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m=new Nt({}),I=new Io({props:{chapter:1,classNames:"absolute z-10 right-0 top-0",notebooks:[{label:"Google Colab",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/master/course/en/chapter1/section3.ipynb"},{label:"Aws Studio",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/course/en/chapter1/section3.ipynb"}]}}),X=new Ee({props:{$$slots:{default:[Ho]},$$scope:{ctx:A}}}),be=new Nt({}),te=new Ee({props:{$$slots:{default:[To]},$$scope:{ctx:A}}}),ke=new Nt({}),qe=new Po({props:{id:"tiZFewofSLM"}}),Pe=new O({props:{code:`from transformers import pipeline
classifier = pipeline("sentiment-analysis")
classifier("I've been waiting for a HuggingFace course my whole life.")`,highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline
classifier = pipeline(<span class="hljs-string">&quot;sentiment-analysis&quot;</span>)
classifier(<span class="hljs-string">&quot;I&#x27;ve been waiting for a HuggingFace course my whole life.&quot;</span>)`}}),Ie=new O({props:{code:"[{'label': 'POSITIVE', 'score': 0.9598047137260437}]",highlighted:'[{<span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;POSITIVE&#x27;</span>, <span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.9598047137260437</span>}]'}}),He=new O({props:{code:`classifier(
    ["I've been waiting for a HuggingFace course my whole life.", "I hate this so much!"]
)`,highlighted:`classifier(
    [<span class="hljs-string">&quot;I&#x27;ve been waiting for a HuggingFace course my whole life.&quot;</span>, <span class="hljs-string">&quot;I hate this so much!&quot;</span>]
)`}}),Te=new O({props:{code:`[{'label': 'POSITIVE', 'score': 0.9598047137260437},
 {'label': 'NEGATIVE', 'score': 0.9994558095932007}]`,highlighted:`[{<span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;POSITIVE&#x27;</span>, <span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.9598047137260437</span>},
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classifier = pipeline("zero-shot-classification")
classifier(
    "This is a course about the Transformers library",
    candidate_labels=["education", "politics", "business"],
)`,highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline
classifier = pipeline(<span class="hljs-string">&quot;zero-shot-classification&quot;</span>)
classifier(
    <span class="hljs-string">&quot;This is a course about the Transformers library&quot;</span>,
    candidate_labels=[<span class="hljs-string">&quot;education&quot;</span>, <span class="hljs-string">&quot;politics&quot;</span>, <span class="hljs-string">&quot;business&quot;</span>],
)`}}),Se=new O({props:{code:`{'sequence': 'This is a course about the Transformers library',
 'labels': ['education', 'business', 'politics'],
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generator = pipeline("text-generation")
generator("In this course, we will teach you how to")`,highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline
generator = pipeline(<span class="hljs-string">&quot;text-generation&quot;</span>)
generator(<span class="hljs-string">&quot;In this course, we will teach you how to&quot;</span>)`}}),Fe=new O({props:{code:`[{'generated_text': 'In this course, we will teach you how to understand and use '
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generator = pipeline("text-generation", model="distilgpt2")
generator(
    "In this course, we will teach you how to",
    max_length=30,
    num_return_sequences=2,
)`,highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline
generator = pipeline(<span class="hljs-string">&quot;text-generation&quot;</span>, model=<span class="hljs-string">&quot;distilgpt2&quot;</span>)
generator(
    <span class="hljs-string">&quot;In this course, we will teach you how to&quot;</span>,
    max_length=<span class="hljs-number">30</span>,
    num_return_sequences=<span class="hljs-number">2</span>,
)`}}),Be=new O({props:{code:`[{'generated_text': 'In this course, we will teach you how to manipulate the world and '
                    'move your mental and physical capabilities to your advantage.'},
 {'generated_text': 'In this course, we will teach you how to become an expert and '
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                    'time and real'}]`,highlighted:`[{<span class="hljs-string">&#x27;generated_text&#x27;</span>: <span class="hljs-string">&#x27;In this course, we will teach you how to manipulate the world and &#x27;</span>
                    <span class="hljs-string">&#x27;move your mental and physical capabilities to your advantage.&#x27;</span>},
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unmasker = pipeline("fill-mask")
unmasker("This course will teach you all about <mask> models.", top_k=2)`,highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline
unmasker = pipeline(<span class="hljs-string">&quot;fill-mask&quot;</span>)
