import{s as ha,o as fa,n as ua}from"../chunks/scheduler.3a17fb72.js";import{S as va,i as _a,e as r,s as a,c as d,h as $a,a as l,d as n,b as o,f as b,g as c,j as v,k as x,l as t,m as g,n as m,t as p,o as h,p as f}from"../chunks/index.093f8863.js";import{C as ba,H as pe,E as xa}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.fc4e8e00.js";import{D as L}from"../chunks/Docstring.531d9a77.js";import{C as ka}from"../chunks/CodeBlock.3e18ce07.js";import{E as Ca}from"../chunks/ExampleCodeBlock.df84a850.js";function La(Se){let $,q="Example:",E,T,y;return T=new ka({props:{code:"dHJhY2tlciUyMCUzRCUyMEV2YWx1YXRpb25UcmFja2VyKCUwQSUyMCUyMCUyMCUyMG91dHB1dF9kaXIlM0QlMjIuJTJGZXZhbF9yZXN1bHRzJTIyJTJDJTBBJTIwJTIwJTIwJTIwcHVzaF90b19odWIlM0RUcnVlJTJDJTBBJTIwJTIwJTIwJTIwaHViX3Jlc3VsdHNfb3JnJTNEJTIybXktb3JnJTIyJTJDJTBBJTIwJTIwJTIwJTIwc2F2ZV9kZXRhaWxzJTNEVHJ1ZSUwQSklMEElMEElMjMlMjBMb2clMjBldmFsdWF0aW9uJTIwcmVzdWx0cyUwQXRyYWNrZXIubWV0cmljc19sb2dnZXIuYWRkX21ldHJpYyglMjJhY2N1cmFjeSUyMiUyQyUyMDAuODUpJTBBdHJhY2tlci5kZXRhaWxzX2xvZ2dlci5hZGRfZGV0YWlsKHRhc2tfbmFtZSUzRCUyMnFhJTIyJTJDJTIwcHJlZGljdGlvbiUzRCUyMlBhcmlzJTIyKSUwQSUwQSUyMyUyMFNhdmUlMjBhbGwlMjByZXN1bHRzJTBBdHJhY2tlci5zYXZlKCk=",highlighted:`tracker = EvaluationTracker(
    output_dir=<span class="hljs-string">&quot;./eval_results&quot;</span>,
    push_to_hub=<span class="hljs-literal">True</span>,
    hub_results_org=<span class="hljs-string">&quot;my-org&quot;</span>,
    save_details=<span class="hljs-literal">True</span>
)

<span class="hljs-comment"># Log evaluation results</span>
tracker.metrics_logger.add_metric(<span class="hljs-string">&quot;accuracy&quot;</span>, <span class="hljs-number">0.85</span>)
tracker.details_logger.add_detail(task_name=<span class="hljs-string">&quot;qa&quot;</span>, prediction=<span class="hljs-string">&quot;Paris&quot;</span>)

<span class="hljs-comment"># Save all results</span>
tracker.save()`,wrap:!1}}),{c(){$=r("p"),$.textContent=q,E=a(),d(T.$$.fragment)},l(_){$=l(_,"P",{"data-svelte-h":!0}),v($)!=="svelte-11lpom8"&&($.textContent=q),E=o(_),c(T.$$.fragment,_)},m(_,w){g(_,$,w),g(_,E,w),m(T,_,w),y=!0},p:ua,i(_){y||(p(T.$$.fragment,_),y=!0)},o(_){h(T.$$.fragment,_),y=!1},d(_){_&&(n($),n(E)),f(T,_)}}}function Ta(Se){let $,q,E,T,y,_,w,ze,R,Ne,s,W,mt,he,Rt="Tracks and manages evaluation results, metrics, and logging for model evaluations.",pt,fe,Wt="The EvaluationTracker coordinates multiple specialized loggers to track different aspects of model evaluation:",ht,ue,Zt="<li>Details Logger (DetailsLogger): Records per-sample evaluation details and predictions</li> <li>Metrics Logger (MetricsLogger): Tracks aggregate evaluation metrics and scores</li> <li>Versions Logger (VersionsLogger): Records task and dataset versions</li> <li>General Config Logger (GeneralConfigLogger): Stores overall evaluation configuration</li> <li>Task Config Logger (TaskConfigLogger): Maintains per-task configuration details</li>",ft,ve,Xt="The tracker can save results locally and optionally push them to:",ut,_e,At="<li>Hugging Face Hub as datasets</li> <li>TensorBoard for visualization</li> <li>Trackio or Weights &amp; Biases for experiment tracking</li>",vt,V,_t,J,Z,$t,$e,Yt="Aggregates and returns all the logger’s experiment information in a dictionary.",bt,be,Qt="This function should be used to gather and display said information at the end of an evaluation run.",xt,G,X,kt,xe,Ot="Pushes the experiment details (all the model predictions for every step) to the hub.",Ct,P,A,Lt,ke,Kt="Fully updates the details repository metadata card for the currently evaluated model",Tt,U,Y,yt,Ce,ea="Saves the experiment information and results to files, and to the hub if requested.",qe,Q,Re,k,O,wt,Le,ta="Tracks general configuration and runtime information for model evaluations.",Dt,Te,aa=`This logger captures key configuration parameters, model details, and timing information
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<p>Dictionary containing all experiment information including config, results, versions, and summaries</p>
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<p>dict</p>
