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<h1 class="relative group"><a id="summary-of-the-tasks" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#summary-of-the-tasks"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
	<span>Summary of the tasks
	</span></h1>



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<p>This page shows the most frequent use-cases when using the library. The models available allow for many different
configurations and a great versatility in use-cases. The most simple ones are presented here, showcasing usage for
tasks such as image classification, question answering, sequence classification, named entity recognition and others.</p>
<p>These examples leverage auto-models, which are classes that will instantiate a model according to a given checkpoint,
automatically selecting the correct model architecture. Please check the <a href="/docs/transformers/pr_19043/en/model_doc/auto#transformers.AutoModel">AutoModel</a> documentation
for more information. Feel free to modify the code to be more specific and adapt it to your specific use-case.</p>
<p>In order for a model to perform well on a task, it must be loaded from a checkpoint corresponding to that task. These
checkpoints are usually pre-trained on a large corpus of data and fine-tuned on a specific task. This means the
following:</p>
<ul><li>Not all models were fine-tuned on all tasks. If you want to fine-tune a model on a specific task, you can leverage
one of the <em>run_$TASK.py</em> scripts in the <a href="https://github.com/huggingface/transformers/tree/main/examples" rel="nofollow">examples</a> directory.</li>
<li>Fine-tuned models were fine-tuned on a specific dataset. This dataset may or may not overlap with your use-case and
domain. As mentioned previously, you may leverage the <a href="https://github.com/huggingface/transformers/tree/main/examples" rel="nofollow">examples</a> scripts to fine-tune your model, or you may
create your own training script.</li></ul>
<p>In order to do an inference on a task, several mechanisms are made available by the library:</p>
<ul><li>Pipelines: very easy-to-use abstractions, which require as little as two lines of code.</li>
<li>Direct model use: Less abstractions, but more flexibility and power via a direct access to a tokenizer
(PyTorch/TensorFlow) and full inference capacity.</li></ul>
<p>Both approaches are showcased here.</p>


<div class="course-tip  bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>All tasks presented here leverage pre-trained checkpoints that were fine-tuned on specific tasks. Loading a
checkpoint that was not fine-tuned on a specific task would load only the base transformer layers and not the
additional head that is used for the task, initializing the weights of that head randomly.</p>
<p>This would produce random output.</p></div>
<h2 class="relative group"><a id="sequence-classification" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#sequence-classification"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
	<span>Sequence Classification
	</span></h2>

<p>Sequence classification is the task of classifying sequences according to a given number of classes. An example of
sequence classification is the GLUE dataset, which is entirely based on that task. If you would like to fine-tune a
model on a GLUE sequence classification task, you may leverage the <a href="https://github.com/huggingface/transformers/tree/main/examples/pytorch/text-classification/run_glue.py" rel="nofollow">run_glue.py</a>, <a href="https://github.com/huggingface/transformers/tree/main/examples/tensorflow/text-classification/run_tf_glue.py" rel="nofollow">run_tf_glue.py</a>, <a href="https://github.com/huggingface/transformers/tree/main/examples/tensorflow/text-classification/run_tf_text_classification.py" rel="nofollow">run_tf_text_classification.py</a> or <a href="https://github.com/huggingface/transformers/tree/main/examples/pytorch/text-classification/run_xnli.py" rel="nofollow">run_xnli.py</a> scripts.</p>
<p>Here is an example of using pipelines to do sentiment analysis: identifying if a sequence is positive or negative. It
leverages a fine-tuned model on sst2, which is a GLUE task.</p>
<p>This returns a label (“POSITIVE” or “NEGATIVE”) alongside a score, as follows:</p>

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	<pre><!-- HTML_TAG_START --><span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline

<span class="hljs-meta">&gt;&gt;&gt; </span>classifier = pipeline(<span class="hljs-string">&quot;sentiment-analysis&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>result = classifier(<span class="hljs-string">&quot;I hate you&quot;</span>)[<span class="hljs-number">0</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;label: <span class="hljs-subst">{result[<span class="hljs-string">&#x27;label&#x27;</span>]}</span>, with score: <span class="hljs-subst">{<span class="hljs-built_in">round</span>(result[<span class="hljs-string">&#x27;score&#x27;</span>], <span class="hljs-number">4</span>)}</span>&quot;</span>)
label: NEGATIVE, <span class="hljs-keyword">with</span> score: <span class="hljs-number">0.9991</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>result = classifier(<span class="hljs-string">&quot;I love you&quot;</span>)[<span class="hljs-number">0</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;label: <span class="hljs-subst">{result[<span class="hljs-string">&#x27;label&#x27;</span>]}</span>, with score: <span class="hljs-subst">{<span class="hljs-built_in">round</span>(result[<span class="hljs-string">&#x27;score&#x27;</span>], <span class="hljs-number">4</span>)}</span>&quot;</span>)
label: POSITIVE, <span class="hljs-keyword">with</span> score: <span class="hljs-number">0.9999</span><!-- HTML_TAG_END --></pre></div>
<p>Here is an example of doing a sequence classification using a model to determine if two sequences are paraphrases of
each other. The process is the following:</p>
<ol><li>Instantiate a tokenizer and a model from the checkpoint name. The model is identified as a BERT model and loads it
with the weights stored in the checkpoint.</li>
<li>Build a sequence from the two sentences, with the correct model-specific separators, token type ids and attention
masks (which will be created automatically by the tokenizer).</li>
<li>Pass this sequence through the model so that it is classified in one of the two available classes: 0 (not a
paraphrase) and 1 (is a paraphrase).</li>
<li>Compute the softmax of the result to get probabilities over the classes.</li>
<li>Print the results.</li></ol>
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	<pre><!-- HTML_TAG_START --><span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, AutoModelForSequenceClassification
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;bert-base-cased-finetuned-mrpc&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;bert-base-cased-finetuned-mrpc&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>classes = [<span class="hljs-string">&quot;not paraphrase&quot;</span>, <span class="hljs-string">&quot;is paraphrase&quot;</span>]

<span class="hljs-meta">&gt;&gt;&gt; </span>sequence_0 = <span class="hljs-string">&quot;The company HuggingFace is based in New York City&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>sequence_1 = <span class="hljs-string">&quot;Apples are especially bad for your health&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>sequence_2 = <span class="hljs-string">&quot;HuggingFace&#x27;s headquarters are situated in Manhattan&quot;</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># The tokenizer will automatically add any model specific separators (i.e. &lt;CLS&gt; and &lt;SEP&gt;) and tokens to</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># the sequence, as well as compute the attention masks.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>paraphrase = tokenizer(sequence_0, sequence_2, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>not_paraphrase = tokenizer(sequence_0, sequence_1, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>paraphrase_classification_logits = model(**paraphrase).logits
<span class="hljs-meta">&gt;&gt;&gt; </span>not_paraphrase_classification_logits = model(**not_paraphrase).logits

<span class="hljs-meta">&gt;&gt;&gt; </span>paraphrase_results = torch.softmax(paraphrase_classification_logits, dim=<span class="hljs-number">1</span>).tolist()[<span class="hljs-number">0</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>not_paraphrase_results = torch.softmax(not_paraphrase_classification_logits, dim=<span class="hljs-number">1</span>).tolist()[<span class="hljs-number">0</span>]

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Should be paraphrase</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">for</span> i <span class="hljs-keyword">in</span> <span class="hljs-built_in">range</span>(<span class="hljs-built_in">len</span>(classes)):
<span class="hljs-meta">... </span>    <span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;<span class="hljs-subst">{classes[i]}</span>: <span class="hljs-subst">{<span class="hljs-built_in">int</span>(<span class="hljs-built_in">round</span>(paraphrase_results[i] * <span class="hljs-number">100</span>))}</span>%&quot;</span>)
<span class="hljs-keyword">not</span> paraphrase: <span class="hljs-number">10</span>%
<span class="hljs-keyword">is</span> paraphrase: <span class="hljs-number">90</span>%

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Should not be paraphrase</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">for</span> i <span class="hljs-keyword">in</span> <span class="hljs-built_in">range</span>(<span class="hljs-built_in">len</span>(classes)):
<span class="hljs-meta">... </span>    <span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;<span class="hljs-subst">{classes[i]}</span>: <span class="hljs-subst">{<span class="hljs-built_in">int</span>(<span class="hljs-built_in">round</span>(not_paraphrase_results[i] * <span class="hljs-number">100</span>))}</span>%&quot;</span>)
<span class="hljs-keyword">not</span> paraphrase: <span class="hljs-number">94</span>%
<span class="hljs-keyword">is</span> paraphrase: <span class="hljs-number">6</span>%<!-- HTML_TAG_END --></pre></div></div></div>
	

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			<span>TensorFlow</span></div>
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	<pre><!-- HTML_TAG_START --><span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, TFAutoModelForSequenceClassification
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;bert-base-cased-finetuned-mrpc&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;bert-base-cased-finetuned-mrpc&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>classes = [<span class="hljs-string">&quot;not paraphrase&quot;</span>, <span class="hljs-string">&quot;is paraphrase&quot;</span>]

<span class="hljs-meta">&gt;&gt;&gt; </span>sequence_0 = <span class="hljs-string">&quot;The company HuggingFace is based in New York City&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>sequence_1 = <span class="hljs-string">&quot;Apples are especially bad for your health&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>sequence_2 = <span class="hljs-string">&quot;HuggingFace&#x27;s headquarters are situated in Manhattan&quot;</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># The tokenizer will automatically add any model specific separators (i.e. &lt;CLS&gt; and &lt;SEP&gt;) and tokens to</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># the sequence, as well as compute the attention masks.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>paraphrase = tokenizer(sequence_0, sequence_2, return_tensors=<span class="hljs-string">&quot;tf&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>not_paraphrase = tokenizer(sequence_0, sequence_1, return_tensors=<span class="hljs-string">&quot;tf&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>paraphrase_classification_logits = model(paraphrase).logits
<span class="hljs-meta">&gt;&gt;&gt; </span>not_paraphrase_classification_logits = model(not_paraphrase).logits

<span class="hljs-meta">&gt;&gt;&gt; </span>paraphrase_results = tf.nn.softmax(paraphrase_classification_logits, axis=<span class="hljs-number">1</span>).numpy()[<span class="hljs-number">0</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>not_paraphrase_results = tf.nn.softmax(not_paraphrase_classification_logits, axis=<span class="hljs-number">1</span>).numpy()[<span class="hljs-number">0</span>]

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Should be paraphrase</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">for</span> i <span class="hljs-keyword">in</span> <span class="hljs-built_in">range</span>(<span class="hljs-built_in">len</span>(classes)):
<span class="hljs-meta">... </span>    <span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;<span class="hljs-subst">{classes[i]}</span>: <span class="hljs-subst">{<span class="hljs-built_in">int</span>(<span class="hljs-built_in">round</span>(paraphrase_results[i] * <span class="hljs-number">100</span>))}</span>%&quot;</span>)
<span class="hljs-keyword">not</span> paraphrase: <span class="hljs-number">10</span>%
<span class="hljs-keyword">is</span> paraphrase: <span class="hljs-number">90</span>%

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Should not be paraphrase</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">for</span> i <span class="hljs-keyword">in</span> <span class="hljs-built_in">range</span>(<span class="hljs-built_in">len</span>(classes)):
<span class="hljs-meta">... </span>    <span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;<span class="hljs-subst">{classes[i]}</span>: <span class="hljs-subst">{<span class="hljs-built_in">int</span>(<span class="hljs-built_in">round</span>(not_paraphrase_results[i] * <span class="hljs-number">100</span>))}</span>%&quot;</span>)
<span class="hljs-keyword">not</span> paraphrase: <span class="hljs-number">94</span>%
<span class="hljs-keyword">is</span> paraphrase: <span class="hljs-number">6</span>%<!-- HTML_TAG_END --></pre></div>
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<h2 class="relative group"><a id="extractive-question-answering" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#extractive-question-answering"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
	<span>Extractive Question Answering
	</span></h2>

