import{s as wt,n as Jt,o as Ut}from"../chunks/scheduler.5f3e6389.js";import{S as jt,i as Ct,e as M,s as i,c as a,h as bt,a as u,d as l,b as n,f as Tt,g as m,j as y,k as st,l as vt,m as s,n as p,t as r,o as c,p as o}from"../chunks/index.373ab25c.js";import{C as It,H as f,E as _t}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.c814c47f.js";import{C as $}from"../chunks/CodeBlock.32836ad5.js";function kt(it){let g,ce,pe,oe,T,Me,w,ue,J,fe,U,ye,j,nt="There are two types of metrics in Lighteval:",ge,C,$e,b,at="<li><strong>Purpose</strong>: Evaluate individual samples/predictions</li> <li><strong>Input</strong>: Takes a <code>Doc</code> and <code>ModelResponse</code> (model’s prediction)</li> <li><strong>Output</strong>: Returns a float or boolean value for that specific sample</li> <li><strong>Example</strong>: Checking if a model’s answer matches the correct answer for one sample</li>",he,v,de,I,mt=`<li><strong>Purpose</strong>: Compute final scores across the entire dataset/corpus</li> <li><strong>Input</strong>: Takes the results from all sample-level evaluations</li> <li><strong>Output</strong>: Returns a single score representing overall performance</li> <li><strong>Examples</strong>:
<ul><li>Simple aggregation: Calculating average accuracy across all test samples</li> <li>Complex metrics: BLEU score where sample-level metric prepares data (tokenization, etc.) and corpus-level metric computes the actual BLEU score</li></ul></li>`,Te,_,we,k,pt=`First, check if you can use one of the parameterized functions in
<a href="package_reference/metrics#corpus-metrics">Corpus Metrics</a> or
<a href="package_reference/metrics#sample-metrics">Sample Metrics</a>.`,Je,G,rt="If not, you can use the <code>custom_task</code> system to register your new metric.",Ue,h,ct='<p>To see an example of a custom metric added along with a custom task, look at the <a href="https://github.com/huggingface/lighteval/tree/main/examples/custom_tasks/ifeval" rel="nofollow">IFEval custom task</a>.</p>',je,d,ot=`<p>To contribute your custom metric to the Lighteval repository, you would first need
to install the required dev dependencies by running <code>pip install -e .[dev]</code>
and then run <code>pre-commit install</code> to install the pre-commit hooks.</p>`,Ce,V,be,E,ve,Z,Mt=`Create a new Python file which should contain the full logic of your metric.
The file also needs to start with these imports:`,Ie,B,_e,x,ke,X,ut=`You need to define a sample-level metric. All sample-level metrics will have the same signature, taking a
<code>~lighteval.types.Doc</code> and a <code>~lighteval.types.ModelResponse</code>. The metric should return a float or a
boolean.`,Ge,S,Ve,R,Ee,Y,Ze,N,ft="If you want to return multiple metrics per sample, you need to return a dictionary with the metrics as keys and the values as values:",Be,z,xe,W,Xe,A,yt="You can define an aggregation function if needed. A common aggregation function is <code>np.mean</code>:",Se,Q,Re,L,Ye,q,Ne,H,gt=`If it’s a sample-level metric, you can use the following code
