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<h1 class="relative group"><a id="using-the-evaluator-with-custom-pipelines" 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="#using-the-evaluator-with-custom-pipelines"><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>Using the <code>evaluator</code> with custom pipelines
	</span></h1>

<p>The evaluator is designed to work with <code>transformer</code> pipelines out-of-the-box. However, in many cases you might have a model or pipeline that’s not part of the <code>transformer</code> ecosystem. You can still use <code>evaluator</code> to easily compute metrics for them. In this guide we show how to do this for a Scikit-Learn <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" rel="nofollow">pipeline</a> and a Spacy <a href="https://spacy.io" rel="nofollow">pipeline</a>. Let’s start with the Scikit-Learn case.</p>
<h2 class="relative group"><a id="scikitlearn" 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="#scikitlearn"><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>Scikit-Learn
	</span></h2>

<p>First we need to train a model. We’ll train a simple text classifier on the <a href="https://huggingface.co/datasets/imdb" rel="nofollow">IMDb dataset</a>, so let’s start by downloading the dataset:</p>

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	<pre><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset

ds = load_dataset(<span class="hljs-string">&quot;imdb&quot;</span>)<!-- HTML_TAG_END --></pre></div>
<p>Then we can build a simple TF-IDF preprocessor and Naive Bayes classifier wrapped in a <code>Pipeline</code>:</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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	<pre><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> sklearn.pipeline <span class="hljs-keyword">import</span> Pipeline
<span class="hljs-keyword">from</span> sklearn.naive_bayes <span class="hljs-keyword">import</span> MultinomialNB
<span class="hljs-keyword">from</span> sklearn.feature_extraction.text <span class="hljs-keyword">import</span> TfidfTransformer
<span class="hljs-keyword">from</span> sklearn.feature_extraction.text <span class="hljs-keyword">import</span> CountVectorizer

text_clf = Pipeline([
        (<span class="hljs-string">&#x27;vect&#x27;</span>, CountVectorizer()),
        (<span class="hljs-string">&#x27;tfidf&#x27;</span>, TfidfTransformer()),
        (<span class="hljs-string">&#x27;clf&#x27;</span>, MultinomialNB()),
])

text_clf.fit(ds[<span class="hljs-string">&quot;train&quot;</span>][<span class="hljs-string">&quot;text&quot;</span>], ds[<span class="hljs-string">&quot;train&quot;</span>][<span class="hljs-string">&quot;label&quot;</span>])<!-- HTML_TAG_END --></pre></div>
<p>Following the convention in the <code>TextClassificationPipeline</code> of <code>transformers</code> our pipeline should be callable and return a list of dictionaries. In addition we use the <code>task</code> attribute to check if the pipeline is compatible with the <code>evaluator</code>. We can write a small wrapper class for that purpose:</p>

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	<pre><!-- HTML_TAG_START --><span class="hljs-keyword">class</span> <span class="hljs-title class_">ScikitEvalPipeline</span>:
    <span class="hljs-keyword">def</span> <span class="hljs-title function_">__init__</span>(<span class="hljs-params">self, pipeline</span>):
        self.pipeline = pipeline
        self.task = <span class="hljs-string">&quot;text-classification&quot;</span>

    <span class="hljs-keyword">def</span> <span class="hljs-title function_">__call__</span>(<span class="hljs-params">self, input_texts, **kwargs</span>):
        <span class="hljs-keyword">return</span> [{<span class="hljs-string">&quot;label&quot;</span>: p} <span class="hljs-keyword">for</span> p <span class="hljs-keyword">in</span> self.pipeline.predict(input_texts)]

pipe = ScikitEvalPipeline(text_clf)<!-- HTML_TAG_END --></pre></div>
<p>We can now pass this <code>pipeline</code> to the <code>evaluator</code>:</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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	<pre><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> evaluate <span class="hljs-keyword">import</span> evaluator