unmasker(<span class="hljs-string">&quot;This course will teach you all about &lt;mask&gt; models.&quot;</span>, top_k=<span class="hljs-number">2</span>)`}}),Ze=new O({props:{code:`[{'sequence': 'This course will teach you all about mathematical models.',
  'score': 0.19619831442832947,
  'token': 30412,
  'token_str': ' mathematical'},
 {'sequence': 'This course will teach you all about computational models.',
  'score': 0.04052725434303284,
  'token': 38163,
  'token_str': ' computational'}]`,highlighted:`[{<span class="hljs-string">&#x27;sequence&#x27;</span>: <span class="hljs-string">&#x27;This course will teach you all about mathematical models.&#x27;</span>,
  <span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.19619831442832947</span>,
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  <span class="hljs-string">&#x27;token_str&#x27;</span>: <span class="hljs-string">&#x27; mathematical&#x27;</span>},
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  <span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.04052725434303284</span>,
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ner = pipeline("ner", grouped_entities=True)
ner("My name is Sylvain and I work at Hugging Face in Brooklyn.")`,highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline
ner = pipeline(<span class="hljs-string">&quot;ner&quot;</span>, grouped_entities=<span class="hljs-literal">True</span>)
ner(<span class="hljs-string">&quot;My name is Sylvain and I work at Hugging Face in Brooklyn.&quot;</span>)`}}),Xe=new O({props:{code:`[{'entity_group': 'PER', 'score': 0.99816, 'word': 'Sylvain', 'start': 11, 'end': 18}, 
 {'entity_group': 'ORG', 'score': 0.97960, 'word': 'Hugging Face', 'start': 33, 'end': 45}, 
 {'entity_group': 'LOC', 'score': 0.99321, 'word': 'Brooklyn', 'start': 49, 'end': 57}
]`,highlighted:`[{<span class="hljs-string">&#x27;entity_group&#x27;</span>: <span class="hljs-string">&#x27;PER&#x27;</span>, <span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.99816</span>, <span class="hljs-string">&#x27;word&#x27;</span>: <span class="hljs-string">&#x27;Sylvain&#x27;</span>, <span class="hljs-string">&#x27;start&#x27;</span>: <span class="hljs-number">11</span>, <span class="hljs-string">&#x27;end&#x27;</span>: <span class="hljs-number">18</span>}, 
 {<span class="hljs-string">&#x27;entity_group&#x27;</span>: <span class="hljs-string">&#x27;ORG&#x27;</span>, <span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.97960</span>, <span class="hljs-string">&#x27;word&#x27;</span>: <span class="hljs-string">&#x27;Hugging Face&#x27;</span>, <span class="hljs-string">&#x27;start&#x27;</span>: <span class="hljs-number">33</span>, <span class="hljs-string">&#x27;end&#x27;</span>: <span class="hljs-number">45</span>}, 
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]`}}),ve=new Ee({props:{$$slots:{default:[Do]},$$scope:{ctx:A}}}),ss=new O({props:{code:`from transformers import pipeline
question_answerer = pipeline("question-answering")
question_answerer(
    question="Where do I work?",
    context="My name is Sylvain and I work at Hugging Face in Brooklyn",
)`,highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline
question_answerer = pipeline(<span class="hljs-string">&quot;question-answering&quot;</span>)
question_answerer(
    question=<span class="hljs-string">&quot;Where do I work?&quot;</span>,
    context=<span class="hljs-string">&quot;My name is Sylvain and I work at Hugging Face in Brooklyn&quot;</span>,
)`}}),ts=new O({props:{code:"{'score': 0.6385916471481323, 'start': 33, 'end': 45, 'answer': 'Hugging Face'}",highlighted:'{<span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.6385916471481323</span>, <span class="hljs-string">&#x27;start&#x27;</span>: <span class="hljs-number">33</span>, <span class="hljs-string">&#x27;end&#x27;</span>: <span class="hljs-number">45</span>, <span class="hljs-string">&#x27;answer&#x27;</span>: <span class="hljs-string">&#x27;Hugging Face&#x27;</span>}'}}),as=new O({props:{code:`from transformers import pipeline
summarizer = pipeline("summarization")
summarizer(
    """
    America has changed dramatically during recent years. Not only has the number of 
    graduates in traditional engineering disciplines such as mechanical, civil, 
    electrical, chemical, and aeronautical engineering declined, but in most of 
    the premier American universities engineering curricula now concentrate on 
    and encourage largely the study of engineering science. As a result, there 
    are declining offerings in engineering subjects dealing with infrastructure, 
    the environment, and related issues, and greater concentration on high 
    technology subjects, largely supporting increasingly complex scientific 
    developments. While the latter is important, it should not be at the expense 
    of more traditional engineering.
