lighteval/pr_953/en/_app/immutable/nodes/14.8ec8a4ef.js

import{s as ci,n as pi,o as gi}from"../chunks/scheduler.7da89386.js";import{S as di,i as ui,g as a,s as l,r as m,A as hi,h as i,f as t,c as n,j as h,u as c,x as f,k as v,y as s,a as o,v as p,d as g,t as d,w as u}from"../chunks/index.20910acc.js";import{D as _}from"../chunks/Docstring.3e3a43d0.js";import{H as $,E as vi}from"../chunks/getInferenceSnippets.174c92b4.js";function _i(xa){let E,rs,es,ss,Le,ls,Me,ns,we,as,ke,Te,is,De,os,P,Ee,on,mr,ya="Metric computed over the whole corpora, with computations happening at the aggregation phase",ms,Pe,cs,R,Re,mn,cr,Ca="Metric computed per sample, then aggregated over the corpus",ps,je,gs,j,Ie,cn,pr,La=`Some metrics are more advantageous to compute together at once.
For example, if a costly preprocessing is the same for all metrics, it makes more sense to compute it once.`,ds,Ae,us,I,Ne,pn,gr,Ma="MetricGrouping computed over the whole corpora, with computations happening at the aggregation phase",hs,Se,vs,A,Ve,gn,dr,wa="MetricGrouping are computed per sample, then aggregated over the corpus",_s,Je,fs,ze,$s,N,Be,dn,ee,Fe,un,ur,ka="Computes the metric score over all the corpus generated items, by using the scikit learn implementation.",bs,Oe,xs,S,Ue,hn,te,Ge,vn,hr,Ta="Computes the metric score over all the corpus generated items.",ys,qe,Cs,V,He,_n,re,Ke,fn,vr,Da="Computes the metric score over all the corpus generated items, by using the sacrebleu implementation.",Ls,Ye,Ms,J,Xe,$n,se,Ze,bn,_r,Ea='Computes the Matthews Correlation Coefficient, using scikit learn (<a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html" rel="nofollow">doc</a>).',ws,Qe,ks,We,Ts,C,et,xn,le,tt,yn,fr,Pa="Computes the metric over a list of golds and predictions for one single sample.",Cn,ne,rt,Ln,$r,Ra="Compares two strings only.",Ds,st,Es,L,lt,Mn,ae,nt,wn,br,ja="Computes the metric over a list of golds and predictions for one single sample.",kn,ie,at,Tn,xr,Ia="Compares two strings only.",Ps,it,Rs,z,ot,Dn,oe,mt,En,yr,Aa=`Computes the log likelihood accuracy: is the choice with the highest logprob in <code>choices_logprob</code> present
in the <code>gold_ixs</code>?`,js,ct,Is,B,pt,Pn,me,gt,Rn,Cr,Na="Computes the log likelihood probability: chance of choosing the best choice.",As,dt,Ns,F,ut,jn,ce,ht,In,Lr,Sa="Computes the log likelihood probability: chance of choosing the best choice.",Ss,vt,Vs,O,_t,An,pe,ft,Nn,Mr,Va=`Computes the recall at the requested depth level: looks at the <code>n</code> best predicted choices (with the
highest log probabilities) and see if there is an actual gold among them.`,Js,$t,zs,U,bt,Sn,ge,xt,Vn,wr,Ja="Mean reciprocal rank. Measures the quality of a ranking of choices (ordered by correctness).",Bs,yt,Fs,G,Ct,Jn,de,Lt,zn,kr,za="Computes the metric(s) over a list of golds and predictions for one single sample.",Os,Mt,Us,q,wt,Bn,ue,kt,Fn,Tr,Ba="Computes the prediction, recall and f1 score using the bert scorer.",Gs,Tt,qs,H,Dt,On,M,Et,Un,Dr,Fa="Compute the extractiveness of the predictions.",Gn,Er,Oa=`This method calculates coverage, density, and compression scores for a single
prediction against the input text.`,Hs,Pt,Ks,K,Rt,qn,w,jt,Hn,Pr,Ua="Compute the faithfulness of the predictions.",Kn,Rr,Ga="The SummaCZS (Summary Content Zero-Shot) model is used with configurable granularity and model variation.",Ys,It,Xs,Y,At,Yn,he,Nt,Xn,jr,qa="Uses the stored BLEURT scorer to compute the score on the current sample.",Zs,St,Qs,X,Vt,Zn,ve,Jt,Qn,Ir,Ha="Computes the sentence level BLEU between the golds and each prediction, then takes the average.",Ws,zt,el,y,Bt,Wn,_e,Ft,ea,Ar,Ka="Computes all the requested metrics on the golds and prediction.",ta,k,Ot,ra,Nr,Ya="Compute the edit similarity between two lists of strings.",sa,Sr,Xa=`Edit similarity is also used in the paper
Lee, Katherine, et al.
“Deduplicating training data makes language models better.”
arXiv preprint arXiv:2107.06499 (2021).`,la,fe,Ut,na,Vr,Za="Compute the length of the longest common prefix.",tl,Gt,rl,qt,Ht,sl,Kt,ll,Z,Yt,aa,$e,Xt,ia,Jr,Qa=`Compute the score of a generative task using a llm as a judge.
The generative task can be multiturn with 2 turns max, in that case, we
return scores for turn 1 and 2. Also returns user_prompt and judgement
which are ignored later by the aggregator.`,nl,Zt,al,Q,Qt,oa,be,Wt,ma,zr,Wa=`Compute the score of a generative task using a llm as a judge.
The generative task can be multiturn with 2 turns max, in that case, we
return scores for turn 1 and 2. Also returns user_prompt and judgement
which are ignored later by the aggregator.`,il,er,ol,W,tr,ca,xe,rr,pa,Br,ei=`Computes the metric over a list of golds and predictions for one single sample.
It applies normalisation (if needed) to model prediction and gold, and takes the most frequent answer of all the available ones,
then compares it to the gold.`,ml,sr,cl,lr,pl,b,nr,ga,Fr,ti="A class representing a judge for evaluating answers using either the OpenAI or Transformers library.",da,Or,ri=`Methods:
evaluate_answer: Evaluates an answer using the OpenAI API or Transformers library.
<strong>lazy_load_client: Lazy loads the OpenAI client or Transformers pipeline.
</strong>call_api: Calls the API to get the judge’s response.
<strong>call_transformers: Calls the Transformers pipeline to get the judge’s response.
