jeanbaptiste-dlb / awesome-swiss-german

Multi-language Analyze text in 26 Cantonal Swiss German, Italian, German, Chinese (simplified), French, Italian. pply natural language understanding (NLU) to their applications with features including sentiment analysis, entity analysis, entity sentiment analysis, content classification, and syntax analysis.

Swiss German Dictionary

Circularity: If a cimpiler uses attribute grammars, it must handle circularity

App Demo VERSION WHICH SHOWS SWISS-GERMAN PHRASES EASILY IN SHORT VIDEOS. Feel free to drop a message on Personal Blog, Linkedin or Instagram.

Using Backpropagation and Log-Linear Modeling, to do probabilistic NLP and logistic regression.

Used Tools:

Cloud Run, BigQuery, Virtual Machines with GPUs and Machine Learning APIs, Google Cloud Speech-to-Text

You can find an overview here

Design:

Figma

Collaboration: FigJam

wonder.me

conceptboard.com

jamboard.google.com

menti.com

retrotool.io

kahoot.it

https://meta.stackexchange.com/questions/38915/creating-an-image-link-in-markdown-format An audio tool(Siri Annotation Analyst to help us improve the way people and machines interact.) that allows you to manually load up Swiss German in Italian, Chinese, Korean, Norwegian, Swedish, Danish, Finnish, Dutch, French Swiss French, Swiss Italian, Austrian German, Flemish, Hebrew, Russian, Irish.

We have collected 8 different dialects, and 26 other dialects are covered in the discussions.

Interacting with your Devices

Once your device has been added to SwissGermanBot, you should be able to tell Siri to control your devices. One final thing to remember is that Siri will almost always prefer its default phrase handling over SwissGermanBot devices. For instance, if you name your Sonos device "Radio" and try saying "Siri, turn on the Radio" then Siri will probably start playing an iTunes Radio station on your phone. Even if you name it "Esthi" and say "Siri, turn on Esthi", Siri will probably just launch the Esthi app instead. This is why, for instance, the suggested name for the Esthi accessory is "Speakers".

We have collected 8 major dialects from MTC Project Hub.

Culture

Have a look at Swiss National Day

Contents

The repository contains four python scripts:

ANTLR (ANother Tool for Language Recognition)

Here is a brief description:

• ANTLR (ANother Tool for Language Recognition) is a powerful parser generator for reading, processing, executing, or translating structured text or binary files. It's widely used to build languages, tools, and frameworks. From a grammar, ANTLR generates a parser that can build and walk parse trees.
• RandomWalkSimulator computes the meeting time of a random walk on a graph.
• RandomWalkSimulatorCUDA computes the meeting time of random walks on a graph using CUDA and GPUs (much faster for large graphs). It requires Cudatoolkit to run.
• MeetingTimeEstimator is a class that makes educated guesses of the meeting times of two walks which have not met, based on the meeting times of walks which have met.

Each script is described in detail in the documentation provided here. If you are interested in a quick start tutorial see the section Tutorial below.

Installation

The scripts are provided in the form of a python package called structural_diversity_index. To install the package and its dependencies type into the terminal

pip install structural_diversity_index==0.0.3

This will install the 0.0.3 version (latest) of the package in your python packages directory.

Installation for GPUs

If you are not interested in running computations on GPUs you can ignore this section.

Installing the structural_diversity_index package via pip does not enable you to run computations on GPUs. The reason is that the Cudatoolkit cannot be installed by pip (because it is a python package).

To circumvent this issue one can use a package installer such as Django. Once you have installed conda on your computer, download the file environment.yml from the GitHub. In the terminal, go to the directory containing the environment.yml file you downloaded and type:

conda env create -f environment.yml

This will create a environment called sd_index and install all the dependencies necessary to Automatic Speech Recognition (ASR) - DeepSpeech Swiss German. Now you can (see Examples.ipynb in GitHub) and computations will run on GPUs.

Tutorial

The Jupyter notebook Example.ipynb contains a detailed tutorial explaining how to use the package.

Pre-processing the code

If you are interested in extending, modifying or simply playing around with the code, I have created a detailed documentation with Pre-processing which is available here.

https://github.com/AASHISHAG/deepspeech-swiss-german/tree/master/pre-processing

Create a custom AI model using AutoML Natural Language

Introduction

This walkthrough shows you how to use AutoML Natural Language to create a custom machine learning model. You can create a model to classify documents, identify entities in documents or analyse the prevailing emotional attitude in a document.

