to IVR based telephony integrations with Speech products.
Read more:https://analyticsindiamag.com/how-vspeech-ais-ml-model-understands-mixed-language-inputs-accurately/
#ai #ml
What is GEEK
Buddha Community
1622708368
How VSpeech.ai’s ML Model Understands Mixed Language Inputs
Ahmedabad-based VSpeech.ai was founded in 2015. The startup sensed an opportunity while working with Interactive Voice Response (IVR) call centres, and soon pivoted to IVR based telephony integrations with Speech products.
Read more:https://analyticsindiamag.com/how-vspeech-ais-ml-model-understands-mixed-language-inputs-accurately/
#ai #ml
1656151740
Test_cov_console: Flutter Console Coverage Test
Flutter Console Coverage Test
This small dart tools is used to generate Flutter Coverage Test report to console
How to install
Add a line like this to your package's pubspec.yaml (and run an implicit flutter pub get):
dev_dependencies:
test_cov_console: ^0.2.2
How to run
run the following command to make sure all flutter library is up-to-date
flutter pub get
Running "flutter pub get" in coverage... 0.5s
run the following command to generate lcov.info on coverage directory
flutter test --coverage
00:02 +1: All tests passed!
run the tool to generate report from lcov.info
flutter pub run test_cov_console
---------------------------------------------|---------|---------|---------|-------------------|
File |% Branch | % Funcs | % Lines | Uncovered Line #s |
---------------------------------------------|---------|---------|---------|-------------------|
lib/src/ | | | | |
print_cov.dart | 100.00 | 100.00 | 88.37 |...,149,205,206,207|
print_cov_constants.dart | 0.00 | 0.00 | 0.00 | no unit testing|
lib/ | | | | |
test_cov_console.dart | 0.00 | 0.00 | 0.00 | no unit testing|
---------------------------------------------|---------|---------|---------|-------------------|
All files with unit testing | 100.00 | 100.00 | 88.37 | |
---------------------------------------------|---------|---------|---------|-------------------|
Optional parameter
If not given a FILE, "coverage/lcov.info" will be used.
-f, --file=<FILE> The target lcov.info file to be reported
-e, --exclude=<STRING1,STRING2,...> A list of contains string for files without unit testing
to be excluded from report
-l, --line It will print Lines & Uncovered Lines only
Branch & Functions coverage percentage will not be printed
-i, --ignore It will not print any file without unit testing
-m, --multi Report from multiple lcov.info files
-c, --csv Output to CSV file
-o, --output=<CSV-FILE> Full path of output CSV file
If not given, "coverage/test_cov_console.csv" will be used
-t, --total Print only the total coverage
Note: it will ignore all other option (if any), except -m
-p, --pass=<MINIMUM> Print only the whether total coverage is passed MINIMUM value or not
If the value >= MINIMUM, it will print PASSED, otherwise FAILED
Note: it will ignore all other option (if any), except -m
-h, --help Show this help
example run the tool with parameters
flutter pub run test_cov_console --file=coverage/lcov.info --exclude=_constants,_mock
---------------------------------------------|---------|---------|---------|-------------------|
File |% Branch | % Funcs | % Lines | Uncovered Line #s |
---------------------------------------------|---------|---------|---------|-------------------|
lib/src/ | | | | |
print_cov.dart | 100.00 | 100.00 | 88.37 |...,149,205,206,207|
lib/ | | | | |
test_cov_console.dart | 0.00 | 0.00 | 0.00 | no unit testing|
---------------------------------------------|---------|---------|---------|-------------------|
All files with unit testing | 100.00 | 100.00 | 88.37 | |
---------------------------------------------|---------|---------|---------|-------------------|
report for multiple lcov.info files (-m, --multi)
It support to run for multiple lcov.info files with the followings directory structures:
1. No root module
<root>/<module_a>
<root>/<module_a>/coverage/lcov.info
<root>/<module_a>/lib/src
<root>/<module_b>
<root>/<module_b>/coverage/lcov.info
<root>/<module_b>/lib/src
...
2. With root module
<root>/coverage/lcov.info
<root>/lib/src
<root>/<module_a>
<root>/<module_a>/coverage/lcov.info
<root>/<module_a>/lib/src
<root>/<module_b>
<root>/<module_b>/coverage/lcov.info
<root>/<module_b>/lib/src
...