<p>Extractive Question Answering is the task of extracting an answer from a text given a question. An example of a
question answering dataset is the SQuAD dataset, which is entirely based on that task. If you would like to fine-tune a
model on a SQuAD task, you may leverage the <a href="https://github.com/huggingface/transformers/tree/main/examples/pytorch/question-answering/run_qa.py" rel="nofollow">run_qa.py</a> and
<a href="https://github.com/huggingface/transformers/tree/main/examples/tensorflow/question-answering/run_tf_squad.py" rel="nofollow">run_tf_squad.py</a>
scripts.</p>
<p>Here is an example of using pipelines to do question answering: extracting an answer from a text given a question. It
leverages a fine-tuned model on SQuAD.</p>

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	<pre><!-- HTML_TAG_START --><span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline

<span class="hljs-meta">&gt;&gt;&gt; </span>question_answerer = pipeline(<span class="hljs-string">&quot;question-answering&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>context = <span class="hljs-string">r&quot;&quot;&quot;
<span class="hljs-meta">... </span>Extractive Question Answering is the task of extracting an answer from a text given a question. An example of a
<span class="hljs-meta">... </span>question answering dataset is the SQuAD dataset, which is entirely based on that task. If you would like to fine-tune
<span class="hljs-meta">... </span>a model on a SQuAD task, you may leverage the examples/pytorch/question-answering/run_squad.py script.
<span class="hljs-meta">... </span>&quot;&quot;&quot;</span><!-- HTML_TAG_END --></pre></div>
<p>This returns an answer extracted from the text, a confidence score, alongside “start” and “end” values, which are the
positions of the extracted answer in the text.</p>

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	<pre><!-- HTML_TAG_START --><span class="hljs-meta">&gt;&gt;&gt; </span>result = question_answerer(question=<span class="hljs-string">&quot;What is extractive question answering?&quot;</span>, context=context)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(
<span class="hljs-meta">... </span>    <span class="hljs-string">f&quot;Answer: &#x27;<span class="hljs-subst">{result[<span class="hljs-string">&#x27;answer&#x27;</span>]}</span>&#x27;, score: <span class="hljs-subst">{<span class="hljs-built_in">round</span>(result[<span class="hljs-string">&#x27;score&#x27;</span>], <span class="hljs-number">4</span>)}</span>, start: <span class="hljs-subst">{result[<span class="hljs-string">&#x27;start&#x27;</span>]}</span>, end: <span class="hljs-subst">{result[<span class="hljs-string">&#x27;end&#x27;</span>]}</span>&quot;</span>
<span class="hljs-meta">... </span>)
Answer: <span class="hljs-string">&#x27;the task of extracting an answer from a text given a question&#x27;</span>, score: <span class="hljs-number">0.6177</span>, start: <span class="hljs-number">34</span>, end: <span class="hljs-number">95</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>result = question_answerer(question=<span class="hljs-string">&quot;What is a good example of a question answering dataset?&quot;</span>, context=context)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(
<span class="hljs-meta">... </span>    <span class="hljs-string">f&quot;Answer: &#x27;<span class="hljs-subst">{result[<span class="hljs-string">&#x27;answer&#x27;</span>]}</span>&#x27;, score: <span class="hljs-subst">{<span class="hljs-built_in">round</span>(result[<span class="hljs-string">&#x27;score&#x27;</span>], <span class="hljs-number">4</span>)}</span>, start: <span class="hljs-subst">{result[<span class="hljs-string">&#x27;start&#x27;</span>]}</span>, end: <span class="hljs-subst">{result[<span class="hljs-string">&#x27;end&#x27;</span>]}</span>&quot;</span>
<span class="hljs-meta">... </span>)
Answer: <span class="hljs-string">&#x27;SQuAD dataset&#x27;</span>, score: <span class="hljs-number">0.5152</span>, start: <span class="hljs-number">147</span>, end: <span class="hljs-number">160</span><!-- HTML_TAG_END --></pre></div>
<p>Here is an example of question answering using a model and a tokenizer. The process is the following:</p>
<ol><li>Instantiate a tokenizer and a model from the checkpoint name. The model is identified as a BERT model and loads it
with the weights stored in the checkpoint.</li>
<li>Define a text and a few questions.</li>
<li>Iterate over the questions and build a sequence from the text and the current question, with the correct
model-specific separators, token type ids and attention masks.</li>
<li>Pass this sequence through the model. This outputs a range of scores across the entire sequence tokens (question and
text), for both the start and end positions.</li>
<li>Compute the softmax of the result to get probabilities over the tokens.</li>
<li>Fetch the tokens from the identified start and stop values, convert those tokens to a string.</li>
<li>Print the results.</li></ol>
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	Copied</div></button></div>
	<pre><!-- HTML_TAG_START --><span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, AutoModelForQuestionAnswering
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;bert-large-uncased-whole-word-masking-finetuned-squad&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;bert-large-uncased-whole-word-masking-finetuned-squad&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>text = <span class="hljs-string">r&quot;&quot;&quot;
<span class="hljs-meta">... </span>🤗 Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides general-purpose
<span class="hljs-meta">... </span>architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet…) for Natural Language Understanding (NLU) and Natural
<span class="hljs-meta">... </span>Language Generation (NLG) with over 32+ pretrained models in 100+ languages and deep interoperability between
<span class="hljs-meta">... </span>TensorFlow 2.0 and PyTorch.
<span class="hljs-meta">... </span>&quot;&quot;&quot;</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>questions = [
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;How many pretrained models are available in 🤗 Transformers?&quot;</span>,
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;What does 🤗 Transformers provide?&quot;</span>,
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;🤗 Transformers provides interoperability between which frameworks?&quot;</span>,
<span class="hljs-meta">... </span>]

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">for</span> question <span class="hljs-keyword">in</span> questions:
<span class="hljs-meta">... </span>    inputs = tokenizer(question, text, add_special_tokens=<span class="hljs-literal">True</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
<span class="hljs-meta">... </span>    input_ids = inputs[<span class="hljs-string">&quot;input_ids&quot;</span>].tolist()[<span class="hljs-number">0</span>]

<span class="hljs-meta">... </span>    outputs = model(**inputs)
<span class="hljs-meta">... </span>    answer_start_scores = outputs.start_logits
<span class="hljs-meta">... </span>    answer_end_scores = outputs.end_logits

<span class="hljs-meta">... </span>    <span class="hljs-comment"># Get the most likely beginning of answer with the argmax of the score</span>
<span class="hljs-meta">... </span>    answer_start = torch.argmax(answer_start_scores)
<span class="hljs-meta">... </span>    <span class="hljs-comment"># Get the most likely end of answer with the argmax of the score</span>
<span class="hljs-meta">... </span>    answer_end = torch.argmax(answer_end_scores) + <span class="hljs-number">1</span>

<span class="hljs-meta">... </span>    answer = tokenizer.convert_tokens_to_string(
<span class="hljs-meta">... </span>        tokenizer.convert_ids_to_tokens(input_ids[answer_start:answer_end])
<span class="hljs-meta">... </span>    )

<span class="hljs-meta">... </span>    <span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;Question: <span class="hljs-subst">{question}</span>&quot;</span>)
<span class="hljs-meta">... </span>    <span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;Answer: <span class="hljs-subst">{answer}</span>&quot;</span>)
Question: How many pretrained models are available <span class="hljs-keyword">in</span> 🤗 Transformers?
Answer: over <span class="hljs-number">32</span> +
Question: What does 🤗 Transformers provide?
Answer: general - purpose architectures
Question: 🤗 Transformers provides interoperability between which frameworks?
Answer: tensorflow <span class="hljs-number">2.</span> <span class="hljs-number">0</span> <span class="hljs-keyword">and</span> pytorch<!-- HTML_TAG_END --></pre></div></div></div>
	

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	<pre><!-- HTML_TAG_START --><span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, TFAutoModelForQuestionAnswering
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;bert-large-uncased-whole-word-masking-finetuned-squad&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;bert-large-uncased-whole-word-masking-finetuned-squad&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>text = <span class="hljs-string">r&quot;&quot;&quot;
<span class="hljs-meta">... </span>🤗 Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides general-purpose
<span class="hljs-meta">... </span>architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet…) for Natural Language Understanding (NLU) and Natural
<span class="hljs-meta">... </span>Language Generation (NLG) with over 32+ pretrained models in 100+ languages and deep interoperability between
<span class="hljs-meta">... </span>TensorFlow 2.0 and PyTorch.
<span class="hljs-meta">... </span>&quot;&quot;&quot;</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>questions = [
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;How many pretrained models are available in 🤗 Transformers?&quot;</span>,
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;What does 🤗 Transformers provide?&quot;</span>,
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;🤗 Transformers provides interoperability between which frameworks?&quot;</span>,
<span class="hljs-meta">... </span>]

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">for</span> question <span class="hljs-keyword">in</span> questions:
<span class="hljs-meta">... </span>    inputs = tokenizer(question, text, add_special_tokens=<span class="hljs-literal">True</span>, return_tensors=<span class="hljs-string">&quot;tf&quot;</span>)
<span class="hljs-meta">... </span>    input_ids = inputs[<span class="hljs-string">&quot;input_ids&quot;</span>].numpy()[<span class="hljs-number">0</span>]

<span class="hljs-meta">... </span>    outputs = model(inputs)
<span class="hljs-meta">... </span>    answer_start_scores = outputs.start_logits
<span class="hljs-meta">... </span>    answer_end_scores = outputs.end_logits

<span class="hljs-meta">... </span>    <span class="hljs-comment"># Get the most likely beginning of answer with the argmax of the score</span>
<span class="hljs-meta">... </span>    answer_start = tf.argmax(answer_start_scores, axis=<span class="hljs-number">1</span>).numpy()[<span class="hljs-number">0</span>]
<span class="hljs-meta">... </span>    <span class="hljs-comment"># Get the most likely end of answer with the argmax of the score</span>
<span class="hljs-meta">... </span>    answer_end = tf.argmax(answer_end_scores, axis=<span class="hljs-number">1</span>).numpy()[<span class="hljs-number">0</span>] + <span class="hljs-number">1</span>

<span class="hljs-meta">... </span>    answer = tokenizer.convert_tokens_to_string(
<span class="hljs-meta">... </span>        tokenizer.convert_ids_to_tokens(input_ids[answer_start:answer_end])
<span class="hljs-meta">... </span>    )

<span class="hljs-meta">... </span>    <span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;Question: <span class="hljs-subst">{question}</span>&quot;</span>)
<span class="hljs-meta">... </span>    <span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;Answer: <span class="hljs-subst">{answer}</span>&quot;</span>)
Question: How many pretrained models are available <span class="hljs-keyword">in</span> 🤗 Transformers?
Answer: over <span class="hljs-number">32</span> +
Question: What does 🤗 Transformers provide?
Answer: general - purpose architectures
Question: 🤗 Transformers provides interoperability between which frameworks?
Answer: tensorflow <span class="hljs-number">2.</span> <span class="hljs-number">0</span> <span class="hljs-keyword">and</span> pytorch<!-- HTML_TAG_END --></pre></div>
					</div></div>
	</div>
<h2 class="relative group"><a id="language-modeling" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#language-modeling"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
	<span>Language Modeling
	</span></h2>