</strong>call_vllm: Calls the VLLM pipeline to get the judge’s response.`,ua,x,ar,ha,Ur,si="Transform a dictionary of lists into a list of dictionaries.",va,Gr,li=`Each dictionary in the output list will contain one element from each list in the input dictionary,
with the same keys as the input dictionary.`,_a,qr,ni="Example:",fa,Hr,ai=`<blockquote><blockquote><p>dict_of_lists_to_list_of_dicts({‘k’: [1, 2, 3], ‘k2’: [‘a’, ‘b’, ‘c’]})
[{‘k’: 1, ‘k2’: ‘a’}, {‘k’: 2, ‘k2’: ‘b’}, {‘k’: 3, ‘k2’: ‘c’}]</p></blockquote></blockquote>`,$a,ye,ir,ba,Kr,ii="Evaluates an answer using either Transformers or OpenAI API.",gl,or,dl,ts,ul;return Le=new $({props:{title:"Metrics",local:"metrics",headingTag:"h1"}}),Me=new $({props:{title:"Metrics",local:"metrics",headingTag:"h2"}}),we=new $({props:{title:"Metric",local:"lighteval.metrics.Metric",headingTag:"h3"}}),Te=new _({props:{name:"class lighteval.metrics.Metric",anchor:"lighteval.metrics.Metric",parameters:[{name:"metric_name",val:": str"},{name:"higher_is_better",val:": bool"},{name:"category",val:": SamplingMethod"},{name:"sample_level_fn",val:": lighteval.metrics.metrics_sample.SampleLevelComputation | lighteval.metrics.sample_preparator.Preparator"},{name:"corpus_level_fn",val:": typing.Union[lighteval.metrics.metrics_corpus.CorpusLevelComputation, typing.Callable]"},{name:"batched_compute",val:": bool = False"}],source:"https://github.com/huggingface/lighteval/blob/vr_953/src/lighteval/metrics/utils/metric_utils.py#L33"}}),De=new $({props:{title:"CorpusLevelMetric",local:"lighteval.metrics.utils.metric_utils.CorpusLevelMetric",headingTag:"h3"}}),Ee=new _({props:{name:"class lighteval.metrics.utils.metric_utils.CorpusLevelMetric",anchor:"lighteval.metrics.utils.metric_utils.CorpusLevelMetric",parameters:[{name:"metric_name",val:": str"},{name:"higher_is_better",val:": bool"},{name:"category",val:": SamplingMethod"},{name:"sample_level_fn",val:": lighteval.metrics.metrics_sample.SampleLevelComputation | lighteval.metrics.sample_preparator.Preparator"},{name:"corpus_level_fn",val:": typing.Union[lighteval.metrics.metrics_corpus.CorpusLevelComputation, typing.Callable]"},{name:"batched_compute",val:": bool = False"}],source:"https://github.com/huggingface/lighteval/blob/vr_953/src/lighteval/metrics/utils/metric_utils.py#L115"}}),Pe=new $({props:{title:"SampleLevelMetric",local:"lighteval.metrics.utils.metric_utils.SampleLevelMetric",headingTag:"h3"}}),Re=new _({props:{name:"class lighteval.metrics.utils.metric_utils.SampleLevelMetric",anchor:"lighteval.metrics.utils.metric_utils.SampleLevelMetric",parameters:[{name:"metric_name",val:": str"},{name:"higher_is_better",val:": bool"},{name:"category",val:": SamplingMethod"},{name:"sample_level_fn",val:": lighteval.metrics.metrics_sample.SampleLevelComputation | lighteval.metrics.sample_preparator.Preparator"},{name:"corpus_level_fn",val:": typing.Union[lighteval.metrics.metrics_corpus.CorpusLevelComputation, typing.Callable]"},{name:"batched_compute",val:": bool = False"}],source:"https://github.com/huggingface/lighteval/blob/vr_953/src/lighteval/metrics/utils/metric_utils.py#L122"}}),je=new $({props:{title:"MetricGrouping",local:"lighteval.metrics.utils.metric_utils.MetricGrouping",headingTag:"h3"}}),Ie=new _({props:{name:"class lighteval.metrics.utils.metric_utils.MetricGrouping",anchor:"lighteval.metrics.utils.metric_utils.MetricGrouping",parameters:[{name:"metric_name",val:": list"},{name:"higher_is_better",val:": dict"},{name:"category",val:": SamplingMethod"},{name:"sample_level_fn",val:": lighteval.metrics.metrics_sample.SampleLevelComputation | lighteval.metrics.sample_preparator.Preparator"},{name:"corpus_level_fn",val:": dict"},{name:"batched_compute",val:": bool = False"}],source:"https://github.com/huggingface/lighteval/blob/vr_953/src/lighteval/metrics/utils/metric_utils.py#L104"}}),Ae=new $({props:{title:"CorpusLevelMetricGrouping",local:"lighteval.metrics.utils.metric_utils.CorpusLevelMetricGrouping",headingTag:"h3"}}),Ne=new _({props:{name:"class lighteval.metrics.utils.metric_utils.CorpusLevelMetricGrouping",anchor:"lighteval.metrics.utils.metric_utils.CorpusLevelMetricGrouping",parameters:[{name:"metric_name",val:": list"},{name:"higher_is_better",val:": dict"},{name:"category",val:": SamplingMethod"},{name:"sample_level_fn",val:": lighteval.metrics.metrics_sample.SampleLevelComputation | lighteval.metrics.sample_preparator.Preparator"},{name:"corpus_level_fn",val:": dict"},{name:"batched_compute",val:": bool = False"}],source:"https://github.com/huggingface/lighteval/blob/vr_953/src/lighteval/metrics/utils/metric_utils.py#L129"}}),Se=new $({props:{title:"SampleLevelMetricGrouping",local:"lighteval.metrics.utils.metric_utils.SampleLevelMetricGrouping",headingTag:"h3"}}),Ve=new _({props:{name:"class lighteval.metrics.utils.metric_utils.SampleLevelMetricGrouping",anchor:"lighteval.metrics.utils.metric_utils.SampleLevelMetricGrouping",parameters:[{name:"metric_name",val:": list"},{name:"higher_is_better",val:": dict"},{name:"category",val:": SamplingMethod"},{name:"sample_level_fn",val:": lighteval.metrics.metrics_sample.SampleLevelComputation | lighteval.metrics.sample_preparator.Preparator"},{name:"corpus_level_fn",val:": dict"},{name:"batched_compute",val:": bool = False"}],source:"https://github.com/huggingface/lighteval/blob/vr_953/src/lighteval/metrics/utils/metric_utils.py#L136"}}),Je=new $({props:{title:"Corpus Metrics",local:"corpus-metrics",headingTag:"h2"}}),ze=new $({props:{title:"CorpusLevelF1Score",local:"lighteval.metrics.metrics_corpus.CorpusLevelF1Score",headingTag:"h3"}}),Be=new _({props:{name:"class lighteval.metrics.metrics_corpus.CorpusLevelF1Score",anchor:"lighteval.metrics.metrics_corpus.CorpusLevelF1Score",parameters:[{name:"average",val:": str"},{name:"num_classes",val:": int = 2"}],source:"https://github.com/huggingface/lighteval/blob/vr_953/src/lighteval/metrics/metrics_corpus.py#L81"}}),Fe=new _({props:{name:"compute_corpus",anchor:"lighteval.metrics.metrics_corpus.CorpusLevelF1Score.compute_corpus",parameters:[{name:"items",val:": list"}],source:"https://github.com/huggingface/lighteval/blob/vr_953/src/lighteval/metrics/metrics_corpus.py#L96"}}),Oe=new $({props:{title:"CorpusLevelPerplexityMetric",local:"lighteval.metrics.metrics_corpus.CorpusLevelPerplexityMetric",headingTag:"h3"}}),Ue=new _({props:{name:"class lighteval.metrics.metrics_corpus.CorpusLevelPerplexityMetric",anchor:"lighteval.metrics.metrics_corpus.CorpusLevelPerplexityMetric",parameters:[{name:"metric_type",val:": str"}],source:"https://github.com/huggingface/lighteval/blob/vr_953/src/lighteval/metrics/metrics_corpus.py#L150"}}),Ge=new _({props:{name:"compute_corpus",anchor:"lighteval.metrics.metrics_corpus.CorpusLevelPerplexityMetric.compute_corpus",parameters:[{name:"items",val:": list"}],source:"https://github.com/huggingface/lighteval/blob/vr_953/src/lighteval/metrics/metrics_corpus.py#L168"}}),qe=new $({props:{title:"CorpusLevelTranslationMetric",local:"lighteval.metrics.metrics_corpus.CorpusLevelTranslationMetric",headingTag:"h3"}}),He=new _({props:{name:"class lighteval.metrics.metrics_corpus.CorpusLevelTranslationMetric",anchor:"lighteval.metrics.metrics_corpus.CorpusLevelTranslationMetric",parameters:[{name:"metric_type",val:": str"},{name:"lang",val:": typing.Literal['zh', 'ja', 'ko', ''] = ''"}],source:"https://github.com/huggingface/lighteval/blob/vr_953/src/lighteval/metrics/metrics_corpus.py#L112"}}),Ke=new _({props:{name:"compute_corpus",anchor:"lighteval.metrics.metrics_corpus.CorpusLevelTranslationMetric.compute_corpus",parameters:[{name:"items",val:": list"}],source:"https://github.com/huggingface/lighteval/blob/vr_953/src/lighteval/metrics/metrics_corpus.py#L135"}}),Ye=new $({props:{title:"MatthewsCorrCoef",local:"lighteval.metrics.metrics_corpus.MatthewsCorrCoef",headingTag:"h3"}}),Xe=new _({props:{name:"class lighteval.metrics.metrics_corpus.MatthewsCorrCoef",anchor:"lighteval.metrics.metrics_corpus.MatthewsCorrCoef",parameters:[],source:"https://github.com/huggingface/lighteval/blob/vr_953/src/lighteval/metrics/metrics_corpus.py#L66"}}),Ze=new _({props:{name:"compute_corpus",anchor:"lighteval.metrics.metrics_corpus.MatthewsCorrCoef.compute_corpus",parameters:[{name:"items",val:": list"}],parametersDescription:[{anchor:"lighteval.metrics.metrics_corpus.MatthewsCorrCoef.compute_corpus.items",description:"<strong>items</strong> (list[dict]) &#x2014; List of GenerativeCorpusMetricInput",name:"items"}],source:"https://github.com/huggingface/lighteval/blob/vr_953/src/lighteval/metrics/metrics_corpus.py#L67",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>Score</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p>float</p>
`}}),Qe=new $({props:{title:"Sample Metrics",local:"sample-metrics",headingTag:"h2"}}),We=new $({props:{title:"ExactMatches",local:"lighteval.metrics.metrics_sample.ExactMatches",headingTag:"h3"}}),et=new _({props:{name:"class lighteval.metrics.metrics_sample.ExactMatches",anchor:"lighteval.metrics.metrics_sample.ExactMatches",parameters:[{name:"aggregation_function",val:": typing.Callable[[list[float]], float] = <built-in function max>"},{name:"normalize_gold",val:": typing.Optional[typing.Callable[[str], str]] = None"},{name:"normalize_pred",val:": typing.Optional[typing.Callable[[str], str]] = None"},{name:"strip_strings",val:": bool = False"},{name:"type_exact_match",val:": str = 'full'"}],source:"https://github.com/huggingface/lighteval/blob/vr_953/src/lighteval/metrics/metrics_sample.py#L81"}}),tt=new _({props:{name:"compute",anchor:"lighteval.metrics.metrics_sample.ExactMatches.compute",parameters:[{name:"doc",val:": Doc"},{name:"model_response",val:": ModelResponse"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"lighteval.metrics.metrics_sample.ExactMatches.compute.doc",description:"<strong>doc</strong> (Doc) &#x2014; The document containing gold references.",name:"doc"},{anchor:"lighteval.metrics.metrics_sample.ExactMatches.compute.model_response",description:"<strong>model_response</strong> (ModelResponse) &#x2014; The model&#x2019;s response containing predictions.",name:"model_response"},{anchor:"lighteval.metrics.metrics_sample.ExactMatches.compute.*kwargs",description:"*<strong>*kwargs</strong> &#x2014; Additional keyword arguments.",name:"*kwargs"}],source:"https://github.com/huggingface/lighteval/blob/vr_953/src/lighteval/metrics/metrics_sample.py#L118",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>Aggregated score over the current sample’s items.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p>float</p>
`}}),rt=new _({props:{name:"compute_one_item",anchor:"lighteval.metrics.metrics_sample.ExactMatches.compute_one_item",parameters:[{name:"gold",val:": str"},{name:"pred",val:": str"}],parametersDescription:[{anchor:"lighteval.metrics.metrics_sample.ExactMatches.compute_one_item.gold",description:"<strong>gold</strong> (str) &#x2014; One of the possible references",name:"gold"},{anchor:"lighteval.metrics.metrics_sample.ExactMatches.compute_one_item.pred",description:"<strong>pred</strong> (str) &#x2014; One of the possible predictions",name:"pred"}],source:"https://github.com/huggingface/lighteval/blob/vr_953/src/lighteval/metrics/metrics_sample.py#L137",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The exact match score. Will be 1 for a match, 0 otherwise.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p>float</p>