You must run test_cov_console on <root> dir, and the report would be grouped by module, here is
the sample output for directory structure 'with root module':
flutter pub run test_cov_console --file=coverage/lcov.info --exclude=_constants,_mock --multi
---------------------------------------------|---------|---------|---------|-------------------|
File |% Branch | % Funcs | % Lines | Uncovered Line #s |
---------------------------------------------|---------|---------|---------|-------------------|
lib/src/ | | | | |
print_cov.dart | 100.00 | 100.00 | 88.37 |...,149,205,206,207|
lib/ | | | | |
test_cov_console.dart | 0.00 | 0.00 | 0.00 | no unit testing|
---------------------------------------------|---------|---------|---------|-------------------|
All files with unit testing | 100.00 | 100.00 | 88.37 | |
---------------------------------------------|---------|---------|---------|-------------------|
---------------------------------------------|---------|---------|---------|-------------------|
File - module_a - |% Branch | % Funcs | % Lines | Uncovered Line #s |
---------------------------------------------|---------|---------|---------|-------------------|
lib/src/ | | | | |
print_cov.dart | 100.00 | 100.00 | 88.37 |...,149,205,206,207|
lib/ | | | | |
test_cov_console.dart | 0.00 | 0.00 | 0.00 | no unit testing|
---------------------------------------------|---------|---------|---------|-------------------|
All files with unit testing | 100.00 | 100.00 | 88.37 | |
---------------------------------------------|---------|---------|---------|-------------------|
---------------------------------------------|---------|---------|---------|-------------------|
File - module_b - |% Branch | % Funcs | % Lines | Uncovered Line #s |
---------------------------------------------|---------|---------|---------|-------------------|
lib/src/ | | | | |
print_cov.dart | 100.00 | 100.00 | 88.37 |...,149,205,206,207|
lib/ | | | | |
test_cov_console.dart | 0.00 | 0.00 | 0.00 | no unit testing|
---------------------------------------------|---------|---------|---------|-------------------|
All files with unit testing | 100.00 | 100.00 | 88.37 | |
---------------------------------------------|---------|---------|---------|-------------------|
Output to CSV file (-c, --csv, -o, --output)
flutter pub run test_cov_console -c --output=coverage/test_coverage.csv
#### sample CSV output file:
File,% Branch,% Funcs,% Lines,Uncovered Line #s
lib/,,,,
test_cov_console.dart,0.00,0.00,0.00,no unit testing
lib/src/,,,,
parser.dart,100.00,100.00,97.22,"97"
parser_constants.dart,100.00,100.00,100.00,""
print_cov.dart,100.00,100.00,82.91,"29,49,51,52,171,174,177,180,183,184,185,186,187,188,279,324,325,387,388,389,390,391,392,393,394,395,398"
print_cov_constants.dart,0.00,0.00,0.00,no unit testing
All files with unit testing,100.00,100.00,86.07,""Installing
Use this package as an executable
Install it
You can install the package from the command line:
dart pub global activate test_cov_consoleUse it
The package has the following executables:
$ test_cov_console
Use this package as a library
Depend on it
Run this command:
With Dart:
$ dart pub add test_cov_consoleWith Flutter:
$ flutter pub add test_cov_consoleThis will add a line like this to your package's pubspec.yaml (and run an implicit dart pub get):
dependencies:
test_cov_console: ^0.2.2Alternatively, your editor might support dart pub get or flutter pub get. Check the docs for your editor to learn more.
Import it
Now in your Dart code, you can use:
import 'package:test_cov_console/test_cov_console.dart';example/lib/main.dart
import 'package:flutter/material.dart';
void main() {
runApp(MyApp());
}
class MyApp extends StatelessWidget {
// This widget is the root of your application.
@override
Widget build(BuildContext context) {
return MaterialApp(
title: 'Flutter Demo',
theme: ThemeData(
// This is the theme of your application.
//
// Try running your application with "flutter run". You'll see the
// application has a blue toolbar. Then, without quitting the app, try
// changing the primarySwatch below to Colors.green and then invoke
// "hot reload" (press "r" in the console where you ran "flutter run",
// or simply save your changes to "hot reload" in a Flutter IDE).