<p>Language modeling is the task of fitting a model to a corpus, which can be domain specific. All popular
transformer-based models are trained using a variant of language modeling, e.g. BERT with masked language modeling,
GPT-2 with causal language modeling.</p>
<p>Language modeling can be useful outside of pretraining as well, for example to shift the model distribution to be
domain-specific: using a language model trained over a very large corpus, and then fine-tuning it to a news dataset or
on scientific papers e.g. <a href="https://huggingface.co/lysandre/arxiv-nlp" rel="nofollow">LysandreJik/arxiv-nlp</a>.</p>
<h3 class="relative group"><a id="masked-language-modeling" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#masked-language-modeling"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
	<span>Masked Language Modeling
	</span></h3>

<p>Masked language modeling is the task of masking tokens in a sequence with a masking token, and prompting the model to
fill that mask with an appropriate token. This allows the model to attend to both the right context (tokens on the
right of the mask) and the left context (tokens on the left of the mask). Such a training creates a strong basis for
downstream tasks requiring bi-directional context, such as SQuAD (question answering, see <a href="https://arxiv.org/abs/1910.13461" rel="nofollow">Lewis, Lui, Goyal et al.</a>, part 4.2). If you would like to fine-tune a model on a masked language modeling
task, you may leverage the <a href="https://github.com/huggingface/transformers/tree/main/examples/pytorch/language-modeling/run_mlm.py" rel="nofollow">run_mlm.py</a> script.</p>
<p>Here is an example of using pipelines to replace a mask from a sequence:</p>

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	<pre><!-- HTML_TAG_START --><span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline

<span class="hljs-meta">&gt;&gt;&gt; </span>unmasker = pipeline(<span class="hljs-string">&quot;fill-mask&quot;</span>)<!-- HTML_TAG_END --></pre></div>
<p>This outputs the sequences with the mask filled, the confidence score, and the token id in the tokenizer vocabulary:</p>

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	<pre><!-- HTML_TAG_START --><span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> pprint <span class="hljs-keyword">import</span> pprint

<span class="hljs-meta">&gt;&gt;&gt; </span>pprint(
<span class="hljs-meta">... </span>    unmasker(
<span class="hljs-meta">... </span>        <span class="hljs-string">f&quot;HuggingFace is creating a <span class="hljs-subst">{unmasker.tokenizer.mask_token}</span> that the community uses to solve NLP tasks.&quot;</span>
<span class="hljs-meta">... </span>    )
<span class="hljs-meta">... </span>)
[{<span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.1793</span>,
  <span class="hljs-string">&#x27;sequence&#x27;</span>: <span class="hljs-string">&#x27;HuggingFace is creating a tool that the community uses to solve &#x27;</span>
              <span class="hljs-string">&#x27;NLP tasks.&#x27;</span>,
  <span class="hljs-string">&#x27;token&#x27;</span>: <span class="hljs-number">3944</span>,
  <span class="hljs-string">&#x27;token_str&#x27;</span>: <span class="hljs-string">&#x27; tool&#x27;</span>},
 {<span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.1135</span>,
  <span class="hljs-string">&#x27;sequence&#x27;</span>: <span class="hljs-string">&#x27;HuggingFace is creating a framework that the community uses to &#x27;</span>
              <span class="hljs-string">&#x27;solve NLP tasks.&#x27;</span>,
  <span class="hljs-string">&#x27;token&#x27;</span>: <span class="hljs-number">7208</span>,
  <span class="hljs-string">&#x27;token_str&#x27;</span>: <span class="hljs-string">&#x27; framework&#x27;</span>},
 {<span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.0524</span>,
  <span class="hljs-string">&#x27;sequence&#x27;</span>: <span class="hljs-string">&#x27;HuggingFace is creating a library that the community uses to &#x27;</span>
              <span class="hljs-string">&#x27;solve NLP tasks.&#x27;</span>,
  <span class="hljs-string">&#x27;token&#x27;</span>: <span class="hljs-number">5560</span>,
  <span class="hljs-string">&#x27;token_str&#x27;</span>: <span class="hljs-string">&#x27; library&#x27;</span>},
 {<span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.0349</span>,
  <span class="hljs-string">&#x27;sequence&#x27;</span>: <span class="hljs-string">&#x27;HuggingFace is creating a database that the community uses to &#x27;</span>
              <span class="hljs-string">&#x27;solve NLP tasks.&#x27;</span>,
  <span class="hljs-string">&#x27;token&#x27;</span>: <span class="hljs-number">8503</span>,
  <span class="hljs-string">&#x27;token_str&#x27;</span>: <span class="hljs-string">&#x27; database&#x27;</span>},
 {<span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.0286</span>,
  <span class="hljs-string">&#x27;sequence&#x27;</span>: <span class="hljs-string">&#x27;HuggingFace is creating a prototype that the community uses to &#x27;</span>
              <span class="hljs-string">&#x27;solve NLP tasks.&#x27;</span>,
  <span class="hljs-string">&#x27;token&#x27;</span>: <span class="hljs-number">17715</span>,
  <span class="hljs-string">&#x27;token_str&#x27;</span>: <span class="hljs-string">&#x27; prototype&#x27;</span>}]<!-- HTML_TAG_END --></pre></div>
<p>Here is an example of doing masked language modeling using a model and a tokenizer. The process is the following:</p>
<ol><li>Instantiate a tokenizer and a model from the checkpoint name. The model is identified as a DistilBERT model and
loads it with the weights stored in the checkpoint.</li>
<li>Define a sequence with a masked token, placing the <code>tokenizer.mask_token</code> instead of a word.</li>
<li>Encode that sequence into a list of IDs and find the position of the masked token in that list.</li>
<li>Retrieve the predictions at the index of the mask token: this tensor has the same size as the vocabulary, and the
values are the scores attributed to each token. The model gives higher score to tokens it deems probable in that
context.</li>
<li>Retrieve the top 5 tokens using the PyTorch <code>topk</code> or TensorFlow <code>top_k</code> methods.</li>
<li>Replace the mask token by the tokens and print the results</li></ol>
<div class="space-y-10 py-6 2xl:py-8 2xl:-mx-4">

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			<span>Pytorch</span></div>
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	Copied</div></button></div>
	<pre><!-- HTML_TAG_START --><span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForMaskedLM, AutoTokenizer
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;distilbert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForMaskedLM.from_pretrained(<span class="hljs-string">&quot;distilbert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>sequence = (
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;Distilled models are smaller than the models they mimic. Using them instead of the large &quot;</span>
<span class="hljs-meta">... </span>    <span class="hljs-string">f&quot;versions would help <span class="hljs-subst">{tokenizer.mask_token}</span> our carbon footprint.&quot;</span>
<span class="hljs-meta">... </span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(sequence, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>mask_token_index = torch.where(inputs[<span class="hljs-string">&quot;input_ids&quot;</span>] == tokenizer.mask_token_id)[<span class="hljs-number">1</span>]

<span class="hljs-meta">&gt;&gt;&gt; </span>token_logits = model(**inputs).logits
<span class="hljs-meta">&gt;&gt;&gt; </span>mask_token_logits = token_logits[<span class="hljs-number">0</span>, mask_token_index, :]

<span class="hljs-meta">&gt;&gt;&gt; </span>top_5_tokens = torch.topk(mask_token_logits, <span class="hljs-number">5</span>, dim=<span class="hljs-number">1</span>).indices[<span class="hljs-number">0</span>].tolist()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">for</span> token <span class="hljs-keyword">in</span> top_5_tokens:
<span class="hljs-meta">... </span>    <span class="hljs-built_in">print</span>(sequence.replace(tokenizer.mask_token, tokenizer.decode([token])))
Distilled models are smaller than the models they mimic. Using them instead of the large versions would <span class="hljs-built_in">help</span> reduce our carbon footprint.
Distilled models are smaller than the models they mimic. Using them instead of the large versions would <span class="hljs-built_in">help</span> increase our carbon footprint.
Distilled models are smaller than the models they mimic. Using them instead of the large versions would <span class="hljs-built_in">help</span> decrease our carbon footprint.
Distilled models are smaller than the models they mimic. Using them instead of the large versions would <span class="hljs-built_in">help</span> offset our carbon footprint.
Distilled models are smaller than the models they mimic. Using them instead of the large versions would <span class="hljs-built_in">help</span> improve our carbon footprint.<!-- HTML_TAG_END --></pre></div></div></div>
	

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			<span>TensorFlow</span></div>
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	Copied</div></button></div>
	<pre><!-- HTML_TAG_START --><span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFAutoModelForMaskedLM, AutoTokenizer
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;distilbert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForMaskedLM.from_pretrained(<span class="hljs-string">&quot;distilbert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>sequence = (
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;Distilled models are smaller than the models they mimic. Using them instead of the large &quot;</span>
<span class="hljs-meta">... </span>    <span class="hljs-string">f&quot;versions would help <span class="hljs-subst">{tokenizer.mask_token}</span> our carbon footprint.&quot;</span>
<span class="hljs-meta">... </span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(sequence, return_tensors=<span class="hljs-string">&quot;tf&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>mask_token_index = tf.where(inputs[<span class="hljs-string">&quot;input_ids&quot;</span>] == tokenizer.mask_token_id)[<span class="hljs-number">0</span>, <span class="hljs-number">1</span>]

<span class="hljs-meta">&gt;&gt;&gt; </span>token_logits = model(**inputs).logits
<span class="hljs-meta">&gt;&gt;&gt; </span>mask_token_logits = token_logits[<span class="hljs-number">0</span>, mask_token_index, :]

<span class="hljs-meta">&gt;&gt;&gt; </span>top_5_tokens = tf.math.top_k(mask_token_logits, <span class="hljs-number">5</span>).indices.numpy()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">for</span> token <span class="hljs-keyword">in</span> top_5_tokens:
<span class="hljs-meta">... </span>    <span class="hljs-built_in">print</span>(sequence.replace(tokenizer.mask_token, tokenizer.decode([token])))
Distilled models are smaller than the models they mimic. Using them instead of the large versions would <span class="hljs-built_in">help</span> reduce our carbon footprint.
Distilled models are smaller than the models they mimic. Using them instead of the large versions would <span class="hljs-built_in">help</span> increase our carbon footprint.
Distilled models are smaller than the models they mimic. Using them instead of the large versions would <span class="hljs-built_in">help</span> decrease our carbon footprint.
Distilled models are smaller than the models they mimic. Using them instead of the large versions would <span class="hljs-built_in">help</span> offset our carbon footprint.
Distilled models are smaller than the models they mimic. Using them instead of the large versions would <span class="hljs-built_in">help</span> improve our carbon footprint.<!-- HTML_TAG_END --></pre></div>
					</div></div>
	</div>
<p>This prints five sequences, with the top 5 tokens predicted by the model.</p>
<h3 class="relative group"><a id="causal-language-modeling" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#causal-language-modeling"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
	<span>Causal Language Modeling
	</span></h3>

<p>Causal language modeling is the task of predicting the token following a sequence of tokens. In this situation, the
model only attends to the left context (tokens on the left of the mask). Such a training is particularly interesting
for generation tasks. If you would like to fine-tune a model on a causal language modeling task, you may leverage the
<a href="https://github.com/huggingface/transformers/tree/main/examples/pytorch/language-modeling/run_clm.py" rel="nofollow">run_clm.py</a> script.</p>
<p>Usually, the next token is predicted by sampling from the logits of the last hidden state the model produces from the
input sequence.</p>
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			<span>Pytorch</span></div>
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				<span>Hide Pytorch content</span></div></div>
	<div class="framework-content">
<p>Here is an example of using the tokenizer and model and leveraging the
<a href="/docs/transformers/pr_19043/en/internal/generation_utils#transformers.top_k_top_p_filtering">top_k_top_p_filtering()</a> method to sample the next token following an input sequence
of tokens.</p>