`}}),st=new $({props:{title:"F1_score",local:"lighteval.metrics.metrics_sample.F1_score",headingTag:"h3"}}),lt=new _({props:{name:"class lighteval.metrics.metrics_sample.F1_score",anchor:"lighteval.metrics.metrics_sample.F1_score",parameters:[{name:"aggregation_function",val:": typing.Callable[[list[float]], float] = <built-in function max>"},{name:"normalize_gold",val:": typing.Optional[typing.Callable[[str], str]] = None"},{name:"normalize_pred",val:": typing.Optional[typing.Callable[[str], str]] = None"},{name:"strip_strings",val:": bool = False"}],source:"https://github.com/huggingface/lighteval/blob/vr_953/src/lighteval/metrics/metrics_sample.py#L170"}}),nt=new _({props:{name:"compute",anchor:"lighteval.metrics.metrics_sample.F1_score.compute",parameters:[{name:"doc",val:": Doc"},{name:"model_response",val:": ModelResponse"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"lighteval.metrics.metrics_sample.F1_score.compute.doc",description:"<strong>doc</strong> (Doc) &#x2014; The document containing gold references.",name:"doc"},{anchor:"lighteval.metrics.metrics_sample.F1_score.compute.model_response",description:"<strong>model_response</strong> (ModelResponse) &#x2014; The model&#x2019;s response containing predictions.",name:"model_response"},{anchor:"lighteval.metrics.metrics_sample.F1_score.compute.*kwargs",description:"*<strong>*kwargs</strong> &#x2014; Additional keyword arguments.",name:"*kwargs"}],source:"https://github.com/huggingface/lighteval/blob/vr_953/src/lighteval/metrics/metrics_sample.py#L197",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>Aggregated score over the current sample’s items.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p>float</p>
`}}),at=new _({props:{name:"compute_one_item",anchor:"lighteval.metrics.metrics_sample.F1_score.compute_one_item",parameters:[{name:"gold",val:": str"},{name:"pred",val:": str"}],parametersDescription:[{anchor:"lighteval.metrics.metrics_sample.F1_score.compute_one_item.gold",description:"<strong>gold</strong> (str) &#x2014; One of the possible references",name:"gold"},{anchor:"lighteval.metrics.metrics_sample.F1_score.compute_one_item.pred",description:"<strong>pred</strong> (str) &#x2014; One of the possible predictions",name:"pred"}],source:"https://github.com/huggingface/lighteval/blob/vr_953/src/lighteval/metrics/metrics_sample.py#L217",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The f1 score over the bag of words, computed using nltk.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p>float</p>
`}}),it=new $({props:{title:"LoglikelihoodAcc",local:"lighteval.metrics.metrics_sample.LoglikelihoodAcc",headingTag:"h3"}}),ot=new _({props:{name:"class lighteval.metrics.metrics_sample.LoglikelihoodAcc",anchor:"lighteval.metrics.metrics_sample.LoglikelihoodAcc",parameters:[{name:"logprob_normalization",val:": lighteval.metrics.normalizations.LogProbCharNorm | lighteval.metrics.normalizations.LogProbTokenNorm | lighteval.metrics.normalizations.LogProbPMINorm | None = None"}],source:"https://github.com/huggingface/lighteval/blob/vr_953/src/lighteval/metrics/metrics_sample.py#L243"}}),mt=new _({props:{name:"compute",anchor:"lighteval.metrics.metrics_sample.LoglikelihoodAcc.compute",parameters:[{name:"doc",val:": Doc"},{name:"model_response",val:": ModelResponse"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"lighteval.metrics.metrics_sample.LoglikelihoodAcc.compute.doc",description:"<strong>doc</strong> (Doc) &#x2014; The document containing choices and gold indices.",name:"doc"},{anchor:"lighteval.metrics.metrics_sample.LoglikelihoodAcc.compute.model_response",description:"<strong>model_response</strong> (ModelResponse) &#x2014; The model&#x2019;s response containing logprobs.",name:"model_response"},{anchor:"lighteval.metrics.metrics_sample.LoglikelihoodAcc.compute.*kwargs",description:"*<strong>*kwargs</strong> &#x2014; Additional keyword arguments.",name:"*kwargs"}],source:"https://github.com/huggingface/lighteval/blob/vr_953/src/lighteval/metrics/metrics_sample.py#L254",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The eval score: 1 if the best log-prob choice is in gold, 0 otherwise.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p>int</p>
`}}),ct=new $({props:{title:"NormalizedMultiChoiceProbability",local:"lighteval.metrics.metrics_sample.NormalizedMultiChoiceProbability",headingTag:"h3"}}),pt=new _({props:{name:"class lighteval.metrics.metrics_sample.NormalizedMultiChoiceProbability",anchor:"lighteval.metrics.metrics_sample.NormalizedMultiChoiceProbability",parameters:[{name:"log_prob_normalization",val:": lighteval.metrics.normalizations.LogProbCharNorm | lighteval.metrics.normalizations.LogProbTokenNorm | lighteval.metrics.normalizations.LogProbPMINorm | None = None"},{name:"aggregation_function",val:": typing.Callable[[numpy.ndarray], float] = <function max at 0x7f29119b5470>"}],source:"https://github.com/huggingface/lighteval/blob/vr_953/src/lighteval/metrics/metrics_sample.py#L297"}}),gt=new _({props:{name:"compute",anchor:"lighteval.metrics.metrics_sample.NormalizedMultiChoiceProbability.compute",parameters:[{name:"doc",val:": Doc"},{name:"model_response",val:": ModelResponse"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"lighteval.metrics.metrics_sample.NormalizedMultiChoiceProbability.compute.doc",description:"<strong>doc</strong> (Doc) &#x2014; The document containing choices and gold indices.",name:"doc"},{anchor:"lighteval.metrics.metrics_sample.NormalizedMultiChoiceProbability.compute.model_response",description:"<strong>model_response</strong> (ModelResponse) &#x2014; The model&#x2019;s response containing logprobs.",name:"model_response"},{anchor:"lighteval.metrics.metrics_sample.NormalizedMultiChoiceProbability.compute.*kwargs",description:"*<strong>*kwargs</strong> &#x2014; Additional keyword arguments.",name:"*kwargs"}],source:"https://github.com/huggingface/lighteval/blob/vr_953/src/lighteval/metrics/metrics_sample.py#L313",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The probability of the best log-prob choice being a gold choice.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p>float</p>
