course/pr_1114/de/_app/immutable/nodes/26.3ab8ea99.js

import{s as j,n as J,o as X}from"../chunks/scheduler.7fa8c90f.js";import{S as Y,i as tt,e as s,s as n,c as P,h as et,a as o,d,b as a,f as Q,g as $,j as L,k as Z,l as dt,m as r,n as w,t as M,o as v,p as A}from"../chunks/index.ae290a83.js";import{C as rt,H as q,E as nt}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.5cd8c442.js";function at(B){let i,x,y,C,l,S,u,z,c,W="<thead><tr><th>Original</th> <th>Übersetzung</th></tr></thead> <tbody><tr><td>Abstraction</td> <td>Abstraktion</td></tr> <tr><td>Account</td> <td>Account</td></tr> <tr><td>Accuracy</td> <td>Genauigkeit</td></tr> <tr><td>Artificial General Intelligence</td> <td>künstliche allgemeine Intelligenz</td></tr> <tr><td>Attention</td> <td>Attention</td></tr> <tr><td>Attention mask (layer)</td> <td>Attention-Mask (Layer)</td></tr> <tr><td>Backward Pass</td> <td>Rückwärtsalgorithmus berechnen</td></tr> <tr><td>Batch</td> <td>Batch</td></tr> <tr><td>Bias</td> <td>Bias (Voreingenommenheit)</td></tr> <tr><td>Causal Language Modeling</td> <td>kausale Sprachmodellierung</td></tr> <tr><td>Chapter</td> <td>Kapitel</td></tr> <tr><td>Checkpoint(s)</td> <td>Checkpoint(s)</td></tr> <tr><td>Class</td> <td>Klasse</td></tr> <tr><td>Classification</td> <td>Klassifizierung</td></tr> <tr><td>Code</td> <td>Code</td></tr> <tr><td>Colab Notebook</td> <td>Colab Notebook</td></tr> <tr><td>Command</td> <td>Befehl</td></tr> <tr><td>Computer Vision</td> <td>Computer Vision</td></tr> <tr><td>Configuration</td> <td>Konfiguration</td></tr> <tr><td>Course</td> <td>Kurs</td></tr> <tr><td>Decoder</td> <td>Decoder</td></tr> <tr><td>Dependency</td> <td>Abhängigkeitsbeziehung</td></tr> <tr><td>Deployment</td> <td>Deployment</td></tr> <tr><td>Development</td> <td>Entwicklung</td></tr> <tr><td>Dictionary</td> <td>Dictionary</td></tr> <tr><td>Distribution</td> <td>Verteilung</td></tr> <tr><td>Download</td> <td>Download</td></tr> <tr><td>Encoder</td> <td>Encoder</td></tr> <tr><td>Extractive question answering</td> <td>Extraktives Question Answering</td></tr> <tr><td>F1 score</td> <td>F1-Maß</td></tr> <tr><td>Feature</td> <td>Feature</td></tr> <tr><td>Fine-tune</td> <td>feintunen</td></tr> <tr><td>Fine-tuning</td> <td>Feintuning</td></tr> <tr><td>Folder</td> <td>Ordner</td></tr> <tr><td>Forward Pass</td> <td>Vorwärtsalgorithmus berechnen</td></tr> <tr><td>Function</td> <td>Funktion</td></tr> <tr><td>Generative question answering</td> <td>Generatives Question Answering</td></tr> <tr><td>Google</td> <td>Google</td></tr> <tr><td>Hugging Face</td> <td>Hugging Face</td></tr> <tr><td>Incompatibility</td> <td>Inkompatibilität</td></tr> <tr><td>Inference</td> <td>Inferenz</td></tr> <tr><td>Input</td> <td>Input</td></tr> <tr><td>Input data</td> <td>Input-Daten</td></tr> <tr><td>Label (verb)</td> <td>labeln (gelabelt), annotieren</td></tr> <tr><td>Label (subj)</td> <td>Label, das / Labels, die (plur.)