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	Copied</div></button></div>
	<pre><!-- HTML_TAG_START --><span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForCausalLM, AutoTokenizer, top_k_top_p_filtering
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> torch <span class="hljs-keyword">import</span> nn

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;gpt2&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForCausalLM.from_pretrained(<span class="hljs-string">&quot;gpt2&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>sequence = <span class="hljs-string">f&quot;Hugging Face is based in DUMBO, New York City, and&quot;</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(sequence, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>input_ids = inputs[<span class="hljs-string">&quot;input_ids&quot;</span>]

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># get logits of last hidden state</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>next_token_logits = model(**inputs).logits[:, -<span class="hljs-number">1</span>, :]

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># filter</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>filtered_next_token_logits = top_k_top_p_filtering(next_token_logits, top_k=<span class="hljs-number">50</span>, top_p=<span class="hljs-number">1.0</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># sample</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>probs = nn.functional.softmax(filtered_next_token_logits, dim=-<span class="hljs-number">1</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>next_token = torch.multinomial(probs, num_samples=<span class="hljs-number">1</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>generated = torch.cat([input_ids, next_token], dim=-<span class="hljs-number">1</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>resulting_string = tokenizer.decode(generated.tolist()[<span class="hljs-number">0</span>])
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(resulting_string)
Hugging Face <span class="hljs-keyword">is</span> based <span class="hljs-keyword">in</span> DUMBO, New York City, <span class="hljs-keyword">and</span> ...<!-- HTML_TAG_END --></pre></div></div></div>
	

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			<span>TensorFlow</span></div>
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	<div class="framework-content">
<p>Here is an example of using the tokenizer and model and leveraging the
<a href="/docs/transformers/pr_19043/en/internal/generation_utils#transformers.tf_top_k_top_p_filtering">tf_top_k_top_p_filtering()</a> method to sample the next token following an input sequence
of tokens.</p>

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	Copied</div></button></div>
	<pre><!-- HTML_TAG_START --><span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFAutoModelForCausalLM, AutoTokenizer, tf_top_k_top_p_filtering
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;gpt2&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForCausalLM.from_pretrained(<span class="hljs-string">&quot;gpt2&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>sequence = <span class="hljs-string">f&quot;Hugging Face is based in DUMBO, New York City, and&quot;</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(sequence, return_tensors=<span class="hljs-string">&quot;tf&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>input_ids = inputs[<span class="hljs-string">&quot;input_ids&quot;</span>]

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># get logits of last hidden state</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>next_token_logits = model(**inputs).logits[:, -<span class="hljs-number">1</span>, :]

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># filter</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>filtered_next_token_logits = tf_top_k_top_p_filtering(next_token_logits, top_k=<span class="hljs-number">50</span>, top_p=<span class="hljs-number">1.0</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># sample</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>next_token = tf.random.categorical(filtered_next_token_logits, dtype=tf.int32, num_samples=<span class="hljs-number">1</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>generated = tf.concat([input_ids, next_token], axis=<span class="hljs-number">1</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>resulting_string = tokenizer.decode(generated.numpy().tolist()[<span class="hljs-number">0</span>])
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(resulting_string)
Hugging Face <span class="hljs-keyword">is</span> based <span class="hljs-keyword">in</span> DUMBO, New York City, <span class="hljs-keyword">and</span> ...<!-- HTML_TAG_END --></pre></div>
					</div></div>
	</div>
<p>This outputs a (hopefully) coherent next token following the original sequence, which in our case is the word <em>is</em> or
<em>features</em>.</p>
<p>In the next section, we show how <a href="/docs/transformers/pr_19043/en/main_classes/text_generation#transformers.generation_utils.GenerationMixin.generate">generation_utils.GenerationMixin.generate()</a> can be used to
generate multiple tokens up to a specified length instead of one token at a time.</p>
<h3 class="relative group"><a id="text-generation" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#text-generation"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
	<span>Text Generation
	</span></h3>

<p>In text generation (<em>a.k.a</em> <em>open-ended text generation</em>) the goal is to create a coherent portion of text that is a
continuation from the given context. The following example shows how <em>GPT-2</em> can be used in pipelines to generate text.
As a default all models apply <em>Top-K</em> sampling when used in pipelines, as configured in their respective configurations
(see <a href="https://huggingface.co/gpt2/blob/main/config.json" rel="nofollow">gpt-2 config</a> for example).</p>
<div class="space-y-10 py-6 2xl:py-8 2xl:-mx-4">

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			<span>Pytorch</span></div>
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	Copied</div></button></div>
	<pre><!-- HTML_TAG_START --><span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline

<span class="hljs-meta">&gt;&gt;&gt; </span>text_generator = pipeline(<span class="hljs-string">&quot;text-generation&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(text_generator(<span class="hljs-string">&quot;As far as I am concerned, I will&quot;</span>, max_length=<span class="hljs-number">50</span>, do_sample=<span class="hljs-literal">False</span>))
[{<span class="hljs-string">&#x27;generated_text&#x27;</span>: <span class="hljs-string">&#x27;As far as I am concerned, I will be the first to admit that I am not a fan of the idea of a
&quot;free market.&quot; I think that the idea of a free market is a bit of a stretch. I think that the idea&#x27;</span>}]<!-- HTML_TAG_END --></pre></div>
<p>Here, the model generates a random text with a total maximal length of <em>50</em> tokens from context <em>“As far as I am
concerned, I will”</em>. Behind the scenes, the pipeline object calls the method
<a href="/docs/transformers/pr_19043/en/main_classes/text_generation#transformers.generation_utils.GenerationMixin.generate">PreTrainedModel.generate()</a> to generate text. The default arguments for this method can be
overridden in the pipeline, as is shown above for the arguments <code>max_length</code> and <code>do_sample</code>.</p>
<p>Below is an example of text generation using <code>XLNet</code> and its tokenizer, which includes calling <code>generate()</code> directly:</p>

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	Copied</div></button></div>
	<pre><!-- HTML_TAG_START --><span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForCausalLM, AutoTokenizer

<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForCausalLM.from_pretrained(<span class="hljs-string">&quot;xlnet-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;xlnet-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Padding text helps XLNet with short prompts - proposed by Aman Rusia in https://github.com/rusiaaman/XLNet-gen#methodology</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>PADDING_TEXT = <span class="hljs-string">&quot;&quot;&quot;In 1991, the remains of Russian Tsar Nicholas II and his family
<span class="hljs-meta">... </span>(except for Alexei and Maria) are discovered.
<span class="hljs-meta">... </span>The voice of Nicholas&#x27;s young son, Tsarevich Alexei Nikolaevich, narrates the
<span class="hljs-meta">... </span>remainder of the story. 1883 Western Siberia,
<span class="hljs-meta">... </span>a young Grigori Rasputin is asked by his father and a group of men to perform magic.
<span class="hljs-meta">... </span>Rasputin has a vision and denounces one of the men as a horse thief. Although his
<span class="hljs-meta">... </span>father initially slaps him for making such an accusation, Rasputin watches as the
<span class="hljs-meta">... </span>man is chased outside and beaten. Twenty years later, Rasputin sees a vision of
<span class="hljs-meta">... </span>the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous,
<span class="hljs-meta">... </span>with people, even a bishop, begging for his blessing. &lt;eod&gt; &lt;/s&gt; &lt;eos&gt;&quot;&quot;&quot;</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>prompt = <span class="hljs-string">&quot;Today the weather is really nice and I am planning on &quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(PADDING_TEXT + prompt, add_special_tokens=<span class="hljs-literal">False</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)[<span class="hljs-string">&quot;input_ids&quot;</span>]

<span class="hljs-meta">&gt;&gt;&gt; </span>prompt_length = <span class="hljs-built_in">len</span>(tokenizer.decode(inputs[<span class="hljs-number">0</span>]))
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model.generate(inputs, max_length=<span class="hljs-number">250</span>, do_sample=<span class="hljs-literal">True</span>, top_p=<span class="hljs-number">0.95</span>, top_k=<span class="hljs-number">60</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>generated = prompt + tokenizer.decode(outputs[<span class="hljs-number">0</span>])[prompt_length + <span class="hljs-number">1</span> :]

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(generated)
Today the weather <span class="hljs-keyword">is</span> really nice <span class="hljs-keyword">and</span> I am planning ...<!-- HTML_TAG_END --></pre></div></div></div>
	

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			<span>TensorFlow</span></div>
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	<pre><!-- HTML_TAG_START --><span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFAutoModelForCausalLM, AutoTokenizer

<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForCausalLM.from_pretrained(<span class="hljs-string">&quot;xlnet-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;xlnet-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Padding text helps XLNet with short prompts - proposed by Aman Rusia in https://github.com/rusiaaman/XLNet-gen#methodology</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>PADDING_TEXT = <span class="hljs-string">&quot;&quot;&quot;In 1991, the remains of Russian Tsar Nicholas II and his family
<span class="hljs-meta">... </span>(except for Alexei and Maria) are discovered.
<span class="hljs-meta">... </span>The voice of Nicholas&#x27;s young son, Tsarevich Alexei Nikolaevich, narrates the
<span class="hljs-meta">... </span>remainder of the story. 1883 Western Siberia,
<span class="hljs-meta">... </span>a young Grigori Rasputin is asked by his father and a group of men to perform magic.
<span class="hljs-meta">... </span>Rasputin has a vision and denounces one of the men as a horse thief. Although his
<span class="hljs-meta">... </span>father initially slaps him for making such an accusation, Rasputin watches as the
<span class="hljs-meta">... </span>man is chased outside and beaten. Twenty years later, Rasputin sees a vision of
<span class="hljs-meta">... </span>the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous,
<span class="hljs-meta">... </span>with people, even a bishop, begging for his blessing. &lt;eod&gt; &lt;/s&gt; &lt;eos&gt;&quot;&quot;&quot;</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>prompt = <span class="hljs-string">&quot;Today the weather is really nice and I am planning on &quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(PADDING_TEXT + prompt, add_special_tokens=<span class="hljs-literal">False</span>, return_tensors=<span class="hljs-string">&quot;tf&quot;</span>)[<span class="hljs-string">&quot;input_ids&quot;</span>]

<span class="hljs-meta">&gt;&gt;&gt; </span>prompt_length = <span class="hljs-built_in">len</span>(tokenizer.decode(inputs[<span class="hljs-number">0</span>]))
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model.generate(inputs, max_length=<span class="hljs-number">250</span>, do_sample=<span class="hljs-literal">True</span>, top_p=<span class="hljs-number">0.95</span>, top_k=<span class="hljs-number">60</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>generated = prompt + tokenizer.decode(outputs[<span class="hljs-number">0</span>])[prompt_length + <span class="hljs-number">1</span> :]