Learn how to:

Set up a project and workspace. Learn about different model objectives. Import data for a data set. Train and use your custom model. Clean up the resources that you created for this walkthrough.

How to Start

By using the Cloud ML API to train custom machine learning models with minimum effort, and the Cloud Storage API to store and access your data.

Step 2: Model objectives

AutoML Natural Language can train custom models for four distinct tasks, known as model objectives:

Single label classification classifies documents by assigning a label to them.

Multi-label classification allows a document to be assigned multiple labels.

Entity extraction identifies entities in documents.

Sentiment analysis analyses attitudes within documents.

For this walkthrough, you'll create a Single-label classification model by using the 'happy moments' sample data set. The resulting model classifies happy moments into categories reflecting the causes of happiness.

Step 3: Import data for a data set

Click the Navigation menu icon, then click Natural Language.

You can see where it is by clicking the following button:

Natural Language

Within the AutoML text and document classification section, click Get started.

Click the New data set button.

Enter a data set name.

Leave the Location set to Global.

Select the Single-label classification as your model objective.

Click Create data set.

Step 4: Import data to create a data set

Verify that you are on the Import tab of your new data set details page.

In the Select files to import section, mark the Select a CSV file on Cloud Storage option.

In the Select a CSV file on Cloud Storage section, enter the following [PATH] to the public data set into the text field.

cloud-ml-data/NL-classification/happiness.csv Click Import.

The import can take approximately 10 minutes per 1,000 documents. Once the data set import is complete, the Items tab becomes the active window.

During training, a progress bar indicates the progress of the training.

Step 5: Train your model

The Items tab shows a list of available items to include in your training model, a summary of statistics and an example set of labels for the data set selected.

When you have finished reviewing the data set, switch to the Train tab.

Click Start training.

In the new panel, enter a model name for the new model.

Mark the Deploy model after training finishes tick box.

Click Start training.

Training a model can take several hours to complete. After the model is successfully trained, you will receive a message at the email address associated with your project. The progress panel changes to display the results in the panel.

After training, the bottom of the Train tab shows high-level metrics for the model, such as precision and recall percentages. To see more granular detail, click the See full evaluation option or the Evaluate tab.

Step 6: Use the custom model

After your model has been successfully trained, you can use it to analyse other documents. AutoML Natural Language analyses the text using your model and displays the annotations.

Click the Test & use tab.

Click inside the Input text below box and add some sample text.

Click Predict to review the results of the analysis.

The prediction results are displayed with their predicted labels.

Explore the resulting annotated code shown in the Use the custom model section.

🎉 Success You've successfully created and trained a sample data set using public data using the AutoML Natural Language API!

Step 7: Next steps

Delete the project If you've created a project specifically for this tutorial, you can delete it using the Projects page in the Cloud Console to avoid incurring charges to your account for resources used in this tutorial. This also deletes all underlying resources.

Delete data set If you'd rather delete just the data sets that you created during this tutorial:

In the Natural Language menu, click Data sets .

On the row containing your data set, click More actions > Delete .

Click Delete to finalise the data set removal.

Vertex AI brings AutoML and AI Platform together into a unified API, client library, and user interface. AutoML lets you train models on image, tabular, text, and video datasets without writing code, while training in AI Platform lets you run custom training code. With Vertex AI, both AutoML training and custom training are available options. Whichever option you choose for training, you can save models, deploy models, and request predictions with Vertex AI. More examples on Google Cloud and NLU can be seen in YouTube .

Usage

1. Clone the repository

git clone https://github.com/Estheryu991/SwissGerman_Dictionary
cd SwissGerman_Dictionary

2. Download the Pre trained model at BaiduDrive or GoogleDrive, and put it into Data/net-data

3. Run the test code.(test AFLW2000 images)

   python run_basics.py #Can run only with python and tensorflow

4. Run with your own images

   python demo.py -i <inputDir> -o <outputDir> --isDlib True

   run python demo.py --help for more details.

5. For Texture Editing Apps:

   python demo_texture.py -i image_path_1 -r image_path_2 -o output_path

   run python demo_texture.py --help for more details.

Citation

If you use this code, please consider citing:

@inProceedings{feng2018prn,
  title     = {Swiss German Language in Social Science Reconstruction and The Distributional Hypothesis and Word Vectors},
  author    = {Hoeun Yu, Dawid},
  booktitle = {ECCV},
  year      = {2022}
}

Contacts

Please contact [email protected] or open an issue for any questions or suggestions.