</td></tr> <tr><td>Layer</td> <td>Layer (plur. Layer(n))</td></tr> <tr><td>Library</td> <td>Bibliothek</td></tr> <tr><td>Linux</td> <td>Linux</td></tr> <tr><td>Load</td> <td>laden</td></tr> <tr><td>Loss function</td> <td>Verlustfunktion</td></tr> <tr><td>Machine Learning</td> <td>Maschinelles Lernen</td></tr> <tr><td>macOS</td> <td>macOS</td></tr> <tr><td>Mask</td> <td>Maskierung</td></tr> <tr><td>Mask Filling</td> <td>Mask Filling</td></tr> <tr><td>Mask Token</td> <td>Mask-Token</td></tr> <tr><td>Masked Language Modeling</td> <td>maskierte Sprachmodellierung</td></tr> <tr><td>Model</td> <td>Modell</td></tr> <tr><td>Model Hub</td> <td>Model Hub</td></tr> <tr><td>Module</td> <td>Modul</td></tr> <tr><td>Named Entities</td> <td>benannte Entitäten</td></tr> <tr><td>Named Entity Recognition</td> <td>Eigennamenerkennung</td></tr> <tr><td>Natural Language Processing</td> <td>Computerlinguistik</td></tr> <tr><td>Output</td> <td>Output</td></tr> <tr><td>Package</td> <td>Paket</td></tr> <tr><td>Package Manager</td> <td>Paketverwaltung</td></tr> <tr><td>Padding</td> <td>das Padding / auffüllen</td></tr> <tr><td>Parameter</td> <td>Parameter</td></tr> <tr><td>Postprocessing</td> <td>Nachverarveitung</td></tr> <tr><td>Preprocessing</td> <td>Vorverarbeitung</td></tr> <tr><td>Pretraining</td> <td>Pretraining</td></tr> <tr><td>Pretrained model</td> <td>vortrainiertes Modell</td></tr> <tr><td>Prompt</td> <td>Prompt</td></tr> <tr><td>Python</td> <td>Python</td></tr> <tr><td>Pytorch</td> <td>Pytorch</td></tr> <tr><td>Question Answering</td> <td>Question Answering</td></tr> <tr><td>Save</td> <td>speichern</td></tr> <tr><td>Sample</td> <td>Sample (auch Stichprobe)</td></tr> <tr><td>Script</td> <td>Script</td></tr> <tr><td>Self-Contained</td> <td>in sich abgeschlossen</td></tr> <tr><td>Sentiment analysis</td> <td>Sentiment-Analyse</td></tr> <tr><td>Sequence-to-sequence models</td> <td>Sequence-to-Sequence-Modelle</td></tr> <tr><td>Setup</td> <td>Installation</td></tr> <tr><td>Speech Processing</td> <td>Verarbeitung gesprochener Sprache</td></tr> <tr><td>Speech Recognition</td> <td>Spracherkennung</td></tr> <tr><td>Summarization</td> <td>Automatische Textzusammenfassung</td></tr> <tr><td>Target</td> <td>Zielvariable / vorherzusagende Variable</td></tr> <tr><td>Task</td> <td>Aufgabe / Aufgabenstellung</td></tr> <tr><td>TensorFlow</td> <td>Tensorflow</td></tr> <tr><td>Terminal</td> <td>Terminal</td></tr> <tr><td>Text generation</td> <td>Textgenerierung</td></tr> <tr><td>Tokenizer</td> <td>Tokenizer</td></tr> <tr><td>Train</td> <td>Training</td></tr> <tr><td>Transfer Learning</td> <td>Transfer Learning</td></tr> <tr><td>Transformer</td> <td>Transformer</td></tr> <tr><td>Transformer models</td> <td>Transformer-Modelle</td></tr> <tr><td>Translation</td> <td>Maschinelle Übersetzung</td></tr> <tr><td>Virtual Environment</td> <td>Virtuelle Umgebung</td></tr> <tr><td>Weight</td> <td>Gewicht</td></tr> <tr><td>Weights</td> <td>Gewichtung</td></tr> <tr><td>Windows</td> <td>Windows</td></tr> <tr><td>Working Environment</td> <td>Arbeitsumgebung</td></tr> <tr><td>Workload</td> <td>Auslastung</td></tr> <tr><td>Workspace</td> <td>Workspace</td></tr> <tr><td>Zero-shot classification</td> <td>Zero-Shot-Klassifizierung</td></tr></tbody>",_,g,U="=======",E,m,F,p,O="<thead><tr><th>Original</th> <th>Übersetzung</th></tr></thead> <tbody><tr><td>NLP</td> <td>CL</td></tr> <tr><td>API</td> <td>API</td></tr> <tr><td>GPU</td> <td>GPU</td></tr> <tr><td>TPU</td> <td>TPU</td></tr> <tr><td>ML</td> <td>ML</td></tr></tbody>",G,h,I,f,R='Please refer to <a href="/chapters/de/TRANSLATING.txt">TRANSLATING.txt</a> for a translation guide. Here are some excerpts relevant to the glossary:',H,b,K=`<li><p>Refer and contribute to the glossary frequently to stay on top of the latest
choices we make. This minimizes the amount of editing that is required.