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(generated)
Today the weather <span class="hljs-keyword">is</span> really nice <span class="hljs-keyword">and</span> I am planning ...<!-- HTML_TAG_END --></pre></div>
					</div></div>
	</div>
<p>Text generation is currently possible with <em>GPT-2</em>, <em>OpenAi-GPT</em>, <em>CTRL</em>, <em>XLNet</em>, <em>Transfo-XL</em> and <em>Reformer</em> in
PyTorch and for most models in Tensorflow as well. As can be seen in the example above <em>XLNet</em> and <em>Transfo-XL</em> often
need to be padded to work well. GPT-2 is usually a good choice for <em>open-ended text generation</em> because it was trained
on millions of webpages with a causal language modeling objective.</p>
<p>For more information on how to apply different decoding strategies for text generation, please also refer to our text
generation blog post <a href="https://huggingface.co/blog/how-to-generate" rel="nofollow">here</a>.</p>
<h2 class="relative group"><a id="named-entity-recognition" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#named-entity-recognition"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
	<span>Named Entity Recognition
	</span></h2>

<p>Named Entity Recognition (NER) is the task of classifying tokens according to a class, for example, identifying a token
as a person, an organisation or a location. An example of a named entity recognition dataset is the CoNLL-2003 dataset,
which is entirely based on that task. If you would like to fine-tune a model on an NER task, you may leverage the
<a href="https://github.com/huggingface/transformers/tree/main/examples/pytorch/token-classification/run_ner.py" rel="nofollow">run_ner.py</a> script.</p>
<p>Here is an example of using pipelines to do named entity recognition, specifically, trying to identify tokens as
belonging to one of 9 classes:</p>
<ul><li>O, Outside of a named entity</li>
<li>B-MIS, Beginning of a miscellaneous entity right after another miscellaneous entity</li>
<li>I-MIS, Miscellaneous entity</li>
<li>B-PER, Beginning of a person’s name right after another person’s name</li>
<li>I-PER, Person’s name</li>
<li>B-ORG, Beginning of an organisation right after another organisation</li>
<li>I-ORG, Organisation</li>
<li>B-LOC, Beginning of a location right after another location</li>
<li>I-LOC, Location</li></ul>
<p>It leverages a fine-tuned model on CoNLL-2003, fine-tuned by <a href="https://github.com/stefan-it" rel="nofollow">@stefan-it</a> from <a href="https://github.com/dbmdz" rel="nofollow">dbmdz</a>.</p>

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	<pre><!-- HTML_TAG_START --><span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline

<span class="hljs-meta">&gt;&gt;&gt; </span>ner_pipe = pipeline(<span class="hljs-string">&quot;ner&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>sequence = <span class="hljs-string">&quot;&quot;&quot;Hugging Face Inc. is a company based in New York City. Its headquarters are in DUMBO,
<span class="hljs-meta">... </span>therefore very close to the Manhattan Bridge which is visible from the window.&quot;&quot;&quot;</span><!-- HTML_TAG_END --></pre></div>
<p>This outputs a list of all words that have been identified as one of the entities from the 9 classes defined above.
Here are the expected results:</p>

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	<pre><!-- HTML_TAG_START --><span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">for</span> entity <span class="hljs-keyword">in</span> ner_pipe(sequence):
<span class="hljs-meta">... </span>    <span class="hljs-built_in">print</span>(entity)
{<span class="hljs-string">&#x27;entity&#x27;</span>: <span class="hljs-string">&#x27;I-ORG&#x27;</span>, <span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.9996</span>, <span class="hljs-string">&#x27;index&#x27;</span>: <span class="hljs-number">1</span>, <span class="hljs-string">&#x27;word&#x27;</span>: <span class="hljs-string">&#x27;Hu&#x27;</span>, <span class="hljs-string">&#x27;start&#x27;</span>: <span class="hljs-number">0</span>, <span class="hljs-string">&#x27;end&#x27;</span>: <span class="hljs-number">2</span>}
{<span class="hljs-string">&#x27;entity&#x27;</span>: <span class="hljs-string">&#x27;I-ORG&#x27;</span>, <span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.9910</span>, <span class="hljs-string">&#x27;index&#x27;</span>: <span class="hljs-number">2</span>, <span class="hljs-string">&#x27;word&#x27;</span>: <span class="hljs-string">&#x27;##gging&#x27;</span>, <span class="hljs-string">&#x27;start&#x27;</span>: <span class="hljs-number">2</span>, <span class="hljs-string">&#x27;end&#x27;</span>: <span class="hljs-number">7</span>}
{<span class="hljs-string">&#x27;entity&#x27;</span>: <span class="hljs-string">&#x27;I-ORG&#x27;</span>, <span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.9982</span>, <span class="hljs-string">&#x27;index&#x27;</span>: <span class="hljs-number">3</span>, <span class="hljs-string">&#x27;word&#x27;</span>: <span class="hljs-string">&#x27;Face&#x27;</span>, <span class="hljs-string">&#x27;start&#x27;</span>: <span class="hljs-number">8</span>, <span class="hljs-string">&#x27;end&#x27;</span>: <span class="hljs-number">12</span>}
{<span class="hljs-string">&#x27;entity&#x27;</span>: <span class="hljs-string">&#x27;I-ORG&#x27;</span>, <span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.9995</span>, <span class="hljs-string">&#x27;index&#x27;</span>: <span class="hljs-number">4</span>, <span class="hljs-string">&#x27;word&#x27;</span>: <span class="hljs-string">&#x27;Inc&#x27;</span>, <span class="hljs-string">&#x27;start&#x27;</span>: <span class="hljs-number">13</span>, <span class="hljs-string">&#x27;end&#x27;</span>: <span class="hljs-number">16</span>}
{<span class="hljs-string">&#x27;entity&#x27;</span>: <span class="hljs-string">&#x27;I-LOC&#x27;</span>, <span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.9994</span>, <span class="hljs-string">&#x27;index&#x27;</span>: <span class="hljs-number">11</span>, <span class="hljs-string">&#x27;word&#x27;</span>: <span class="hljs-string">&#x27;New&#x27;</span>, <span class="hljs-string">&#x27;start&#x27;</span>: <span class="hljs-number">40</span>, <span class="hljs-string">&#x27;end&#x27;</span>: <span class="hljs-number">43</span>}
{<span class="hljs-string">&#x27;entity&#x27;</span>: <span class="hljs-string">&#x27;I-LOC&#x27;</span>, <span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.9993</span>, <span class="hljs-string">&#x27;index&#x27;</span>: <span class="hljs-number">12</span>, <span class="hljs-string">&#x27;word&#x27;</span>: <span class="hljs-string">&#x27;York&#x27;</span>, <span class="hljs-string">&#x27;start&#x27;</span>: <span class="hljs-number">44</span>, <span class="hljs-string">&#x27;end&#x27;</span>: <span class="hljs-number">48</span>}
{<span class="hljs-string">&#x27;entity&#x27;</span>: <span class="hljs-string">&#x27;I-LOC&#x27;</span>, <span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.9994</span>, <span class="hljs-string">&#x27;index&#x27;</span>: <span class="hljs-number">13</span>, <span class="hljs-string">&#x27;word&#x27;</span>: <span class="hljs-string">&#x27;City&#x27;</span>, <span class="hljs-string">&#x27;start&#x27;</span>: <span class="hljs-number">49</span>, <span class="hljs-string">&#x27;end&#x27;</span>: <span class="hljs-number">53</span>}
{<span class="hljs-string">&#x27;entity&#x27;</span>: <span class="hljs-string">&#x27;I-LOC&#x27;</span>, <span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.9863</span>, <span class="hljs-string">&#x27;index&#x27;</span>: <span class="hljs-number">19</span>, <span class="hljs-string">&#x27;word&#x27;</span>: <span class="hljs-string">&#x27;D&#x27;</span>, <span class="hljs-string">&#x27;start&#x27;</span>: <span class="hljs-number">79</span>, <span class="hljs-string">&#x27;end&#x27;</span>: <span class="hljs-number">80</span>}
{<span class="hljs-string">&#x27;entity&#x27;</span>: <span class="hljs-string">&#x27;I-LOC&#x27;</span>, <span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.9514</span>, <span class="hljs-string">&#x27;index&#x27;</span>: <span class="hljs-number">20</span>, <span class="hljs-string">&#x27;word&#x27;</span>: <span class="hljs-string">&#x27;##UM&#x27;</span>, <span class="hljs-string">&#x27;start&#x27;</span>: <span class="hljs-number">80</span>, <span class="hljs-string">&#x27;end&#x27;</span>: <span class="hljs-number">82</span>}
{<span class="hljs-string">&#x27;entity&#x27;</span>: <span class="hljs-string">&#x27;I-LOC&#x27;</span>, <span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.9337</span>, <span class="hljs-string">&#x27;index&#x27;</span>: <span class="hljs-number">21</span>, <span class="hljs-string">&#x27;word&#x27;</span>: <span class="hljs-string">&#x27;##BO&#x27;</span>, <span class="hljs-string">&#x27;start&#x27;</span>: <span class="hljs-number">82</span>, <span class="hljs-string">&#x27;end&#x27;</span>: <span class="hljs-number">84</span>}
{<span class="hljs-string">&#x27;entity&#x27;</span>: <span class="hljs-string">&#x27;I-LOC&#x27;</span>, <span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.9762</span>, <span class="hljs-string">&#x27;index&#x27;</span>: <span class="hljs-number">28</span>, <span class="hljs-string">&#x27;word&#x27;</span>: <span class="hljs-string">&#x27;Manhattan&#x27;</span>, <span class="hljs-string">&#x27;start&#x27;</span>: <span class="hljs-number">114</span>, <span class="hljs-string">&#x27;end&#x27;</span>: <span class="hljs-number">123</span>}
{<span class="hljs-string">&#x27;entity&#x27;</span>: <span class="hljs-string">&#x27;I-LOC&#x27;</span>, <span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.9915</span>, <span class="hljs-string">&#x27;index&#x27;</span>: <span class="hljs-number">29</span>, <span class="hljs-string">&#x27;word&#x27;</span>: <span class="hljs-string">&#x27;Bridge&#x27;</span>, <span class="hljs-string">&#x27;start&#x27;</span>: <span class="hljs-number">124</span>, <span class="hljs-string">&#x27;end&#x27;</span>: <span class="hljs-number">130</span>}<!-- HTML_TAG_END --></pre></div>
<p>Note how the tokens of the sequence “Hugging Face” have been identified as an organisation, and “New York City”,
“DUMBO” and “Manhattan Bridge” have been identified as locations.</p>
<p>Here is an example of doing named entity recognition, using a model and a tokenizer. The process is the following:</p>
<ol><li>Instantiate a tokenizer and a model from the checkpoint name. The model is identified as a BERT model and loads it
with the weights stored in the checkpoint.</li>
<li>Define a sequence with known entities, such as “Hugging Face” as an organisation and “New York City” as a location.</li>
<li>Split words into tokens so that they can be mapped to predictions. We use a small hack by, first, completely
encoding and decoding the sequence, so that we’re left with a string that contains the special tokens.</li>
<li>Encode that sequence into IDs (special tokens are added automatically).</li>
<li>Retrieve the predictions by passing the input to the model and getting the first output. This results in a
distribution over the 9 possible classes for each token. We take the argmax to retrieve the most likely class for
each token.</li>
<li>Zip together each token with its prediction and print it.</li></ol>
<div class="space-y-10 py-6 2xl:py-8 2xl:-mx-4">

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			<span>Pytorch</span></div>
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	<pre><!-- HTML_TAG_START --><span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForTokenClassification, AutoTokenizer
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForTokenClassification.from_pretrained(<span class="hljs-string">&quot;dbmdz/bert-large-cased-finetuned-conll03-english&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>sequence = (
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;Hugging Face Inc. is a company based in New York City. Its headquarters are in DUMBO, &quot;</span>
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;therefore very close to the Manhattan Bridge.&quot;</span>
<span class="hljs-meta">... </span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(sequence, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokens = inputs.tokens()