`}}),dt=new $({props:{title:"Probability",local:"lighteval.metrics.metrics_sample.Probability",headingTag:"h3"}}),ut=new _({props:{name:"class lighteval.metrics.metrics_sample.Probability",anchor:"lighteval.metrics.metrics_sample.Probability",parameters:[{name:"normalization",val:": lighteval.metrics.normalizations.LogProbTokenNorm | None = None"},{name:"aggregation_function",val:": typing.Callable[[numpy.ndarray], float] = <function max at 0x7f29119b5470>"}],source:"https://github.com/huggingface/lighteval/blob/vr_953/src/lighteval/metrics/metrics_sample.py#L357"}}),ht=new _({props:{name:"compute",anchor:"lighteval.metrics.metrics_sample.Probability.compute",parameters:[{name:"doc",val:": Doc"},{name:"model_response",val:": ModelResponse"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"lighteval.metrics.metrics_sample.Probability.compute.doc",description:"<strong>doc</strong> (Doc) &#x2014; The document containing choices and gold indices.",name:"doc"},{anchor:"lighteval.metrics.metrics_sample.Probability.compute.model_response",description:"<strong>model_response</strong> (ModelResponse) &#x2014; The model&#x2019;s response containing logprobs.",name:"model_response"},{anchor:"lighteval.metrics.metrics_sample.Probability.compute.*kwargs",description:"*<strong>*kwargs</strong> &#x2014; Additional keyword arguments.",name:"*kwargs"}],source:"https://github.com/huggingface/lighteval/blob/vr_953/src/lighteval/metrics/metrics_sample.py#L373",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The probability of the best log-prob choice being a gold choice.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p>float</p>
`}}),vt=new $({props:{title:"Recall",local:"lighteval.metrics.metrics_sample.Recall",headingTag:"h3"}}),_t=new _({props:{name:"class lighteval.metrics.metrics_sample.Recall",anchor:"lighteval.metrics.metrics_sample.Recall",parameters:[{name:"k",val:": int"}],source:"https://github.com/huggingface/lighteval/blob/vr_953/src/lighteval/metrics/metrics_sample.py#L408"}}),ft=new _({props:{name:"compute",anchor:"lighteval.metrics.metrics_sample.Recall.compute",parameters:[{name:"doc",val:": Doc"},{name:"model_response",val:": ModelResponse"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"lighteval.metrics.metrics_sample.Recall.compute.doc",description:"<strong>doc</strong> (Doc) &#x2014; The document containing choices and gold indices.",name:"doc"},{anchor:"lighteval.metrics.metrics_sample.Recall.compute.model_response",description:"<strong>model_response</strong> (ModelResponse) &#x2014; The model&#x2019;s response containing logprobs.",name:"model_response"},{anchor:"lighteval.metrics.metrics_sample.Recall.compute.*kwargs",description:"*<strong>*kwargs</strong> &#x2014; Additional keyword arguments.",name:"*kwargs"}],source:"https://github.com/huggingface/lighteval/blob/vr_953/src/lighteval/metrics/metrics_sample.py#L418",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>Score: 1 if one of the top level predicted choices was correct, 0 otherwise.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p>int</p>
`}}),$t=new $({props:{title:"MRR",local:"lighteval.metrics.metrics_sample.MRR",headingTag:"h3"}}),bt=new _({props:{name:"class lighteval.metrics.metrics_sample.MRR",anchor:"lighteval.metrics.metrics_sample.MRR",parameters:[{name:"length_normalization",val:": bool = False"}],source:"https://github.com/huggingface/lighteval/blob/vr_953/src/lighteval/metrics/metrics_sample.py#L438"}}),xt=new _({props:{name:"compute",anchor:"lighteval.metrics.metrics_sample.MRR.compute",parameters:[{name:"model_response",val:": ModelResponse"},{name:"doc",val:": Doc"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"lighteval.metrics.metrics_sample.MRR.compute.model_response",description:"<strong>model_response</strong> (ModelResponse) &#x2014; The model&#x2019;s response containing logprobs.",name:"model_response"},{anchor:"lighteval.metrics.metrics_sample.MRR.compute.doc",description:"<strong>doc</strong> (Doc) &#x2014; The document containing choices and gold indices.",name:"doc"},{anchor:"lighteval.metrics.metrics_sample.MRR.compute.*kwargs",description:"*<strong>*kwargs</strong> &#x2014; Additional keyword arguments.",name:"*kwargs"}],source:"https://github.com/huggingface/lighteval/blob/vr_953/src/lighteval/metrics/metrics_sample.py#L447",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>MRR score.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p>float</p>
`}}),yt=new $({props:{title:"ROUGE",local:"lighteval.metrics.metrics_sample.ROUGE",headingTag:"h3"}}),Ct=new _({props:{name:"class lighteval.metrics.metrics_sample.ROUGE",anchor:"lighteval.metrics.metrics_sample.ROUGE",parameters:[{name:"methods",val:": str | list[str]"},{name:"multiple_golds",val:": bool = False"},{name:"bootstrap",val:": bool = False"},{name:"normalize_gold",val:": typing.Optional[typing.Callable] = None"},{name:"normalize_pred",val:": typing.Optional[typing.Callable] = None"},{name:"aggregation_function",val:": typing.Optional[typing.Callable] = None"},{name:"tokenizer",val:": object = None"}],source:"https://github.com/huggingface/lighteval/blob/vr_953/src/lighteval/metrics/metrics_sample.py#L486"}}),Lt=new _({props:{name:"compute",anchor:"lighteval.metrics.metrics_sample.ROUGE.compute",parameters:[{name:"doc",val:": Doc"},{name:"model_response",val:": ModelResponse"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"lighteval.metrics.metrics_sample.ROUGE.compute.doc",description:"<strong>doc</strong> (Doc) &#x2014; The document containing gold references.",name:"doc"},{anchor:"lighteval.metrics.metrics_sample.ROUGE.compute.model_response",description:"<strong>model_response</strong> (ModelResponse) &#x2014; The model&#x2019;s response containing predictions.",name:"model_response"},{anchor:"lighteval.metrics.metrics_sample.ROUGE.compute.*kwargs",description:"*<strong>*kwargs</strong> &#x2014; Additional keyword arguments.",name:"*kwargs"}],source:"https://github.com/huggingface/lighteval/blob/vr_953/src/lighteval/metrics/metrics_sample.py#L533",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>Aggregated score over the current sample’s items.
