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| <link rel="modulepreload" href="/docs/course/pr_1114/de/_app/immutable/chunks/DocNotebookDropdown.4b5087ab.js"><!-- HEAD_svelte-u9bgzb_START --><meta name="hf:doc:metadata" content="{"title":"Bias und Einschränkungen","local":"bias-und-einschränkungen","sections":[],"depth":1}"><!-- HEAD_svelte-u9bgzb_END --> <p></p> <div class="items-center shrink-0 min-w-[100px] max-sm:min-w-[50px] justify-end ml-auto flex" style="float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"><div class="inline-flex rounded-md max-sm:rounded-sm"><button class="inline-flex items-center gap-1 max-sm:gap-0.5 h-6 max-sm:h-5 px-2 max-sm:px-1.5 text-[11px] max-sm:text-[9px] font-medium text-gray-800 border border-r-0 rounded-l-md max-sm:rounded-l-sm border-gray-200 bg-white hover:shadow-inner dark:border-gray-850 dark:bg-gray-950 dark:text-gray-200 dark:hover:bg-gray-800" aria-live="polite"><span class="inline-flex items-center justify-center rounded-md p-0.5 max-sm:p-0"><svg 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<a href="https://colab.research.google.com/github/huggingface/notebooks/blob/master/course/de/chapter1/section8.ipynb" target="_blank"><img alt="Open In Colab" class="!m-0" src="https://colab.research.google.com/assets/colab-badge.svg"></a> <a href="https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/course/de/chapter1/section8.ipynb" target="_blank"><img alt="Open In Studio Lab" class="!m-0" src="https://studiolab.sagemaker.aws/studiolab.svg"></a></div> <p data-svelte-h="svelte-ywmlmo">Wenn du vorhast, ein vortrainiertes Modell oder eine feingetunte Modellversion in der Produktion zu verwenden, sei dir bitte darüber im Klaren, dass diese zwar leistungsstarke Werkzeuge sind, allerdings aber auch ihre Grenzen haben. Die größte Einschränkung ergibt sich daraus, dass Forscherinnen und Forscher für das auf Basis großer Datenmengen durchgeführte Pretraining oft alle Inhalte, die sie finden können, zusammensuchen und dabei sowohl all das Gute als auch das Schlechte einbezogen wird, was das Internet zu bieten hat.</p> <p data-svelte-h="svelte-pwgyh6">Greifen wir zur Veranschaulichung noch einmal das Beispiel einer <code>fill-mask</code>-Pipeline mit dem BERT-Modell auf:</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline | |
| unmasker = pipeline(<span class="hljs-string">"fill-mask"</span>, model=<span class="hljs-string">"bert-base-uncased"</span>) | |
| result = unmasker(<span class="hljs-string">"This man works as a [MASK]."</span>) | |
| <span class="hljs-built_in">print</span>([r[<span class="hljs-string">"token_str"</span>] <span class="hljs-keyword">for</span> r <span class="hljs-keyword">in</span> result]) | |
| result = unmasker(<span class="hljs-string">"This woman works as a [MASK]."</span>) | |
| <span class="hljs-built_in">print</span>([r[<span class="hljs-string">"token_str"</span>] <span class="hljs-keyword">for</span> r <span class="hljs-keyword">in</span> result])<!-- HTML_TAG_END --></pre></div> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START -->[<span class="hljs-string">'lawyer'</span>, <span class="hljs-string">'carpenter'</span>, <span class="hljs-string">'doctor'</span>, <span class="hljs-string">'waiter'</span>, <span class="hljs-string">'mechanic'</span>] | |
| [<span class="hljs-string">'nurse'</span>, <span class="hljs-string">'waitress'</span>, <span class="hljs-string">'teacher'</span>, <span class="hljs-string">'maid'</span>, <span class="hljs-string">'prostitute'</span>]<!-- HTML_TAG_END --></pre></div> <p data-svelte-h="svelte-6gp8xh">Wenn das Modell aufgefordert wird, das fehlende Wort in diesen beiden Sätzen zu ergänzen, gibt es lediglich eine geschlechtsneutrale Antwort (Kellnerin/Kellner - waitress/waiter). Bei den anderen handelt es sich um Berufe, die normalerweise mit einem bestimmten Geschlecht assoziiert werden - und ja, “prostitute” landete unter den Top 5, die das Modell mit “woman” und “work” assoziiert. Und das, obwohl BERT eines der wenigen Transformer-Modelle ist, das nicht auf Daten aus dem gesamten Internet beruht, sondern auf vermeintlich neutralen Daten (es wurde auf dem <a href="https://huggingface.co/datasets/wikipedia" rel="nofollow">englischsprachigen Wikipedia-</a> und dem <a href="https://huggingface.co/datasets/bookcorpus" rel="nofollow">BookCorpus-Datensatz</a> trainiert).</p> <p data-svelte-h="svelte-1enwwif">Wenn du diese Werkzeuge verwendest, musst du daher im Hinterkopf behalten, dass das ursprüngliche Modell, das du verwendest, sehr leicht sexistische, rassistische oder homophobe Inhalte hervorbringen könnte. Beim Feintuning des Modells auf deinen Daten werden diese inhärenten Voreingenommenheiten bzw. Vorurteile (engl. Bias) nicht verschwinden.</p> <a class="!text-gray-400 !no-underline text-sm flex items-center not-prose mt-4" href="https://github.com/huggingface/course/blob/main/chapters/de/chapter1/8.mdx" target="_blank"><svg class="mr-1" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M31,16l-7,7l-1.41-1.41L28.17,16l-5.58-5.59L24,9l7,7z"></path><path d="M1,16l7-7l1.41,1.41L3.83,16l5.58,5.59L8,23l-7-7z"></path><path d="M12.419,25.484L17.639,6.552l1.932,0.518L14.351,26.002z"></path></svg> <span data-svelte-h="svelte-zjs2n5"><span class="underline">Update</span> on GitHub</span></a> <p></p> | |
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