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs).logits
<span class="hljs-meta">&gt;&gt;&gt; </span>predictions = torch.argmax(outputs, dim=<span class="hljs-number">2</span>)<!-- HTML_TAG_END --></pre></div></div></div>
	

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			<span>TensorFlow</span></div>
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	Copied</div></button></div>
	<pre><!-- HTML_TAG_START --><span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFAutoModelForTokenClassification, AutoTokenizer
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf

<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForTokenClassification.from_pretrained(<span class="hljs-string">&quot;dbmdz/bert-large-cased-finetuned-conll03-english&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>sequence = (
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;Hugging Face Inc. is a company based in New York City. Its headquarters are in DUMBO, &quot;</span>
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;therefore very close to the Manhattan Bridge.&quot;</span>
<span class="hljs-meta">... </span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(sequence, return_tensors=<span class="hljs-string">&quot;tf&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokens = inputs.tokens()

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)[<span class="hljs-number">0</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>predictions = tf.argmax(outputs, axis=<span class="hljs-number">2</span>)<!-- HTML_TAG_END --></pre></div>
					</div></div>
	</div>
<p>This outputs a list of each token mapped to its corresponding prediction. Differently from the pipeline, here every
token has a prediction as we didn’t remove the “0”th class, which means that no particular entity was found on that
token.</p>
<p>In the above example, <code>predictions</code> is an integer that corresponds to the predicted class. We can use the
<code>model.config.id2label</code> property in order to recover the class name corresponding to the class number, which is
illustrated below:</p>

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	Copied</div></button></div>
	<pre><!-- HTML_TAG_START --><span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">for</span> token, prediction <span class="hljs-keyword">in</span> <span class="hljs-built_in">zip</span>(tokens, predictions[<span class="hljs-number">0</span>].numpy()):
<span class="hljs-meta">... </span>    <span class="hljs-built_in">print</span>((token, model.config.id2label[prediction]))
(<span class="hljs-string">&#x27;[CLS]&#x27;</span>, <span class="hljs-string">&#x27;O&#x27;</span>)
(<span class="hljs-string">&#x27;Hu&#x27;</span>, <span class="hljs-string">&#x27;I-ORG&#x27;</span>)
(<span class="hljs-string">&#x27;##gging&#x27;</span>, <span class="hljs-string">&#x27;I-ORG&#x27;</span>)
(<span class="hljs-string">&#x27;Face&#x27;</span>, <span class="hljs-string">&#x27;I-ORG&#x27;</span>)
(<span class="hljs-string">&#x27;Inc&#x27;</span>, <span class="hljs-string">&#x27;I-ORG&#x27;</span>)
(<span class="hljs-string">&#x27;.&#x27;</span>, <span class="hljs-string">&#x27;O&#x27;</span>)
(<span class="hljs-string">&#x27;is&#x27;</span>, <span class="hljs-string">&#x27;O&#x27;</span>)
(<span class="hljs-string">&#x27;a&#x27;</span>, <span class="hljs-string">&#x27;O&#x27;</span>)
(<span class="hljs-string">&#x27;company&#x27;</span>, <span class="hljs-string">&#x27;O&#x27;</span>)
(<span class="hljs-string">&#x27;based&#x27;</span>, <span class="hljs-string">&#x27;O&#x27;</span>)
(<span class="hljs-string">&#x27;in&#x27;</span>, <span class="hljs-string">&#x27;O&#x27;</span>)
(<span class="hljs-string">&#x27;New&#x27;</span>, <span class="hljs-string">&#x27;I-LOC&#x27;</span>)
(<span class="hljs-string">&#x27;York&#x27;</span>, <span class="hljs-string">&#x27;I-LOC&#x27;</span>)
(<span class="hljs-string">&#x27;City&#x27;</span>, <span class="hljs-string">&#x27;I-LOC&#x27;</span>)
(<span class="hljs-string">&#x27;.&#x27;</span>, <span class="hljs-string">&#x27;O&#x27;</span>)
(<span class="hljs-string">&#x27;Its&#x27;</span>, <span class="hljs-string">&#x27;O&#x27;</span>)
(<span class="hljs-string">&#x27;headquarters&#x27;</span>, <span class="hljs-string">&#x27;O&#x27;</span>)
(<span class="hljs-string">&#x27;are&#x27;</span>, <span class="hljs-string">&#x27;O&#x27;</span>)
(<span class="hljs-string">&#x27;in&#x27;</span>, <span class="hljs-string">&#x27;O&#x27;</span>)
(<span class="hljs-string">&#x27;D&#x27;</span>, <span class="hljs-string">&#x27;I-LOC&#x27;</span>)
(<span class="hljs-string">&#x27;##UM&#x27;</span>, <span class="hljs-string">&#x27;I-LOC&#x27;</span>)
(<span class="hljs-string">&#x27;##BO&#x27;</span>, <span class="hljs-string">&#x27;I-LOC&#x27;</span>)
(<span class="hljs-string">&#x27;,&#x27;</span>, <span class="hljs-string">&#x27;O&#x27;</span>)
(<span class="hljs-string">&#x27;therefore&#x27;</span>, <span class="hljs-string">&#x27;O&#x27;</span>)
(<span class="hljs-string">&#x27;very&#x27;</span>, <span class="hljs-string">&#x27;O&#x27;</span>)
(<span class="hljs-string">&#x27;close&#x27;</span>, <span class="hljs-string">&#x27;O&#x27;</span>)
(<span class="hljs-string">&#x27;to&#x27;</span>, <span class="hljs-string">&#x27;O&#x27;</span>)
(<span class="hljs-string">&#x27;the&#x27;</span>, <span class="hljs-string">&#x27;O&#x27;</span>)
(<span class="hljs-string">&#x27;Manhattan&#x27;</span>, <span class="hljs-string">&#x27;I-LOC&#x27;</span>)
(<span class="hljs-string">&#x27;Bridge&#x27;</span>, <span class="hljs-string">&#x27;I-LOC&#x27;</span>)
(<span class="hljs-string">&#x27;.&#x27;</span>, <span class="hljs-string">&#x27;O&#x27;</span>)
(<span class="hljs-string">&#x27;[SEP]&#x27;</span>, <span class="hljs-string">&#x27;O&#x27;</span>)<!-- HTML_TAG_END --></pre></div>
<h2 class="relative group"><a id="summarization" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#summarization"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
	<span>Summarization
	</span></h2>

<p>Summarization is the task of summarizing a document or an article into a shorter text. If you would like to fine-tune a
model on a summarization task, you may leverage the <a href="https://github.com/huggingface/transformers/tree/main/examples/pytorch/summarization/run_summarization.py" rel="nofollow">run_summarization.py</a>
script.</p>
<p>An example of a summarization dataset is the CNN / Daily Mail dataset, which consists of long news articles and was
created for the task of summarization. If you would like to fine-tune a model on a summarization task, various
approaches are described in this <a href="https://github.com/huggingface/transformers/tree/main/examples/pytorch/summarization/README.md" rel="nofollow">document</a>.</p>
<p>Here is an example of using the pipelines to do summarization. It leverages a Bart model that was fine-tuned on the CNN
/ Daily Mail data set.</p>

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	<pre><!-- HTML_TAG_START --><span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline

<span class="hljs-meta">&gt;&gt;&gt; </span>summarizer = pipeline(<span class="hljs-string">&quot;summarization&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>ARTICLE = <span class="hljs-string">&quot;&quot;&quot; New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County, New York.
<span class="hljs-meta">... </span>A year later, she got married again in Westchester County, but to a different man and without divorcing her first husband.
<span class="hljs-meta">... </span>Only 18 days after that marriage, she got hitched yet again. Then, Barrientos declared &quot;I do&quot; five more times, sometimes only within two weeks of each other.
<span class="hljs-meta">... </span>In 2010, she married once more, this time in the Bronx. In an application for a marriage license, she stated it was her &quot;first and only&quot; marriage.
<span class="hljs-meta">... </span>Barrientos, now 39, is facing two criminal counts of &quot;offering a false instrument for filing in the first degree,&quot; referring to her false statements on the
<span class="hljs-meta">... </span>2010 marriage license application, according to court documents.
<span class="hljs-meta">... </span>Prosecutors said the marriages were part of an immigration scam.
<span class="hljs-meta">... </span>On Friday, she pleaded not guilty at State Supreme Court in the Bronx, according to her attorney, Christopher Wright, who declined to comment further.
<span class="hljs-meta">... </span>After leaving court, Barrientos was arrested and charged with theft of service and criminal trespass for allegedly sneaking into the New York subway through an emergency exit, said Detective
<span class="hljs-meta">... </span>Annette Markowski, a police spokeswoman. In total, Barrientos has been married 10 times, with nine of her marriages occurring between 1999 and 2002.
<span class="hljs-meta">... </span>All occurred either in Westchester County, Long Island, New Jersey or the Bronx. She is believed to still be married to four men, and at one time, she was married to eight men at once, prosecutors say.
<span class="hljs-meta">... </span>Prosecutors said the immigration scam involved some of her husbands, who filed for permanent residence status shortly after the marriages.
<span class="hljs-meta">... </span>Any divorces happened only after such filings were approved. It was unclear whether any of the men will be prosecuted.
<span class="hljs-meta">... </span>The case was referred to the Bronx District Attorney\&#x27;s Office by Immigration and Customs Enforcement and the Department of Homeland Security\&#x27;s
<span class="hljs-meta">... </span>Investigation Division. Seven of the men are from so-called &quot;red-flagged&quot; countries, including Egypt, Turkey, Georgia, Pakistan and Mali.
<span class="hljs-meta">... </span>Her eighth husband, Rashid Rajput, was deported in 2006 to his native Pakistan after an investigation by the Joint Terrorism Task Force.
<span class="hljs-meta">... </span>If convicted, Barrientos faces up to four years in prison.  Her next court appearance is scheduled for May 18.
<span class="hljs-meta">... </span>&quot;&quot;&quot;</span><!-- HTML_TAG_END --></pre></div>
<p>Because the summarization pipeline depends on the <code>PreTrainedModel.generate()</code> method, we can override the default
arguments of <code>PreTrainedModel.generate()</code> directly in the pipeline for <code>max_length</code> and <code>min_length</code> as shown
below. This outputs the following summary:</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>
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	Copied</div></button></div>
	<pre><!-- HTML_TAG_START --><span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(summarizer(ARTICLE, max_length=<span class="hljs-number">130</span>, min_length=<span class="hljs-number">30</span>, do_sample=<span class="hljs-literal">False</span>))
[{<span class="hljs-string">&#x27;summary_text&#x27;</span>: <span class="hljs-string">&#x27; Liana Barrientos, 39, is charged with two counts of &quot;offering a false instrument for filing in
the first degree&quot; In total, she has been married 10 times, with nine of her marriages occurring between 1999 and
2002 . At one time, she was married to eight men at once, prosecutors say .&#x27;</span>}]<!-- HTML_TAG_END --></pre></div>
<p>Here is an example of doing summarization using a model and a tokenizer. The process is the following:</p>
<ol><li>Instantiate a tokenizer and a model from the checkpoint name. Summarization is usually done using an encoder-decoder
model, such as <code>Bart</code> or <code>T5</code>.</li>
<li>Define the article that should be summarized.</li>
<li>Add the T5 specific prefix “summarize: “.</li>
<li>Use the <code>PreTrainedModel.generate()</code> method to generate the summary.</li></ol>
<p>In this example we use Google’s T5 model. Even though it was pre-trained only on a multi-task mixed dataset (including
CNN / Daily Mail), it yields very good results.</p>
<div class="space-y-10 py-6 2xl:py-8 2xl:-mx-4">