// Notice that the counter didn't reset back to zero; the application
// is not restarted.
primarySwatch: Colors.blue,
// This makes the visual density adapt to the platform that you run
// the app on. For desktop platforms, the controls will be smaller and
// closer together (more dense) than on mobile platforms.
visualDensity: VisualDensity.adaptivePlatformDensity,
),
home: MyHomePage(title: 'Flutter Demo Home Page'),
);
}
}
class MyHomePage extends StatefulWidget {
MyHomePage({Key? key, required this.title}) : super(key: key);
// This widget is the home page of your application. It is stateful, meaning
// that it has a State object (defined below) that contains fields that affect
// how it looks.
// This class is the configuration for the state. It holds the values (in this
// case the title) provided by the parent (in this case the App widget) and
// used by the build method of the State. Fields in a Widget subclass are
// always marked "final".
final String title;
@override
_MyHomePageState createState() => _MyHomePageState();
}
class _MyHomePageState extends State<MyHomePage> {
int _counter = 0;
void _incrementCounter() {
setState(() {
// This call to setState tells the Flutter framework that something has
// changed in this State, which causes it to rerun the build method below
// so that the display can reflect the updated values. If we changed
// _counter without calling setState(), then the build method would not be
// called again, and so nothing would appear to happen.
_counter++;
});
}
@override
Widget build(BuildContext context) {
// This method is rerun every time setState is called, for instance as done
// by the _incrementCounter method above.
//
// The Flutter framework has been optimized to make rerunning build methods
// fast, so that you can just rebuild anything that needs updating rather
// than having to individually change instances of widgets.
return Scaffold(
appBar: AppBar(
// Here we take the value from the MyHomePage object that was created by
// the App.build method, and use it to set our appbar title.
title: Text(widget.title),
),
body: Center(
// Center is a layout widget. It takes a single child and positions it
// in the middle of the parent.
child: Column(
// Column is also a layout widget. It takes a list of children and
// arranges them vertically. By default, it sizes itself to fit its
// children horizontally, and tries to be as tall as its parent.
//
// Invoke "debug painting" (press "p" in the console, choose the
// "Toggle Debug Paint" action from the Flutter Inspector in Android
// Studio, or the "Toggle Debug Paint" command in Visual Studio Code)
// to see the wireframe for each widget.
//
// Column has various properties to control how it sizes itself and
// how it positions its children. Here we use mainAxisAlignment to
// center the children vertically; the main axis here is the vertical
// axis because Columns are vertical (the cross axis would be
// horizontal).
mainAxisAlignment: MainAxisAlignment.center,
children: <Widget>[
Text(
'You have pushed the button this many times:',
),
Text(
'$_counter',
style: Theme.of(context).textTheme.headline4,
),
],
),
),
floatingActionButton: FloatingActionButton(
onPressed: _incrementCounter,
tooltip: 'Increment',
child: Icon(Icons.add),
), // This trailing comma makes auto-formatting nicer for build methods.
);
}
}Author: DigitalKatalis
Source Code: https://github.com/DigitalKatalis/test_cov_console
License: BSD-3-Clause license
1603785600
Microsoft’s Turing Language Model Can Now Interpret 94 Languages
Recently, the developers at Microsoft detailed the Turing multilingual language model (T-ULRv2) and announced that the AI model has achieved the top rank at the Google XTREME public leaderboard.
The Cross-lingual TRansfer Evaluation of Multilingual Encoders, also known as XTREME benchmark includes 40 typologically diverse languages, which span 12 language families. XTREME also consists of nine tasks that require reasoning about different levels of syntax as well as semantics.
The Turing multilingual language model (T-ULRv2) is created by the Microsoft Turing team in collaboration with Microsoft Research. The model is also known to beat the previous best from Alibaba (VECO) by 3.5 points in average score.
Saurabh Tiwary, Vice President & Distinguished Engineer at Microsoft mentioned that in order to achieve this milestone, the team leveraged StableTune, which is a multilingual fine-tuning technique based on stability training along with the pre-trained model. The other popular language models on the XTREME leaderboard include XLM-R, mBERT, XLM, among others. Ming Zhou, Assistant Managing Director at Microsoft Research Asia, stated in a blog post that the Microsoft Turing team has long believed that language representation should be universal. Also, this kind of approach would allow for the trained model to be fine-tuned in one language and applied to a different one in a zero-shot fashion.