with <a href="/docs/lighteval/pr_1015/en/package_reference/metrics#lighteval.metrics.utils.metric_utils.SampleLevelMetric">SampleLevelMetric</a>:`,ze,F,We,D,Ae,P,$t=`If your metric defines multiple metrics per sample, you can use the following code
with <a href="/docs/lighteval/pr_1015/en/package_reference/metrics#lighteval.metrics.utils.metric_utils.SampleLevelMetricGrouping">SampleLevelMetricGrouping</a>:`,Qe,O,Le,K,qe,ee,ht=`To finish, add the following code so that it adds your metric to our metrics list
when loaded as a module:`,He,te,Fe,le,De,se,Pe,ie,dt="You can then give your custom metric to Lighteval by using <code>--custom-tasks path_to_your_file</code> when launching it after adding it to the task config.",Oe,ne,Ke,ae,et,me,tt,re,lt;return T=new It({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),w=new f({props:{title:"Adding a New Metric",local:"adding-a-new-metric",headingTag:"h1"}}),J=new f({props:{title:"Before You Start",local:"before-you-start",headingTag:"h2"}}),U=new f({props:{title:"Two different types of metrics",local:"two-different-types-of-metrics",headingTag:"h3"}}),C=new f({props:{title:"Sample-Level Metrics",local:"sample-level-metrics",headingTag:"h4"}}),v=new f({props:{title:"Corpus-Level Metrics",local:"corpus-level-metrics",headingTag:"h4"}}),_=new f({props:{title:"Check Existing Metrics",local:"check-existing-metrics",headingTag:"h3"}}),V=new f({props:{title:"Creating a Custom Metric",local:"creating-a-custom-metric",headingTag:"h2"}}),E=new f({props:{title:"Step 1: Create the Metric File",local:"step-1-create-the-metric-file",headingTag:"h3"}}),B=new $({props:{code:"ZnJvbSUyMGFlbnVtJTIwaW1wb3J0JTIwZXh0ZW5kX2VudW0lMEFmcm9tJTIwbGlnaHRldmFsLm1ldHJpY3MlMjBpbXBvcnQlMjBNZXRyaWNz",highlighted:`<span class="hljs-keyword">from</span> aenum <span class="hljs-keyword">import</span> extend_enum
<span class="hljs-keyword">from</span> lighteval.metrics <span class="hljs-keyword">import</span> Metrics`,wrap:!1}}),x=new f({props:{title:"Step 2: Define the Sample-Level Metric",local:"step-2-define-the-sample-level-metric",headingTag:"h3"}}),S=new f({props:{title:"Single Metric Example",local:"single-metric-example",headingTag:"h4"}}),R=new $({props:{code:"ZGVmJTIwY3VzdG9tX21ldHJpYyhkb2MlM0ElMjBEb2MlMkMlMjBtb2RlbF9yZXNwb25zZSUzQSUyME1vZGVsUmVzcG9uc2UpJTIwLSUzRSUyMGJvb2wlM0ElMEElMjAlMjAlMjAlMjByZXNwb25zZSUyMCUzRCUyMG1vZGVsX3Jlc3BvbnNlLmZpbmFsX3RleHQlNUIwJTVEJTBBJTIwJTIwJTIwJTIwcmV0dXJuJTIwcmVzcG9uc2UlMjAlM0QlM0QlMjBkb2MuY2hvaWNlcyU1QmRvYy5nb2xkX2luZGV4JTVE",highlighted:`<span class="hljs-keyword">def</span> <span class="hljs-title function_">custom_metric</span>(<span class="hljs-params">doc: Doc, model_response: ModelResponse</span>) -&gt; <span class="hljs-built_in">bool</span>:
    response = model_response.final_text[<span class="hljs-number">0</span>]