    Rapidly developing economies such as China and India, as well as other 
    industrial countries in Europe and Asia, continue to encourage and advance 
    the teaching of engineering. Both China and India, respectively, graduate 
    six and eight times as many traditional engineers as does the United States. 
    Other industrial countries at minimum maintain their output, while America 
    suffers an increasingly serious decline in the number of engineering graduates 
    and a lack of well-educated engineers.
"""
)`,highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline
summarizer = pipeline(<span class="hljs-string">&quot;summarization&quot;</span>)
summarizer(
    <span class="hljs-string">&quot;&quot;&quot;
    America has changed dramatically during recent years. Not only has the number of 
    graduates in traditional engineering disciplines such as mechanical, civil, 
    electrical, chemical, and aeronautical engineering declined, but in most of 
    the premier American universities engineering curricula now concentrate on 
    and encourage largely the study of engineering science. As a result, there 
    are declining offerings in engineering subjects dealing with infrastructure, 
    the environment, and related issues, and greater concentration on high 
    technology subjects, largely supporting increasingly complex scientific 
    developments. While the latter is important, it should not be at the expense 
    of more traditional engineering.
    Rapidly developing economies such as China and India, as well as other 
    industrial countries in Europe and Asia, continue to encourage and advance 
    the teaching of engineering. Both China and India, respectively, graduate 
    six and eight times as many traditional engineers as does the United States. 
    Other industrial countries at minimum maintain their output, while America 
    suffers an increasingly serious decline in the number of engineering graduates 
    and a lack of well-educated engineers.
&quot;&quot;&quot;</span>
)`}}),ns=new O({props:{code:`[{'summary_text': ' America has changed dramatically during recent years . The '
                  'number of engineering graduates in the U.S. has declined in '
                  'traditional engineering disciplines such as mechanical, civil '
                  ', electrical, chemical, and aeronautical engineering . Rapidly '
                  'developing economies such as China and India, as well as other '
                  'industrial countries in Europe and Asia, continue to encourage '
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translator = pipeline("translation", model="Helsinki-NLP/opus-mt-fr-en")
translator("Ce cours est produit par Hugging Face.")`,highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline
translator = pipeline(<span class="hljs-string">&quot;translation&quot;</span>, model=<span class="hljs-string">&quot;Helsinki-NLP/opus-mt-fr-en&quot;</span>)
translator(<span class="hljs-string">&quot;Ce cours est produit par Hugging Face.&quot;</span>)`}}),ps=new O({props:{code:"[{'translation_text': 'This course is produced by Hugging Face.'}]",highlighted:'[{<span class="hljs-string">&#x27;translation_text&#x27;</span>: <span class="hljs-string">&#x27;This course is produced by Hugging Face.&#x27;</span>}]'}}),xe=new Ee({props:{$$slots:{default:[No]},$$scope:{ctx:A}}}),{c(){u=i("meta"),_=f(),h=i("h1"),v=i("a"),g=i("span"),x(m.$$.fragment),d=f(),q=i("span"),C=a("Transformers\u3067\u4F55\u304C\u3067\u304D\u308B?"),P=f(),x(I.$$.fragment),T=f(),k=i("p"),V=a("\u3053\u306E\u30BB\u30AF\u30B7\u30E7\u30F3\u3067\u306F\u3001Transformer\u30E2\u30C7\u30EB\u304C\u3067\u304D\u308B\u3053\u3068\u3092\u898B\u3066\u3044\u304D\u3001\u{1F917} Transformers\u30E9\u30A4\u30D6\u30E9\u30EA\u306E\u6700\u521D\u306E\u30C4\u30FC\u30EB\u3068\u3057\u3066 "),F=i("code"),W=a("pipeline()"),xa=a(" 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