<span class="hljs-built_in">eval</span> = evaluator(<span class="hljs-string">&quot;text-classification&quot;</span>)
<span class="hljs-built_in">eval</span>.compute(pipe, ds[<span class="hljs-string">&quot;test&quot;</span>], <span class="hljs-string">&quot;accuracy&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>{<span class="hljs-string">&#x27;accuracy&#x27;</span>: <span class="hljs-number">0.82956</span>}<!-- HTML_TAG_END --></pre></div>
<p>Implementing that simple wrapper is all that’s needed to use any model from any framework with the <code>evaluator</code>. In the <code>__call__</code> you can implement all logic necessary for efficient forward passes through your model.</p>
<h2 class="relative group"><a id="spacy" 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="#spacy"><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>Spacy
	</span></h2>

<p>We’ll use the <code>polarity</code> feature of the <code>spacytextblob</code> project to get a simple sentiment analyzer. First you’ll need to install the project and download the resources:</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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	<pre><!-- HTML_TAG_START -->pip install spacytextblob
python -m textblob.download_corpora
python -m spacy download en_core_web_sm<!-- HTML_TAG_END --></pre></div>
<p>Then we can simply load the <code>nlp</code> pipeline and add the <code>spacytextblob</code> pipeline:</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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	<pre><!-- HTML_TAG_START --><span class="hljs-keyword">import</span> spacy

nlp = spacy.load(<span class="hljs-string">&#x27;en_core_web_sm&#x27;</span>)
nlp.add_pipe(<span class="hljs-string">&#x27;spacytextblob&#x27;</span>)<!-- HTML_TAG_END --></pre></div>
<p>This snippet shows how we can use the <code>polarity</code> feature added with <code>spacytextblob</code> to get the sentiment of a text:</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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	<pre><!-- HTML_TAG_START -->texts = [<span class="hljs-string">&quot;This movie is horrible&quot;</span>, <span class="hljs-string">&quot;This movie is awesome&quot;</span>]
results = nlp.pipe(texts)

<span class="hljs-keyword">for</span> txt, res <span class="hljs-keyword">in</span> <span class="hljs-built_in">zip</span>(texts, results):
    <span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;<span class="hljs-subst">{text}</span> | Polarity: <span class="hljs-subst">{res._.blob.polarity}</span>&quot;</span>)<!-- HTML_TAG_END --></pre></div>
<p>Now we can wrap it in a simple wrapper class like in the Scikit-Learn example before. It just has to return a list of dictionaries with the predicted lables. If the polarity is larger than 0 we’ll predict positive sentiment and negative otherwise:</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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	<pre><!-- HTML_TAG_START --><span class="hljs-keyword">class</span> <span class="hljs-title class_">SpacyEvalPipeline</span>:
    <span class="hljs-keyword">def</span> <span class="hljs-title function_">__init__</span>(<span class="hljs-params">self, nlp</span>):
        self.nlp = nlp
        self.task = <span class="hljs-string">&quot;text-classification&quot;</span>

    <span class="hljs-keyword">def</span> <span class="hljs-title function_">__call__</span>(<span class="hljs-params">self, input_texts, **kwargs</span>):
        results =[]
        <span class="hljs-keyword">for</span> p <span class="hljs-keyword">in</span> self.nlp.pipe(input_texts):
            <span class="hljs-keyword">if</span> p._.blob.polarity&gt;=<span class="hljs-number">0</span>:
                results.append({<span class="hljs-string">&quot;label&quot;</span>: <span class="hljs-number">1</span>})
            <span class="hljs-keyword">else</span>:
                results.append({<span class="hljs-string">&quot;label&quot;</span>: <span class="hljs-number">0</span>})
        <span class="hljs-keyword">return</span> results

pipe = SpacyEvalPipeline(nlp)<!-- HTML_TAG_END --></pre></div>
<p>That class is compatible with the <code>evaluator</code> and we can use the same instance from the previous examlpe along with the IMDb test set:</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-built_in">eval</span>.compute(pipe, ds[<span class="hljs-string">&quot;test&quot;</span>], <span class="hljs-string">&quot;accuracy&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>{<span class="hljs-string">&#x27;accuracy&#x27;</span>: <span class="hljs-number">0.6914</span>}<!-- HTML_TAG_END --></pre></div>
<p>This will take a little longer than the Scikit-Learn example but after roughly 10-15min you will have the evaluation results!</p>


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