If several rouge functions have been selected, returns a dict which maps name and scores.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p>float or dict</p>
`}}),Mt=new $({props:{title:"BertScore",local:"lighteval.metrics.metrics_sample.BertScore",headingTag:"h3"}}),wt=new _({props:{name:"class lighteval.metrics.metrics_sample.BertScore",anchor:"lighteval.metrics.metrics_sample.BertScore",parameters:[{name:"normalize_gold",val:": typing.Optional[typing.Callable] = None"},{name:"normalize_pred",val:": typing.Optional[typing.Callable] = None"}],source:"https://github.com/huggingface/lighteval/blob/vr_953/src/lighteval/metrics/metrics_sample.py#L598"}}),kt=new _({props:{name:"compute",anchor:"lighteval.metrics.metrics_sample.BertScore.compute",parameters:[{name:"doc",val:": Doc"},{name:"model_response",val:": ModelResponse"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"lighteval.metrics.metrics_sample.BertScore.compute.doc",description:"<strong>doc</strong> (Doc) &#x2014; The document containing gold references.",name:"doc"},{anchor:"lighteval.metrics.metrics_sample.BertScore.compute.model_response",description:"<strong>model_response</strong> (ModelResponse) &#x2014; The model&#x2019;s response containing predictions.",name:"model_response"},{anchor:"lighteval.metrics.metrics_sample.BertScore.compute.*kwargs",description:"*<strong>*kwargs</strong> &#x2014; Additional keyword arguments.",name:"*kwargs"}],source:"https://github.com/huggingface/lighteval/blob/vr_953/src/lighteval/metrics/metrics_sample.py#L628",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>Scores over the current sample’s items.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p>dict</p>
`}}),Tt=new $({props:{title:"Extractiveness",local:"lighteval.metrics.metrics_sample.Extractiveness",headingTag:"h3"}}),Dt=new _({props:{name:"class lighteval.metrics.metrics_sample.Extractiveness",anchor:"lighteval.metrics.metrics_sample.Extractiveness",parameters:[{name:"normalize_input",val:": <built-in function callable> = <function remove_braces at 0x7f2807910430>"},{name:"normalize_pred",val:": <built-in function callable> = <function remove_braces_and_strip at 0x7f28079104c0>"},{name:"input_column",val:": str = 'text'"}],source:"https://github.com/huggingface/lighteval/blob/vr_953/src/lighteval/metrics/metrics_sample.py#L661"}}),Et=new _({props:{name:"compute",anchor:"lighteval.metrics.metrics_sample.Extractiveness.compute",parameters:[{name:"doc",val:": Doc"},{name:"model_response",val:": ModelResponse"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"lighteval.metrics.metrics_sample.Extractiveness.compute.doc",description:"<strong>doc</strong> (Doc) &#x2014; The document containing input text.",name:"doc"},{anchor:"lighteval.metrics.metrics_sample.Extractiveness.compute.model_response",description:"<strong>model_response</strong> (ModelResponse) &#x2014; The model&#x2019;s response containing predictions.",name:"model_response"},{anchor:"lighteval.metrics.metrics_sample.Extractiveness.compute.*kwargs",description:"*<strong>*kwargs</strong> &#x2014; Additional keyword arguments.",name:"*kwargs"}],source:"https://github.com/huggingface/lighteval/blob/vr_953/src/lighteval/metrics/metrics_sample.py#L682",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The extractiveness scores.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p>dict[str, float]</p>
`}}),Pt=new $({props:{title:"Faithfulness",local:"lighteval.metrics.metrics_sample.Faithfulness",headingTag:"h3"}}),Rt=new _({props:{name:"class lighteval.metrics.metrics_sample.Faithfulness",anchor:"lighteval.metrics.metrics_sample.Faithfulness",parameters:[{name:"normalize_input",val:": typing.Callable = <function remove_braces at 0x7f2807910430>"},{name:"normalize_pred",val:": typing.Callable = <function remove_braces_and_strip at 0x7f28079104c0>"},{name:"input_column",val:": str = 'text'"}],source:"https://github.com/huggingface/lighteval/blob/vr_953/src/lighteval/metrics/metrics_sample.py#L714"}}),jt=new _({props:{name:"compute",anchor:"lighteval.metrics.metrics_sample.Faithfulness.compute",parameters:[{name:"doc",val:": Doc"},{name:"model_response",val:": ModelResponse"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"lighteval.metrics.metrics_sample.Faithfulness.compute.doc",description:"<strong>doc</strong> (Doc) &#x2014; The document containing input text.",name:"doc"},{anchor:"lighteval.metrics.metrics_sample.Faithfulness.compute.model_response",description:"<strong>model_response</strong> (ModelResponse) &#x2014; The model&#x2019;s response containing predictions.",name:"model_response"},{anchor:"lighteval.metrics.metrics_sample.Faithfulness.compute.*kwargs",description:"*<strong>*kwargs</strong> &#x2014; Additional keyword arguments.",name:"*kwargs"}],source:"https://github.com/huggingface/lighteval/blob/vr_953/src/lighteval/metrics/metrics_sample.py#L735",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The faithfulness scores.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p>dict[str, float]</p>
`}}),It=new $({props:{title:"BLEURT",local:"lighteval.metrics.metrics_sample.BLEURT",headingTag:"h3"}}),At=new _({props:{name:"class lighteval.metrics.metrics_sample.BLEURT",anchor:"lighteval.metrics.metrics_sample.BLEURT",parameters:[],source:"https://github.com/huggingface/lighteval/blob/vr_953/src/lighteval/metrics/metrics_sample.py#L762"}}),Nt=new _({props:{name:"compute",anchor:"lighteval.metrics.metrics_sample.BLEURT.compute",parameters:[{name:"doc",val:": Doc"},{name:"model_response",val:": ModelResponse"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"lighteval.metrics.metrics_sample.BLEURT.compute.doc",description:"<strong>doc</strong> (Doc) &#x2014; The document containing gold references.",name:"doc"},{anchor:"lighteval.metrics.metrics_sample.BLEURT.compute.model_response",description:"<strong>model_response</strong> (ModelResponse) &#x2014; The model&#x2019;s response containing predictions.",name:"model_response"},{anchor:"lighteval.metrics.metrics_sample.BLEURT.compute.*kwargs",description:"*<strong>*kwargs</strong> &#x2014; Additional keyword arguments.",name:"*kwargs"}],source:"https://github.com/huggingface/lighteval/blob/vr_953/src/lighteval/metrics/metrics_sample.py#L783",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>Score over the current sample’s items.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p>float</p>