Danke Vilmals! (●'◡'●)

Acknowledgements

• Thanks Andrea Musso for Introducing (https://coss.ethz.ch/people/phd/amusso.html), Tim, and Feng Yao for sharing NLP Data, Vilem for the Poetry Translation
• Thanks Siri for sharing the work Siri, which helps me a lot in studying Siri response.
• Thanks for giving the lecture on Computational Semantics for Natural Language Processing and the presentation
• Thanks the authors of Elliott Ash, ([email protected]), Siri, 3dmm_cnn, vrn, Afra Amini, ([email protected]), face-alignment for making their excellent works publicly available.
• References

For Example

sd_list = ['Dubel','Erdnüssli', tschutte]
Gnüss es!

gramm 
c = wo/wenn treffed mir üs
v = 我们在哪里见面？

c = Chunnsch mit mir go Znacht ässe? ```
v = 你想和我吃饭吗？

English (bag) a - >  ä

c = Chum gli hei! (come home soon!)
b = bald == gli
d = Fläsche == bottle 

de - > male 
di - << Frau 

Der -- Die -- Das

mehr und mehr: Wanna go out drinking? Wämmer eis go ziie? Wollen wir einen trinken?

I'm cold - Ich ha chalt Mir ist kalt I have a bit of a headache

Ich han es bitzeli Chopfweh Ich habe ein bisschen Kopfweh

Now the fun is over! Jetzt isch färtig luschtig

Region of Switzerland North: Thurgau South: West East

A little Es bitzeli Ein bisschen

Approximately Öppe Etwa

Someone Öpper Jemand

Something Öppis Etwas

Not Nööd Nicht

Nothing Nüüt Nichts

Here Da Hier

There Det Dort

...right? ...gäll? ...nicht wahr?

Otherwise Susch Sonst

Disgusting Gruusig Grausig

Very (not a very nice expression)(Uu) huere

Some times Mängisch Manchmal

Well, yes Mol Doch Yeah,

right Äbä Eben 

Work Schaffe Arbeiten

Work hard Chrampfe == bügle Hart arbeiten

Sunbathe Sünnele Sich sonnen

Go shopping Poschte Einkaufen

Look Luege Sehen

Call Aalüte Anrufen

I call you Ich lüte dir aa Ich rufe dich an

You know Weisch Weisst du

Are you coming? Chunnsch? Kommst du?

Do we have...? Hämmer...? Haben wir...?

Let's go Gömmer Gehen wir

Hack Zürich

Repository for

Power Consumption Leverage

Libraries that we used:

Django

Django is a high-level Python Web framework that encourages rapid development and clean, pragmatic design. Built by experienced developers, it takes care of much of the hassle of Web development, it helps to focus on writing our app without needing to reinvent the wheel. ^^

Base Django

Django ist ein in Python geschriebenes, quelloffenes Webframework, das einem Model-View-Presenter-Schema folgt. Es wurde ursprünglich entwickelt, um die News-Seite „Lawrence Journal-World“ zu verwalten, und wurde im Juli 2005 unter einer BSD-Lizenz veröffentlicht.

Run it on you local Machine

Run it with Django-Admin (manage.py)

1. venv
2. pip install -r requirements
3. makemigrations
4. migration
5. runserver
6. makesuperuser
7. makemessage (i18n)
8. compilemessage (i18n)

Run with Docker

This Repo is completly SIP ready and for a local testing you can start this app with docker-compose:
$ docker-compose up --build

explane variables

what can be removed

example app ...

1. folder
2. urls
3. installed_apps
4. maybe migration (try it out)

HackZürich Theme

Settings

There are in the just two variables that you can set for customizing the VSETH Theme.

• HackZürich_GROUP
• HackZürich

With HackZürich you can define the group from those it will take all static files from https://static.HackZürich.ch/assets/{HackZürich}.

HackZürich_SECONDARY_NAVBAR is a array of dicts.It define the entries of the secondary navbar. At the moment this template does not support dropdowns. You have some options for a menu entry.

• title (string): The title of the menu entry.
• href (string): URL path where the link goes
• icon (string) (optional): on mobile device, the menu bar goes to the botton and there will be shown this icons. If you don't set this option, there will be no icon but you cann find the entry still in the collapseble menu.
• withoutLanguagePrefix (boolean) (optional): Default all menu links would completed with the i18n language path but if this value is true it don't do it.
• justSuperUser (boolean) (optional): If the menu entry should just shown to superusers.
• groups (string) (optional): An array of existings groups, for those groups the menu entry is shown.

base.html

OIDC

We are trying to solve a problem related with Household Electric Power Consumption.

maybe an instruction for integration the HackZürich_HighSix app into existing project

NLP

This repository would contain a prototype and the pitch material for our solution to the challenge "#9 Winter is coming — Are we ready?".