`}}),X=new L({props:{name:"push_to_hub",anchor:"lighteval.logging.evaluation_tracker.EvaluationTracker.push_to_hub",parameters:[{name:"date_id",val:": str"},{name:"details",val:": dict"},{name:"results_dict",val:": dict"}],source:"https://github.com/huggingface/lighteval/blob/main/src/lighteval/logging/evaluation_tracker.py#L387"}}),A=new L({props:{name:"recreate_metadata_card",anchor:"lighteval.logging.evaluation_tracker.EvaluationTracker.recreate_metadata_card",parameters:[{name:"repo_id",val:": str"}],parametersDescription:[{anchor:"lighteval.logging.evaluation_tracker.EvaluationTracker.recreate_metadata_card.repo_id",description:"<strong>repo_id</strong> (str) &#x2014; Details dataset repository path on the hub (<code>org/dataset</code>)",name:"repo_id"}],source:"https://github.com/huggingface/lighteval/blob/main/src/lighteval/logging/evaluation_tracker.py#L454"}}),Y=new L({props:{name:"save",anchor:"lighteval.logging.evaluation_tracker.EvaluationTracker.save",parameters:[],source:"https://github.com/huggingface/lighteval/blob/main/src/lighteval/logging/evaluation_tracker.py#L247"}}),Q=new pe({props:{title:"GeneralConfigLogger",local:"lighteval.logging.info_loggers.GeneralConfigLogger",headingTag:"h2"}}),O=new L({props:{name:"class lighteval.logging.info_loggers.GeneralConfigLogger",anchor:"lighteval.logging.info_loggers.GeneralConfigLogger",parameters:[],parametersDescription:[{anchor:"lighteval.logging.info_loggers.GeneralConfigLogger.lighteval_sha",description:`<strong>lighteval_sha</strong> (str) &#x2014; Git commit SHA of lighteval used for evaluation, enabling exact version reproducibility.
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<ul>
<li>If &lt;= 1: Single evaluation with seed=0</li>
<li>If &gt; 1: Multiple evaluations with different few-shot samplings (HELM-style)</li>
</ul>`,name:"num_fewshot_seeds"},{anchor:"lighteval.logging.info_loggers.GeneralConfigLogger.max_samples",description:`<strong>max_samples</strong> (int, optional) &#x2014; Maximum number of samples to evaluate per task.
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Used to cross-reference with scheduler logs.`,name:"job_id"},{anchor:"lighteval.logging.info_loggers.GeneralConfigLogger.start_time",description:`<strong>start_time</strong> (float) &#x2014; Unix timestamp when evaluation started.
Automatically set during logger initialization.`,name:"start_time"},{anchor:"lighteval.logging.info_loggers.GeneralConfigLogger.end_time",description:`<strong>end_time</strong> (float) &#x2014; Unix timestamp when evaluation completed.
Set by calling log_end_time().`,name:"end_time"},{anchor:"lighteval.logging.info_loggers.GeneralConfigLogger.total_evaluation_time_secondes",description:`<strong>total_evaluation_time_secondes</strong> (str) &#x2014; Total runtime in seconds.
Calculated as end_time - start_time.`,name:"total_evaluation_time_secondes"},{anchor:"lighteval.logging.info_loggers.GeneralConfigLogger.model_config",description:`<strong>model_config</strong> (ModelConfig) &#x2014; Complete model configuration settings.
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Example: {&#x201C;winogrande|winogrande_xl&#x201D;: {&#x201C;accuracy&#x201D;: 0.5}&#x200C;}`,name:"metric_aggregated"}],source:"https://github.com/huggingface/lighteval/blob/main/src/lighteval/logging/info_loggers.py#L309"}}),ie=new L({props:{name:"aggregate",anchor:"lighteval.logging.info_loggers.MetricsLogger.aggregate",parameters:[{name:"task_dict",val:": dict"},{name:"bootstrap_iters",val:": int = 1000"}],parametersDescription:[{anchor:"lighteval.logging.info_loggers.MetricsLogger.aggregate.task_dict",description:"<strong>task_dict</strong> (dict[str, LightevalTask]) &#x2014; used to determine what aggregation function to use for each metric",name:"task_dict"},{anchor:"lighteval.logging.info_loggers.MetricsLogger.aggregate.bootstrap_iters",description:"<strong>bootstrap_iters</strong> (int, optional) &#x2014; Number of runs used to run the statistical bootstrap. 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