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			<span>Pytorch</span></div>
		<div class="cursor-pointer flex items-center justify-center space-x-1 text-sm px-2 bg-white dark:bg-gray-950 hover:underline leading-none"><svg class="" width="0.9em" height="0.9em" viewBox="0 0 10 9" fill="currentColor" xmlns="http://www.w3.org/2000/svg"><path d="M1.39125 1.9725L0.0883333 0.669997L0.677917 0.0804138L8.9275 8.33041L8.33792 8.91958L6.95875 7.54041C6.22592 8.00523 5.37572 8.25138 4.50792 8.25C2.26125 8.25 0.392083 6.63333 0 4.5C0.179179 3.52946 0.667345 2.64287 1.39167 1.9725H1.39125ZM5.65667 6.23833L5.04667 5.62833C4.81335 5.73996 4.55116 5.77647 4.29622 5.73282C4.04129 5.68918 3.80617 5.56752 3.62328 5.38463C3.44039 5.20175 3.31874 4.96663 3.27509 4.71169C3.23144 4.45676 3.26795 4.19456 3.37958 3.96125L2.76958 3.35125C2.50447 3.75187 2.38595 4.2318 2.4341 4.70978C2.48225 5.18777 2.6941 5.63442 3.0338 5.97411C3.37349 6.31381 3.82015 6.52567 4.29813 6.57382C4.77611 6.62197 5.25605 6.50345 5.65667 6.23833ZM2.83042 1.06666C3.35 0.862497 3.91625 0.749997 4.50792 0.749997C6.75458 0.749997 8.62375 2.36666 9.01583 4.5C8.88816 5.19404 8.60119 5.84899 8.1775 6.41333L6.56917 4.805C6.61694 4.48317 6.58868 4.15463 6.48664 3.84569C6.3846 3.53675 6.21162 3.256 5.98156 3.02594C5.7515 2.79588 5.47075 2.6229 5.16181 2.52086C4.85287 2.41882 4.52433 2.39056 4.2025 2.43833L2.83042 1.06708V1.06666Z" fill="currentColor"></path></svg>
				<span>Hide Pytorch content</span></div></div>
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	Copied</div></button></div>
	<pre><!-- HTML_TAG_START --><span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForSeq2SeqLM, AutoTokenizer

<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSeq2SeqLM.from_pretrained(<span class="hljs-string">&quot;t5-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;t5-base&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># T5 uses a max_length of 512 so we cut the article to 512 tokens.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(<span class="hljs-string">&quot;summarize: &quot;</span> + ARTICLE, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>, max_length=<span class="hljs-number">512</span>, truncation=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model.generate(
<span class="hljs-meta">... </span>    inputs[<span class="hljs-string">&quot;input_ids&quot;</span>], max_length=<span class="hljs-number">150</span>, min_length=<span class="hljs-number">40</span>, length_penalty=<span class="hljs-number">2.0</span>, num_beams=<span class="hljs-number">4</span>, early_stopping=<span class="hljs-literal">True</span>
<span class="hljs-meta">... </span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(tokenizer.decode(outputs[<span class="hljs-number">0</span>], skip_special_tokens=<span class="hljs-literal">True</span>))
prosecutors say the marriages were part of an immigration scam. <span class="hljs-keyword">if</span> convicted, barrientos faces two criminal
counts of <span class="hljs-string">&quot;offering a false instrument for filing in the first degree&quot;</span> she has been married <span class="hljs-number">10</span> times, nine of them
between <span class="hljs-number">1999</span> <span class="hljs-keyword">and</span> <span class="hljs-number">2002.</span><!-- HTML_TAG_END --></pre></div></div></div>
	

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			<span>TensorFlow</span></div>
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	Copied</div></button></div>
	<pre><!-- HTML_TAG_START --><span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFAutoModelForSeq2SeqLM, AutoTokenizer

<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSeq2SeqLM.from_pretrained(<span class="hljs-string">&quot;t5-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;t5-base&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># T5 uses a max_length of 512 so we cut the article to 512 tokens.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(<span class="hljs-string">&quot;summarize: &quot;</span> + ARTICLE, return_tensors=<span class="hljs-string">&quot;tf&quot;</span>, max_length=<span class="hljs-number">512</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model.generate(
<span class="hljs-meta">... </span>    inputs[<span class="hljs-string">&quot;input_ids&quot;</span>], max_length=<span class="hljs-number">150</span>, min_length=<span class="hljs-number">40</span>, length_penalty=<span class="hljs-number">2.0</span>, num_beams=<span class="hljs-number">4</span>, early_stopping=<span class="hljs-literal">True</span>
<span class="hljs-meta">... </span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(tokenizer.decode(outputs[<span class="hljs-number">0</span>], skip_special_tokens=<span class="hljs-literal">True</span>))
prosecutors say the marriages were part of an immigration scam. <span class="hljs-keyword">if</span> convicted, barrientos faces two criminal
counts of <span class="hljs-string">&quot;offering a false instrument for filing in the first degree&quot;</span> she has been married <span class="hljs-number">10</span> times, nine of them
between <span class="hljs-number">1999</span> <span class="hljs-keyword">and</span> <span class="hljs-number">2002.</span><!-- HTML_TAG_END --></pre></div>
					</div></div>
	</div>
<h2 class="relative group"><a id="translation" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#translation"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
	<span>Translation
	</span></h2>

<p>Translation is the task of translating a text from one language to another. If you would like to fine-tune a model on a
translation task, you may leverage the <a href="https://github.com/huggingface/transformers/tree/main/examples/pytorch/translation/run_translation.py" rel="nofollow">run_translation.py</a> script.</p>
<p>An example of a translation dataset is the WMT English to German dataset, which has sentences in English as the input
data and the corresponding sentences in German as the target data. If you would like to fine-tune a model on a
translation task, various approaches are described in this <a href="https://github.com/huggingface/transformers/tree/main/examples/pytorch/translation/README.md" rel="nofollow">document</a>.</p>
<p>Here is an example of using the pipelines to do translation. It leverages a T5 model that was only pre-trained on a
multi-task mixture dataset (including WMT), yet, yielding impressive translation results.</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>
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	Copied</div></button></div>
	<pre><!-- HTML_TAG_START --><span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline

<span class="hljs-meta">&gt;&gt;&gt; </span>translator = pipeline(<span class="hljs-string">&quot;translation_en_to_de&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(translator(<span class="hljs-string">&quot;Hugging Face is a technology company based in New York and Paris&quot;</span>, max_length=<span class="hljs-number">40</span>))
[{<span class="hljs-string">&#x27;translation_text&#x27;</span>: <span class="hljs-string">&#x27;Hugging Face ist ein Technologieunternehmen mit Sitz in New York und Paris.&#x27;</span>}]<!-- HTML_TAG_END --></pre></div>
<p>Because the translation pipeline depends on the <code>PreTrainedModel.generate()</code> method, we can override the default
arguments of <code>PreTrainedModel.generate()</code> directly in the pipeline as is shown for <code>max_length</code> above.</p>
<p>Here is an example of doing translation using a model and a tokenizer. The process is the following:</p>
<ol><li>Instantiate a tokenizer and a model from the checkpoint name. Summarization is usually done using an encoder-decoder
model, such as <code>Bart</code> or <code>T5</code>.</li>
<li>Define the article that should be summarized.</li>
<li>Add the T5 specific prefix “translate English to German: ”</li>
<li>Use the <code>PreTrainedModel.generate()</code> method to perform the translation.</li></ol>
<div class="space-y-10 py-6 2xl:py-8 2xl:-mx-4">

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			<span>Pytorch</span></div>
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	<pre><!-- HTML_TAG_START --><span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForSeq2SeqLM, AutoTokenizer

<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSeq2SeqLM.from_pretrained(<span class="hljs-string">&quot;t5-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;t5-base&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;translate English to German: Hugging Face is a technology company based in New York and Paris&quot;</span>,
<span class="hljs-meta">... </span>    return_tensors=<span class="hljs-string">&quot;pt&quot;</span>,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model.generate(inputs[<span class="hljs-string">&quot;input_ids&quot;</span>], max_length=<span class="hljs-number">40</span>, num_beams=<span class="hljs-number">4</span>, early_stopping=<span class="hljs-literal">True</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(tokenizer.decode(outputs[<span class="hljs-number">0</span>], skip_special_tokens=<span class="hljs-literal">True</span>))
Hugging Face ist ein Technologieunternehmen mit Sitz <span class="hljs-keyword">in</span> New York und Paris.<!-- HTML_TAG_END --></pre></div></div></div>
	

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			<span>TensorFlow</span></div>
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	<pre><!-- HTML_TAG_START --><span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFAutoModelForSeq2SeqLM, AutoTokenizer

<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSeq2SeqLM.from_pretrained(<span class="hljs-string">&quot;t5-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;t5-base&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;translate English to German: Hugging Face is a technology company based in New York and Paris&quot;</span>,
<span class="hljs-meta">... </span>    return_tensors=<span class="hljs-string">&quot;tf&quot;</span>,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model.generate(inputs[<span class="hljs-string">&quot;input_ids&quot;</span>], max_length=<span class="hljs-number">40</span>, num_beams=<span class="hljs-number">4</span>, early_stopping=<span class="hljs-literal">True</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(tokenizer.decode(outputs[<span class="hljs-number">0</span>], skip_special_tokens=<span class="hljs-literal">True</span>))
Hugging Face ist ein Technologieunternehmen mit Sitz <span class="hljs-keyword">in</span> New York und Paris.<!-- HTML_TAG_END --></pre></div>
					</div></div>
	</div>
<p>We get the same translation as with the pipeline example.</p>
<h2 class="relative group"><a id="audio-classification" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#audio-classification"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
	<span>Audio classification
	</span></h2>

<p>Audio classification assigns a class to an audio signal. The Keyword Spotting dataset from the <a href="https://huggingface.co/datasets/superb" rel="nofollow">SUPERB</a> benchmark is an example dataset that can be used for audio classification fine-tuning. This dataset contains ten classes of keywords for classification. If you’d like to fine-tune a model for audio classification, take a look at the <a href="https://github.com/huggingface/transformers/blob/main/examples/pytorch/audio-classification/run_audio_classification.py" rel="nofollow">run_audio_classification.py</a> script or this <a href="./tasks/audio_classification">how-to guide</a>.</p>
<p>The following examples demonstrate how to use a <a href="/docs/transformers/pr_19043/en/main_classes/pipelines#transformers.pipeline">pipeline()</a> and a model and tokenizer for audio classification inference:</p>

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	<pre><!-- HTML_TAG_START --><span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>torch.manual_seed(<span class="hljs-number">42</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>dataset = load_dataset(<span class="hljs-string">&quot;hf-internal-testing/librispeech_asr_demo&quot;</span>, <span class="hljs-string">&quot;clean&quot;</span>, split=<span class="hljs-string">&quot;validation&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>dataset = dataset.sort(<span class="hljs-string">&quot;id&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>audio_file = dataset[<span class="hljs-number">0</span>][<span class="hljs-string">&quot;audio&quot;</span>][<span class="hljs-string">&quot;path&quot;</span>]