Add new terms alphabetically sorted.</p></li> <li><p>The German language accepts English words especially in modern contexts more
than many other languages (i.e. Anglicisms). Check for the correct usage of
terms in computer science and commonly used terms in other publications.</p></li> <li><p>Don’t translate industry-accepted acronyms. e.g. TPU or GPU.</p></li> <li><p>If translating a technical word, keep the choice of German translation consistent.
This does not apply for non-technical choices, as in those cases variety actually
helps keep the text engaging.</p></li> <li><p>Be exact when choosing equivalents for technical words. Package is package.
Library is library. Don’t mix and match.</p></li>`,D,k,N,T,V;return l=new rt({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),u=new q({props:{title:"Wörterverzeichnis",local:"wörterverzeichnis",headingTag:"h1"}}),m=new q({props:{title:"Abkürzungen",local:"abkürzungen",headingTag:"h2"}}),h=new q({props:{title:"Notes",local:"notes",headingTag:"h2"}}),k=new nt({props:{source:"https://github.com/huggingface/course/blob/main/chapters/de/glossary/1.mdx"}}),{c(){i=s("meta"),x=n(),y=s("p"),C=n(),P(l.$$.fragment),S=n(),P(u.$$.fragment),z=n(),c=s("table"),c.innerHTML=W,_=n(),g=s("p"),g.textContent=U,E=n(),P(m.$$.fragment),F=n(),p=s("table"),p.innerHTML=O,G=n(),P(h.$$.fragment),I=n(),f=s("p"),f.innerHTML=R,H=n(),b=s("ul"),b.innerHTML=K,D=n(),P(k.$$.fragment),N=n(),T=s("p"),this.h()},l(t){const e=et("svelte-u9bgzb",document.head);i=o(e,"META",{name:!0,content:!0}),e.forEach(d),x=a(t),y=o(t,"P",{}),Q(y).forEach(d),C=a(t),$(l.$$.fragment,t),S=a(t),$(u.$$.fragment,t),z=a(t),c=o(t,"TABLE",{"data-svelte-h":!0}),L(c)!=="svelte-1so3zcs"&&(c.innerHTML=W),_=a(t),g=o(t,"P",{"data-svelte-h":!0}),L(g)!=="svelte-756rwd"&&(g.textContent=U),E=a(t),$(m.$$.fragment,t),F=a(t),p=o(t,"TABLE",{"data-svelte-h":!0}),L(p)!=="svelte-1no24c7"&&(p.innerHTML=O),G=a(t),$(h.$$.fragment,t),I=a(t),f=o(t,"P",{"data-svelte-h":!0}),L(f)!=="svelte-153d3rm"&&(f.innerHTML=R),H=a(t),b=o(t,"UL",{"data-svelte-h":!0}),L(b)!=="svelte-cei796"&&(b.innerHTML=K),D=a(t),$(k.$$.fragment,t),N=a(t),T=o(t,"P",{}),Q(T).forEach(d),this.h()},h(){Z(i,"name","hf:doc:metadata"),Z(i,"content",it)},m(t,e){dt(document.head,i),r(t,x,e),r(t,y,e),r(t,C,e),w(l,t,e),r(t,S,e),w(u,t,e),r(t,z,e),r(t,c,e),r(t,_,e),r(t,g,e),r(t,E,e),w(m,t,e),r(t,F,e),r(t,p,e),r(t,G,e),w(h,t,e),r(t,I,e),r(t,f,e),r(t,H,e),r(t,b,e),r(t,D,e),w(k,t,e),r(t,N,e),r(t,T,e),V=!0},p:J,i(t){V||(M(l.$$.fragment,t),M(u.$$.fragment,t),M(m.$$.fragment,t),M(h.$$.fragment,t),M(k.$$.fragment,t),V=!0)},o(t){v(l.$$.fragment,t),v(u.$$.fragment,t),v(m.$$.fragment,t),v(h.$$.fragment,t),v(k.$$.fragment,t),V=!1},d(t){t&&(d(x),d(y),d(C),d(S),d(z),d(c),d(_),d(g),d(E),d(F),d(p),d(G),d(I),d(f),d(H),d(b),d(D),d(N),d(T)),d(i),A(l,t),A(u,t),A(m,t),A(h,t),A(k,t)}}}const it='{"title":"Wörterverzeichnis","local":"wörterverzeichnis","sections":[{"title":"Abkürzungen","local":"abkürzungen","sections":[],"depth":2},{"title":"Notes","local":"notes","sections":[],"depth":2}],"depth":1}';function st(B){return X(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class ct extends Y{constructor(i){super(),tt(this,i,st,at,j,{})}}export{ct as component};