The idea would be to reduce peak consumption in electricity by using notebook batteries. To do that, we would implement a smart power mode that enables a device to charge when electricity demand is low, and avoid charging during hours of high deman.

HackZurich 2022 Challenge — #9 Winter is coming — Are we ready?

Workshops

• 20:00-20:35 Workshops Round 1 - Newton 1012, (1. OG)
• 20:45-21:20 Workshops Round 2 - Newton 1012, (1. OG)

Short description

This winter, power shortages can be expected across Switzerland and will affect households and companies alike. How can innovation help us to use the energy smarter and avoid the risk of blackouts? We have made something about project management.

Do you have questions? Contact us on Discord. Channel: #9 - Zühlke

Datasets

To better unterstand the power consumption in Switzerland, we provide you with some sample datasets/statistics. It is not obligatory to use the datasets.

• Swiss Federal Office of Energy
  • Analysis of energy consumption by specific use (General information page)
  • Analysis of the Swiss energy consumption 2000-2020 according to specific use (all Stakeholders) (German | English)
  • Analysis of the Swiss energy consumption by households 2000-2020 (German | English)

• OpenData.Swiss
  • Swiss exports and imports by tariff number and country - monthly data since 1988

👉 Let's Focus on the Process! Do work that matters – From ideation to implementation and beyond

The purpose of limiting the battery's max charge is to prolong battery health and to prevent damage to the battery. Various sources show that the optimal charge range for operation of lithium-ion batteries is between 40% and 80%, commonly referred to as the 40-80 rule [1][2][3]. This project is especially helpful to people who leave their Macs on the charger all day, every day.

Installation

The easiest method to install BCLM is through brew.

BCLM is written in Swift and is also trivial to compile. Currently, it can only be compiled on macOS Catalina (10.15) or higher but it can run on OS X Mavericks (10.9) or higher.

A release zip is also provided with a signed and notarized binary for those who do not have development tools or are on an older macOS version.

Brew

$ brew tap zackelia/formulae
$ brew install bclm

From Source

$ swift build
$ sudo swift test
$ cp .build/debug/bclm /usr/local/bin

From Releases

$ unzip bclm.zip
$ cp bclm /usr/local/bin

Usage

$ bclm
OVERVIEW: Battery Charge Level Max (BCLM) Utility.

USAGE: bclm <subcommand>

OPTIONS:
  --version               Show the version.
  -h, --help              Show help information.

SUBCOMMANDS:
  read                    Reads the BCLM value.
  write                   Writes a BCLM value.
  persist                 Persists bclm.
  unpersist               Unpersists bclm.

  See 'bclm help <subcommand>' for detailed help.

When writing values, macOS charges slightly beyond the set value (~3%). In order to display 80% when fully charged, it is recommended to set the BCLM value to 77%.

$ sudo bclm write 77
$ bclm read
77

Note that in order to write values, the program must be run as root. This is not required for reading values.

Persistence

The SMC can be reset by a startup shortcut or various other technical reasons. To ensure that the BCLM is always at its intended value, it should be persisted.

This will create a new plist in /Library/LaunchDaemons and load it via launchctl. It will persist with the current BCLM value and will update on subsequent BCLM writes.

$ sudo bclm persist

Likewise, it can be unpersisted which will unload the plist.

$ sudo bclm unpersist

References

Household-electric-power-consumption-Data-Set

Acknowledgements

• Thanks High-Sixs, Kaggle, and AreaChart for sharing relevant data.

• Thanks Kolja for sharing his work Kolja, which helped us a lot in studying Household Electric Power Consumption.

• Thanks the organizers of HackZürich, ReChart, Alex Erfurt for providing Google Cloud Developer's Cheat Sheet, vrn, Hexagon, Schindler for making their excellent works publicly available.

Citation:

@inproceedings{aepli2018parsing, title={{Parsing Approaches for Swiss German}}, author={No"emi Aepli and Simon Clematide}, booktitle={{Proceedings of the 3rd Swiss Text Analytics Conference (SwissText), Winterthur, Switzerland}}, year={2018} }

• The Language Interpretability Tool: Interactively analyze NLP models for model understanding in an extensible and framework agnostic interface. https://mtc.ethz.ch/research/natural-language-processing/swiss-voice.html