<span class="hljs-meta">&gt;&gt;&gt; </span>audio_classifier = pipeline(
<span class="hljs-meta">... </span>    task=<span class="hljs-string">&quot;audio-classification&quot;</span>, model=<span class="hljs-string">&quot;ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition&quot;</span>
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>predictions = audio_classifier(audio_file)
<span class="hljs-meta">&gt;&gt;&gt; </span>predictions = [{<span class="hljs-string">&quot;score&quot;</span>: <span class="hljs-built_in">round</span>(pred[<span class="hljs-string">&quot;score&quot;</span>], <span class="hljs-number">4</span>), <span class="hljs-string">&quot;label&quot;</span>: pred[<span class="hljs-string">&quot;label&quot;</span>]} <span class="hljs-keyword">for</span> pred <span class="hljs-keyword">in</span> predictions]
<span class="hljs-meta">&gt;&gt;&gt; </span>predictions
[{<span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.1315</span>, <span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;calm&#x27;</span>}, {<span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.1307</span>, <span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;neutral&#x27;</span>}, {<span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.1274</span>, <span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;sad&#x27;</span>}, {<span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.1261</span>, <span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;fearful&#x27;</span>}, {<span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.1242</span>, <span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;happy&#x27;</span>}]<!-- HTML_TAG_END --></pre></div>
<p>The general process for using a model and feature extractor for audio classification is:</p>
<ol><li>Instantiate a feature extractor and a model from the checkpoint name.</li>
<li>Process the audio signal to be classified with a feature extractor.</li>
<li>Pass the input through the model and take the <code>argmax</code> to retrieve the most likely class.</li>
<li>Convert the class id to a class name with <code>id2label</code> to return an interpretable result.</li></ol>
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	<pre><!-- HTML_TAG_START --><span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoFeatureExtractor, AutoModelForAudioClassification
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>dataset = load_dataset(<span class="hljs-string">&quot;hf-internal-testing/librispeech_asr_demo&quot;</span>, <span class="hljs-string">&quot;clean&quot;</span>, split=<span class="hljs-string">&quot;validation&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>dataset = dataset.sort(<span class="hljs-string">&quot;id&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>sampling_rate = dataset.features[<span class="hljs-string">&quot;audio&quot;</span>].sampling_rate

<span class="hljs-meta">&gt;&gt;&gt; </span>feature_extractor = AutoFeatureExtractor.from_pretrained(<span class="hljs-string">&quot;superb/wav2vec2-base-superb-ks&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForAudioClassification.from_pretrained(<span class="hljs-string">&quot;superb/wav2vec2-base-superb-ks&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = feature_extractor(dataset[<span class="hljs-number">0</span>][<span class="hljs-string">&quot;audio&quot;</span>][<span class="hljs-string">&quot;array&quot;</span>], sampling_rate=sampling_rate, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">with</span> torch.no_grad():
<span class="hljs-meta">... </span>    logits = model(**inputs).logits

<span class="hljs-meta">&gt;&gt;&gt; </span>predicted_class_ids = torch.argmax(logits, dim=-<span class="hljs-number">1</span>).item()
<span class="hljs-meta">&gt;&gt;&gt; </span>predicted_label = model.config.id2label[predicted_class_ids]
<span class="hljs-meta">&gt;&gt;&gt; </span>predicted_label
<span class="hljs-string">&#x27;_unknown_&#x27;</span><!-- HTML_TAG_END --></pre></div></div></div>
	
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<h2 class="relative group"><a id="automatic-speech-recognition" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#automatic-speech-recognition"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
	<span>Automatic speech recognition
	</span></h2>

<p>Automatic speech recognition transcribes an audio signal to text. The <a href="https://huggingface.co/datasets/common_voice" rel="nofollow">Common Voice</a> dataset is an example dataset that can be used for automatic speech recognition fine-tuning. It contains an audio file of a speaker and the corresponding sentence. If you’d like to fine-tune a model for automatic speech recognition, take a look at the <a href="https://github.com/huggingface/transformers/blob/main/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py" rel="nofollow">run_speech_recognition_ctc.py</a> or <a href="https://github.com/huggingface/transformers/blob/main/examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py" rel="nofollow">run_speech_recognition_seq2seq.py</a> scripts or this <a href="./tasks/asr">how-to guide</a>.</p>
<p>The following examples demonstrate how to use a <a href="/docs/transformers/pr_19043/en/main_classes/pipelines#transformers.pipeline">pipeline()</a> and a model and tokenizer for automatic speech recognition inference:</p>

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	<pre><!-- HTML_TAG_START --><span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset

<span class="hljs-meta">&gt;&gt;&gt; </span>dataset = load_dataset(<span class="hljs-string">&quot;hf-internal-testing/librispeech_asr_demo&quot;</span>, <span class="hljs-string">&quot;clean&quot;</span>, split=<span class="hljs-string">&quot;validation&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>dataset = dataset.sort(<span class="hljs-string">&quot;id&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>audio_file = dataset[<span class="hljs-number">0</span>][<span class="hljs-string">&quot;audio&quot;</span>][<span class="hljs-string">&quot;path&quot;</span>]

<span class="hljs-meta">&gt;&gt;&gt; </span>speech_recognizer = pipeline(task=<span class="hljs-string">&quot;automatic-speech-recognition&quot;</span>, model=<span class="hljs-string">&quot;facebook/wav2vec2-base-960h&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>speech_recognizer(audio_file)
{<span class="hljs-string">&#x27;text&#x27;</span>: <span class="hljs-string">&#x27;MISTER QUILTER IS THE APOSTLE OF THE MIDDLE CLASSES AND WE ARE GLAD TO WELCOME HIS GOSPEL&#x27;</span>}<!-- HTML_TAG_END --></pre></div>
<p>The general process for using a model and processor for automatic speech recognition is:</p>
<ol><li>Instantiate a processor (which regroups a feature extractor for input processing and a tokenizer for decoding) and a model from the checkpoint name.</li>
<li>Process the audio signal and text with a processor.</li>
<li>Pass the input through the model and take the <code>argmax</code> to retrieve the predicted text.</li>
<li>Decode the text with a tokenizer to obtain the transcription.</li></ol>
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	<pre><!-- HTML_TAG_START --><span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoProcessor, AutoModelForCTC
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>dataset = load_dataset(<span class="hljs-string">&quot;hf-internal-testing/librispeech_asr_demo&quot;</span>, <span class="hljs-string">&quot;clean&quot;</span>, split=<span class="hljs-string">&quot;validation&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>dataset = dataset.sort(<span class="hljs-string">&quot;id&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>sampling_rate = dataset.features[<span class="hljs-string">&quot;audio&quot;</span>].sampling_rate

<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoProcessor.from_pretrained(<span class="hljs-string">&quot;facebook/wav2vec2-base-960h&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForCTC.from_pretrained(<span class="hljs-string">&quot;facebook/wav2vec2-base-960h&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = processor(dataset[<span class="hljs-number">0</span>][<span class="hljs-string">&quot;audio&quot;</span>][<span class="hljs-string">&quot;array&quot;</span>], sampling_rate=sampling_rate, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">with</span> torch.no_grad():
<span class="hljs-meta">... </span>    logits = model(**inputs).logits
<span class="hljs-meta">&gt;&gt;&gt; </span>predicted_ids = torch.argmax(logits, dim=-<span class="hljs-number">1</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>transcription = processor.batch_decode(predicted_ids)
<span class="hljs-meta">&gt;&gt;&gt; </span>transcription[<span class="hljs-number">0</span>]
<span class="hljs-string">&#x27;MISTER QUILTER IS THE APOSTLE OF THE MIDDLE CLASSES AND WE ARE GLAD TO WELCOME HIS GOSPEL&#x27;</span><!-- HTML_TAG_END --></pre></div></div></div>
	
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<h2 class="relative group"><a id="image-classification" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#image-classification"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
	<span>Image classification
	</span></h2>

<p>Like text and audio classification, image classification assigns a class to an image. The <a href="https://huggingface.co/datasets/cifar100" rel="nofollow">CIFAR-100</a> dataset is an example dataset that can be used for image classification fine-tuning. It contains an image and the corresponding class. If you’d like to fine-tune a model for image classification, take a look at the <a href="https://github.com/huggingface/transformers/blob/main/examples/pytorch/image-classification/run_image_classification.py" rel="nofollow">run_image_classification.py</a> script or this <a href="./tasks/image_classification">how-to guide</a>.</p>
<p>The following examples demonstrate how to use a <a href="/docs/transformers/pr_19043/en/main_classes/pipelines#transformers.pipeline">pipeline()</a> and a model and tokenizer for image classification inference:</p>

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	Copied</div></button></div>
	<pre><!-- HTML_TAG_START --><span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline

<span class="hljs-meta">&gt;&gt;&gt; </span>vision_classifier = pipeline(task=<span class="hljs-string">&quot;image-classification&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>result = vision_classifier(
<span class="hljs-meta">... </span>    images=<span class="hljs-string">&quot;https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg&quot;</span>
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(<span class="hljs-string">&quot;\n&quot;</span>.join([<span class="hljs-string">f&quot;Class <span class="hljs-subst">{d[<span class="hljs-string">&#x27;label&#x27;</span>]}</span> with score <span class="hljs-subst">{<span class="hljs-built_in">round</span>(d[<span class="hljs-string">&#x27;score&#x27;</span>], <span class="hljs-number">4</span>)}</span>&quot;</span> <span class="hljs-keyword">for</span> d <span class="hljs-keyword">in</span> result]))
Class lynx, catamount <span class="hljs-keyword">with</span> score <span class="hljs-number">0.4335</span>                                                    
Class cougar, puma, catamount, mountain lion, painter, panther, Felis concolor <span class="hljs-keyword">with</span> score <span class="hljs-number">0.0348</span>
Class snow leopard, ounce, Panthera uncia <span class="hljs-keyword">with</span> score <span class="hljs-number">0.0324</span>          
Class Egyptian cat <span class="hljs-keyword">with</span> score <span class="hljs-number">0.0239</span>                                                       
Class tiger cat <span class="hljs-keyword">with</span> score <span class="hljs-number">0.0229</span><!-- HTML_TAG_END --></pre></div>
<p>The general process for using a model and feature extractor for image classification is:</p>
<ol><li>Instantiate a feature extractor and a model from the checkpoint name.</li>
<li>Process the image to be classified with a feature extractor.</li>
<li>Pass the input through the model and take the <code>argmax</code> to retrieve the predicted class.</li>
<li>Convert the class id to a class name with <code>id2label</code> to return an interpretable result.</li></ol>
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	<pre><!-- HTML_TAG_START --><span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoFeatureExtractor, AutoModelForImageClassification
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset

<span class="hljs-meta">&gt;&gt;&gt; </span>dataset = load_dataset(<span class="hljs-string">&quot;huggingface/cats-image&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>image = dataset[<span class="hljs-string">&quot;test&quot;</span>][<span class="hljs-string">&quot;image&quot;</span>][<span class="hljs-number">0</span>]

<span class="hljs-meta">&gt;&gt;&gt; </span>feature_extractor = AutoFeatureExtractor.from_pretrained(<span class="hljs-string">&quot;google/vit-base-patch16-224&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForImageClassification.from_pretrained(<span class="hljs-string">&quot;google/vit-base-patch16-224&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = feature_extractor(image, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">with</span> torch.no_grad():
<span class="hljs-meta">... </span>    logits = model(**inputs).logits

<span class="hljs-meta">&gt;&gt;&gt; </span>predicted_label = logits.argmax(-<span class="hljs-number">1</span>).item()
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(model.config.id2label[predicted_label])
Egyptian cat<!-- HTML_TAG_END --></pre></div></div></div>
	
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