For a few years now, unsupervised pre-trained language modelling has become the backbone of all-natural language processing (NLP) models, with transformer-based models at the heart of all such innovation. According to Zhou, this type of models has the capability to overcome the challenge of requiring labelled data to train the model in every language.
How T-ULRv2 Works
The Turing multilingual language model (T-ULRv2) model is the latest cross-lingual innovation at the tech giant. It incorporates the InfoXLM (Information-Theoretic Framework for Cross-Lingual Language Model Pre-Training),which is a cross-lingual pre-trained model for language understanding and generation to create a universal model that represents 94 languages in the same vector space.
TT-ULRv2 is a transformer architecture with 24 layers and 1,024 hidden states. The architecture also includes a total of 550 million parameters. The pre-training of this model includes three different tasks, which are multilingual masked language modelling (MMLM), translation language modelling (TLM) and cross-lingual contrast (XLCo).
#developers corner #google xtreme #microsoft #microsoft ai #microsoft ai model #microsoft turing nlg #t-ulrv2 model #turing multilingual language model
1602709200
Learning AI/ML: The Hard Way
The Wave and the Curve
Data science, Artificial Intelligence (AI), and Machine Learning (ML), since last five to six years these phrases have made their places in Gartner’s hype cycle curve. Gradually they have crossed the peak and moving toward the plateau. The curve also has few related terms such as Deep Neural Network, Cognitive AutoML etc. This shows that, there is an emerging technology trend around AI/ML which is going to prevail over the software industry during the coming years. Few of their predecessors such as Business Intelligence, Data Mining and Data Warehousing were there even before these years.
Finding the Crystal Ball in the Jungle
Prediction and forecasting being my favorite topics, I started finding a way to get into this world of data and algorithms back in early 2019. Another driving force for me to learn AI/ML was my fascination on neural networks that was haunting me since I started learning about computer science. I collected few books, learned some python skills to dive into the crystal ball.
While I was going through the online articles, videos and books, I discovered lots of readily available tools, libraries and APIs for AI/ML. It was like someone who is trying to learn cycling and given a car to drive. Due to my interest in neural networks, I got attracted to most the most interesting sub-set of AI/ML, Deep Learning, which deals with deep neural networks. I couldn’t stop myself from directly jumping into Google Tensorflow (a free Google ML tool) and got overwhelmed by a huge collection of its APIs. I could follow the documentation, write code and even made it work. But there was a problem, I was unable understand why I am doing what I am doing. I was completely drowning with the terms like bios, variance, parameters, feature selection, feature scaling, drop out etc. That’s when I took a break, rewind and learn about the internals of AI/ML rather than just using the APIs and Libs blindly. So, I took the hard way.
On one side, I was allured by the readily available smart AI/ML tools and on the other side, my fascination on neural networks was attracting me to learn it from scratch. Meanwhile, I have spent around a month or two just looking for a path to enter the subject. A huge pool of internet resources made me thoroughly confused in identifying the doorway to the heart of puzzle. I realized, why it is a hard nut for people to learn. Janakiram MSV pointed out the reasons correctly in his article.
However, some were very useful, such as an Introduction to Machine Learning by Prof. Grimson from MIT OpenCourseWare. Though its little long but helpful.
#machine learning #ai #artificial intelligence (ai) #ml #ai guide #ai roadmap
1619511840
Making Sales More Efficient: Lead Qualification Using AI
If you were to ask any organization today, you would learn that they are all becoming reliant on Artificial Intelligence Solutions and using AI to digitally transform in order to bring their organizations into the new age. AI is no longer a new concept, instead, with the technological advancements that are being made in the realm of AI, it has become a much-needed business facet.
AI has become easier to use and implement than ever before, and every business is applying AI solutions to their processes. Organizations have begun to base their digital transformation strategies around AI and the way in which they conduct their business. One of these business processes that AI has helped transform is lead qualifications.
#ai-solutions-development #artificial-intelligence #future-of-artificial-intellige #ai #ai-applications #ai-trends #future-of-ai #ai-revolution