    <span class="hljs-keyword">return</span> response == doc.choices[doc.gold_index]`,wrap:!1}}),Y=new f({props:{title:"Multiple Metrics Example",local:"multiple-metrics-example",headingTag:"h4"}}),z=new $({props:{code:"ZGVmJTIwY3VzdG9tX21ldHJpYyhkb2MlM0ElMjBEb2MlMkMlMjBtb2RlbF9yZXNwb25zZSUzQSUyME1vZGVsUmVzcG9uc2UpJTIwLSUzRSUyMGRpY3QlM0ElMEElMjAlMjAlMjAlMjByZXNwb25zZSUyMCUzRCUyMG1vZGVsX3Jlc3BvbnNlLmZpbmFsX3RleHQlNUIwJTVEJTBBJTIwJTIwJTIwJTIwcmV0dXJuJTIwJTdCJTIyYWNjdXJhY3klMjIlM0ElMjByZXNwb25zZSUyMCUzRCUzRCUyMGRvYy5jaG9pY2VzJTVCZG9jLmdvbGRfaW5kZXglNUQlMkMlMjAlMjJvdGhlcl9tZXRyaWMlMjIlM0ElMjAwLjUlN0Q=",highlighted:`<span class="hljs-keyword">def</span> <span class="hljs-title function_">custom_metric</span>(<span class="hljs-params">doc: Doc, model_response: ModelResponse</span>) -&gt; <span class="hljs-built_in">dict</span>:
    response = model_response.final_text[<span class="hljs-number">0</span>]
    <span class="hljs-keyword">return</span> {<span class="hljs-string">&quot;accuracy&quot;</span>: response == doc.choices[doc.gold_index], <span class="hljs-string">&quot;other_metric&quot;</span>: <span class="hljs-number">0.5</span>}`,wrap:!1}}),W=new f({props:{title:"Step 3: Define Aggregation Function (Optional)",local:"step-3-define-aggregation-function-optional",headingTag:"h3"}}),Q=new $({props:{code:"ZGVmJTIwYWdnX2Z1bmN0aW9uKGl0ZW1zKSUzQSUwQSUyMCUyMCUyMCUyMGZsYXRfaXRlbXMlMjAlM0QlMjAlNUJpdGVtJTIwZm9yJTIwc3VibGlzdCUyMGluJTIwaXRlbXMlMjBmb3IlMjBpdGVtJTIwaW4lMjBzdWJsaXN0JTVEJTBBJTIwJTIwJTIwJTIwc2NvcmUlMjAlM0QlMjBzdW0oZmxhdF9pdGVtcyklMjAlMkYlMjBsZW4oZmxhdF9pdGVtcyklMEElMjAlMjAlMjAlMjByZXR1cm4lMjBzY29yZQ==",highlighted:`<span class="hljs-keyword">def</span> <span class="hljs-title function_">agg_function</span>(<span class="hljs-params">items</span>):
    flat_items = [item <span class="hljs-keyword">for</span> sublist <span class="hljs-keyword">in</span> items <span class="hljs-keyword">for</span> item <span class="hljs-keyword">in</span> sublist]
    score = <span class="hljs-built_in">sum</span>(flat_items) / <span class="hljs-built_in">len</span>(flat_items)
    <span class="hljs-keyword">return</span> score`,wrap:!1}}),L=new f({props:{title:"Step 4: Create the Metric Object",local:"step-4-create-the-metric-object",headingTag:"h3"}}),q=new f({props:{title:"Single Metric",local:"single-metric",headingTag:"h4"}}),F=new $({props:{code:"bXlfY3VzdG9tX21ldHJpYyUyMCUzRCUyMFNhbXBsZUxldmVsTWV0cmljKCUwQSUyMCUyMCUyMCUyMG1ldHJpY19uYW1lJTNEJTIyY3VzdG9tX2FjY3VyYWN5JTIyJTJDJTBBJTIwJTIwJTIwJTIwaGlnaGVyX2lzX2JldHRlciUzRFRydWUlMkMlMEElMjAlMjAlMjAlMjBjYXRlZ29yeSUzRFNhbXBsaW5nTWV0aG9kLkdFTkVSQVRJVkUlMkMlMEElMjAlMjAlMjAlMjBzYW1wbGVfbGV2ZWxfZm4lM0RjdXN0b21fbWV0cmljJTJDJTBBJTIwJTIwJTIwJTIwY29ycHVzX2xldmVsX2ZuJTNEYWdnX2Z1bmN0aW9uJTJDJTBBKQ==",highlighted:`my_custom_metric = SampleLevelMetric(
    metric_name=<span class="hljs-string">&quot;custom_accuracy&quot;</span>,
    higher_is_better=<span class="hljs-literal">True</span>,
    category=SamplingMethod.GENERATIVE,
    sample_level_fn=custom_metric,
    corpus_level_fn=agg_function,
)`,wrap:!1}}),D=new f({props:{title:"Multiple Metrics",local:"multiple-metrics",headingTag:"h4"}}),O=new $({props:{code:"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",highlighted:`custom_metric = SampleLevelMetricGrouping(
    metric_name=[<span class="hljs-string">&quot;accuracy&quot;</span>, <span class="hljs-string">&quot;response_length&quot;</span>, <span class="hljs-string">&quot;confidence&quot;</span>],
    higher_is_better={
        <span class="hljs-string">&quot;accuracy&quot;</span>: <span class="hljs-literal">True</span>,
        <span class="hljs-string">&quot;response_length&quot;</span>: <span class="hljs-literal">False</span>,  <span class="hljs-comment"># Shorter responses might be better</span>
        <span class="hljs-string">&quot;confidence&quot;</span>: <span class="hljs-literal">True</span>
    },
    category=SamplingMethod.GENERATIVE,
    sample_level_fn=custom_metric,
    corpus_level_fn={
        <span class="hljs-string">&quot;accuracy&quot;</span>: np.mean,
        <span class="hljs-string">&quot;response_length&quot;</span>: np.mean,
        <span class="hljs-string">&quot;confidence&quot;</span>: np.mean,
    },
)`,wrap:!1}}),K=new f({props:{title:"Step 5: Register the Metric",local:"step-5-register-the-metric",headingTag:"h3"}}),te=new $({props:{code:"JTIzJTIwQWRkcyUyMHRoZSUyMG1ldHJpYyUyMHRvJTIwdGhlJTIwbWV0cmljJTIwbGlzdCElMEFleHRlbmRfZW51bShNZXRyaWNzJTJDJTIwJTIyQ1VTVE9NX0FDQ1VSQUNZJTIyJTJDJTIwbXlfY3VzdG9tX21ldHJpYyklMEElMEFpZiUyMF9fbmFtZV9fJTIwJTNEJTNEJTIwJTIyX19tYWluX18lMjIlM0ElMEElMjAlMjAlMjAlMjBwcmludCglMjJJbXBvcnRlZCUyMG1ldHJpYyUyMik=",highlighted:`<span class="hljs-comment"># Adds the metric to the metric list!</span>
extend_enum(Metrics, <span class="hljs-string">&quot;CUSTOM_ACCURACY&quot;</span>, my_custom_metric)

<span class="hljs-keyword">if</span> __name__ == <span class="hljs-string">&quot;__main__&quot;</span>:
    <span class="hljs-built_in">print</span>(<span class="hljs-string">&quot;Imported metric&quot;</span>)`,wrap:!1}}),le=new f({props:{title:"Using Your Custom Metric",local:"using-your-custom-metric",headingTag:"h2"}}),se=new f({props:{title:"With Custom Tasks",local:"with-custom-tasks",headingTag:"h3"}}),ne=new $({props:{code:"bGlnaHRldmFsJTIwYWNjZWxlcmF0ZSUyMCU1QyUwQSUyMCUyMCUyMCUyMCUyMm1vZGVsX25hbWUlM0RvcGVuYWktY29tbXVuaXR5JTJGZ3B0MiUyMiUyMCU1QyUwQSUyMCUyMCUyMCUyMCUyMmxlYWRlcmJvYXJkJTdDdHJ1dGhmdWxxYSUzQW1jJTdDMCUyMiUyMCU1QyUwQSUyMCUyMCUyMCUyMC0tY3VzdG9tLXRhc2tzJTIwcGF0aF90b195b3VyX21ldHJpY19maWxlLnB5",highlighted:`lighteval accelerate \\
    <span class="hljs-string">&quot;model_name=openai-community/gpt2&quot;</span> \\
    <span class="hljs-string">&quot;leaderboard|truthfulqa:mc|0&quot;</span> \\
    --custom-tasks path_to_your_metric_file.py`,wrap:!1}}),ae=new $({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> lighteval.tasks.lighteval_task <span class="hljs-keyword">import</span> LightevalTaskConfig

task = LightevalTaskConfig(
    name=<span class="hljs-string">&quot;my_custom_task&quot;</span>,
    suite=[<span class="hljs-string">&quot;community&quot;</span>],
    metric=[my_custom_metric],  <span class="hljs-comment"># Use your custom metric here</span>
    prompt_function=my_prompt_function,
    hf_repo=<span class="hljs-string">&quot;my_dataset&quot;</span>,
    evaluation_splits=[<span class="hljs-string">&quot;test&quot;</span>]
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