`}}),St=new $({props:{title:"BLEU",local:"lighteval.metrics.metrics_sample.BLEU",headingTag:"h3"}}),Vt=new _({props:{name:"class lighteval.metrics.metrics_sample.BLEU",anchor:"lighteval.metrics.metrics_sample.BLEU",parameters:[{name:"n_gram",val:": int"}],source:"https://github.com/huggingface/lighteval/blob/vr_953/src/lighteval/metrics/metrics_sample.py#L802"}}),Jt=new _({props:{name:"compute",anchor:"lighteval.metrics.metrics_sample.BLEU.compute",parameters:[{name:"doc",val:": Doc"},{name:"model_response",val:": ModelResponse"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"lighteval.metrics.metrics_sample.BLEU.compute.doc",description:"<strong>doc</strong> (Doc) &#x2014; The document containing gold references.",name:"doc"},{anchor:"lighteval.metrics.metrics_sample.BLEU.compute.model_response",description:"<strong>model_response</strong> (ModelResponse) &#x2014; The model&#x2019;s response containing predictions.",name:"model_response"},{anchor:"lighteval.metrics.metrics_sample.BLEU.compute.*kwargs",description:"*<strong>*kwargs</strong> &#x2014; Additional keyword arguments.",name:"*kwargs"}],source:"https://github.com/huggingface/lighteval/blob/vr_953/src/lighteval/metrics/metrics_sample.py#L812",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>Score over the current sample’s items.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p>float</p>
`}}),zt=new $({props:{title:"StringDistance",local:"lighteval.metrics.metrics_sample.StringDistance",headingTag:"h3"}}),Bt=new _({props:{name:"class lighteval.metrics.metrics_sample.StringDistance",anchor:"lighteval.metrics.metrics_sample.StringDistance",parameters:[{name:"metric_types",val:": list[str] | str"},{name:"strip_prediction",val:": bool = True"}],source:"https://github.com/huggingface/lighteval/blob/vr_953/src/lighteval/metrics/metrics_sample.py#L841"}}),Ft=new _({props:{name:"compute",anchor:"lighteval.metrics.metrics_sample.StringDistance.compute",parameters:[{name:"doc",val:": Doc"},{name:"model_response",val:": ModelResponse"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"lighteval.metrics.metrics_sample.StringDistance.compute.doc",description:"<strong>doc</strong> (Doc) &#x2014; The document containing gold references.",name:"doc"},{anchor:"lighteval.metrics.metrics_sample.StringDistance.compute.model_response",description:"<strong>model_response</strong> (ModelResponse) &#x2014; The model&#x2019;s response containing predictions.",name:"model_response"},{anchor:"lighteval.metrics.metrics_sample.StringDistance.compute.*kwargs",description:"*<strong>*kwargs</strong> &#x2014; Additional keyword arguments.",name:"*kwargs"}],source:"https://github.com/huggingface/lighteval/blob/vr_953/src/lighteval/metrics/metrics_sample.py#L863",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The different scores computed</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p>dict</p>
`}}),Ot=new _({props:{name:"edit_similarity",anchor:"lighteval.metrics.metrics_sample.StringDistance.edit_similarity",parameters:[{name:"s1",val:""},{name:"s2",val:""}],source:"https://github.com/huggingface/lighteval/blob/vr_953/src/lighteval/metrics/metrics_sample.py#L921",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>Edit similarity score between 0 and 1</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p>float</p>
`}}),Ut=new _({props:{name:"longest_common_prefix_length",anchor:"lighteval.metrics.metrics_sample.StringDistance.longest_common_prefix_length",parameters:[{name:"s1",val:": ndarray"},{name:"s2",val:": ndarray"}],source:"https://github.com/huggingface/lighteval/blob/vr_953/src/lighteval/metrics/metrics_sample.py#L914"}}),Gt=new $({props:{title:"JudgeLLM",local:"lighteval.metrics.metrics_sample.JudgeLLM",headingTag:"h3"}}),Ht=new _({props:{name:"class lighteval.metrics.metrics_sample.JudgeLLM",anchor:"lighteval.metrics.metrics_sample.JudgeLLM",parameters:[{name:"judge_model_name",val:": str"},{name:"template",val:": typing.Callable"},{name:"process_judge_response",val:": typing.Callable"},{name:"judge_backend",val:": typing.Literal['litellm', 'openai', 'transformers', 'vllm', 'tgi', 'inference-providers']"},{name:"short_judge_name",val:": str | None = None"},{name:"response_format",val:": pydantic.main.BaseModel | None = None"},{name:"url",val:": str | None = None"},{name:"hf_provider",val:": str | None = None"},{name:"max_tokens",val:": int | None = None"}],source:"https://github.com/huggingface/lighteval/blob/vr_953/src/lighteval/metrics/metrics_sample.py#L936"}}),Kt=new $({props:{title:"JudgeLLMMTBench",local:"lighteval.metrics.metrics_sample.JudgeLLMMTBench",headingTag:"h3"}}),Yt=new _({props:{name:"class lighteval.metrics.metrics_sample.JudgeLLMMTBench",anchor:"lighteval.metrics.metrics_sample.JudgeLLMMTBench",parameters:[{name:"judge_model_name",val:": str"},{name:"template",val:": typing.Callable"},{name:"process_judge_response",val:": typing.Callable"},{name:"judge_backend",val:": typing.Literal['litellm', 'openai', 'transformers', 'vllm', 'tgi', 'inference-providers']"},{name:"short_judge_name",val:": str | None = None"},{name:"response_format",val:": pydantic.main.BaseModel | None = None"},{name:"url",val:": str | None = None"},{name:"hf_provider",val:": str | None = None"},{name:"max_tokens",val:": int | None = None"}],source:"https://github.com/huggingface/lighteval/blob/vr_953/src/lighteval/metrics/metrics_sample.py#L1038"}}),Xt=new _({props:{name:"compute",anchor:"lighteval.metrics.metrics_sample.JudgeLLMMTBench.compute",parameters:[{name:"model_response",val:": list"},{name:"docs",val:": list"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/lighteval/blob/vr_953/src/lighteval/metrics/metrics_sample.py#L1039"}}),Zt=new $({props:{title:"JudgeLLMMixEval",local:"lighteval.metrics.metrics_sample.JudgeLLMMixEval",headingTag:"h3"}}),Qt=new _({props:{name:"class lighteval.metrics.metrics_sample.JudgeLLMMixEval",anchor:"lighteval.metrics.metrics_sample.JudgeLLMMixEval",parameters:[{name:"judge_model_name",val:": str"},{name:"template",val:": typing.Callable"},{name:"process_judge_response",val:": typing.Callable"},{name:"judge_backend",val:": typing.Literal['litellm', 'openai', 'transformers', 'vllm', 'tgi', 'inference-providers']"},{name:"short_judge_name",val:": str | None = None"},{name:"response_format",val:": pydantic.main.BaseModel | None = None"},{name:"url",val:": str | None = None"},{name:"hf_provider",val:": str | None = None"},{name:"max_tokens",val:": int | None = None"}],source:"https://github.com/huggingface/lighteval/blob/vr_953/src/lighteval/metrics/metrics_sample.py#L1070"}}),Wt=new _({props:{name:"compute",anchor:"lighteval.metrics.metrics_sample.JudgeLLMMixEval.compute",parameters:[{name:"model_responses",val:": list"},{name:"docs",val:": list"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/lighteval/blob/vr_953/src/lighteval/metrics/metrics_sample.py#L1071"}}),er=new $({props:{title:"MajAtK",local:"lighteval.metrics.metrics_sample.MajAtK",headingTag:"h3"}}),tr=new _({props:{name:"class lighteval.metrics.metrics_sample.MajAtK",anchor:"lighteval.metrics.metrics_sample.MajAtK",parameters:[{name:"k",val:": int | None = None"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/lighteval/blob/vr_953/src/lighteval/metrics/metrics_sample.py#L1206"}}),rr=new _({props:{name:"compute",anchor:"lighteval.metrics.metrics_sample.MajAtK.compute",parameters:[{name:"model_response",val:": ModelResponse"},{name:"docs",val:": Doc"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"lighteval.metrics.metrics_sample.MajAtK.compute.model_response",description:"<strong>model_response</strong> (ModelResponse) &#x2014; The model&#x2019;s response containing predictions.",name:"model_response"},{anchor:"lighteval.metrics.metrics_sample.MajAtK.compute.docs",description:"<strong>docs</strong> (Doc) &#x2014; The document containing gold references.",name:"docs"},{anchor:"lighteval.metrics.metrics_sample.MajAtK.compute.*kwargs",description:"*<strong>*kwargs</strong> &#x2014; Additional keyword arguments.",name:"*kwargs"}],source:"https://github.com/huggingface/lighteval/blob/vr_953/src/lighteval/metrics/metrics_sample.py#L1219",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>Aggregated score over the current sample’s items.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p>float</p>
`}}),sr=new $({props:{title:"LLM-as-a-Judge",local:"llm-as-a-judge",headingTag:"h2"}}),lr=new $({props:{title:"JudgeLM",local:"lighteval.metrics.utils.llm_as_judge.JudgeLM",headingTag:"h3"}}),nr=new _({props:{name:"class lighteval.metrics.utils.llm_as_judge.JudgeLM",anchor:"lighteval.metrics.utils.llm_as_judge.JudgeLM",parameters:[{name:"model",val:": str"},{name:"templates",val:": typing.Callable"},{name:"process_judge_response",val:": typing.Callable"},{name:"judge_backend",val:": typing.Literal['litellm', 'openai', 'transformers', 'tgi', 'vllm', 'inference-providers']"},{name:"url",val:": str | None = None"},{name:"api_key",val:": str | None = None"},{name:"max_tokens",val:": int = 512"},{name:"response_format",val:": BaseModel = None"},{name:"hf_provider",val:": typing.Optional[typing.Literal['black-forest-labs', 'cerebras', 'cohere', 'fal-ai', 'fireworks-ai', 'inference-providers', 'hyperbolic', 'nebius', 'novita', 'openai', 'replicate', 'sambanova', 'together']] = None"}],parametersDescription:[{anchor:"lighteval.metrics.utils.llm_as_judge.JudgeLM.model",description:"<strong>model</strong> (str) &#x2014; The name of the model.",name:"model"},{anchor:"lighteval.metrics.utils.llm_as_judge.JudgeLM.templates",description:"<strong>templates</strong> (Callable) &#x2014; A function taking into account the question, options, answer, and gold and returning the judge prompt.",name:"templates"},{anchor:"lighteval.metrics.utils.llm_as_judge.JudgeLM.process_judge_response",description:"<strong>process_judge_response</strong> (Callable) &#x2014; A function for processing the judge&#x2019;s response.",name:"process_judge_response"},{anchor:"lighteval.metrics.utils.llm_as_judge.JudgeLM.judge_backend",description:"<strong>judge_backend</strong> (Literal[&#x201C;openai&#x201D;, &#x201C;transformers&#x201D;, &#x201C;tgi&#x201D;, &#x201C;vllm&#x201D;]) &#x2014; The backend for the judge.",name:"judge_backend"},{anchor:"lighteval.metrics.utils.llm_as_judge.JudgeLM.url",description:"<strong>url</strong> (str | None) &#x2014; The URL for the OpenAI API.",name:"url"},{anchor:"lighteval.metrics.utils.llm_as_judge.JudgeLM.api_key",description:"<strong>api_key</strong> (str | None) &#x2014; The API key for the OpenAI API (either OpenAI or HF key).",name:"api_key"},{anchor:"lighteval.metrics.utils.llm_as_judge.JudgeLM.model",description:"<strong>model</strong> (str) &#x2014; The name of the model.",name:"model"},{anchor:"lighteval.metrics.utils.llm_as_judge.JudgeLM.template",description:"<strong>template</strong> (Callable) &#x2014; A function taking into account the question, options, answer, and gold and returning the judge prompt.",name:"template"},{anchor:"lighteval.metrics.utils.llm_as_judge.JudgeLM.API_MAX_RETRY",description:"<strong>API_MAX_RETRY</strong> (int) &#x2014; The maximum number of retries for the API.",name:"API_MAX_RETRY"},{anchor:"lighteval.metrics.utils.llm_as_judge.JudgeLM.API_RETRY_SLEEP",description:"<strong>API_RETRY_SLEEP</strong> (int) &#x2014; The time to sleep between retries.",name:"API_RETRY_SLEEP"},{anchor:"lighteval.metrics.utils.llm_as_judge.JudgeLM.client",description:"<strong>client</strong> (OpenAI | None) &#x2014; The OpenAI client.",name:"client"},{anchor:"lighteval.metrics.utils.llm_as_judge.JudgeLM.pipe",description:"<strong>pipe</strong> (LLM | AutoModel | None) &#x2014; The Transformers or vllm pipeline.",name:"pipe"},{anchor:"lighteval.metrics.utils.llm_as_judge.JudgeLM.process_judge_response",description:"<strong>process_judge_response</strong> (Callable) &#x2014; A function for processing the judge&#x2019;s response.",name:"process_judge_response"},{anchor:"lighteval.metrics.utils.llm_as_judge.JudgeLM.url",description:"<strong>url</strong> (str | None) &#x2014; The URL for the OpenAI API.",name:"url"},{anchor:"lighteval.metrics.utils.llm_as_judge.JudgeLM.api_key",description:"<strong>api_key</strong> (str | None) &#x2014; The API key for the OpenAI API (either OpenAI or HF key).",name:"api_key"},{anchor:"lighteval.metrics.utils.llm_as_judge.JudgeLM.backend",description:"<strong>backend</strong> (Literal[&#x201C;openai&#x201D;, &#x201C;transformers&#x201D;, &#x201C;tgi&#x201D;, &#x201C;vllm&#x201D;]) &#x2014; The backend for the judge",name:"backend"}],source:"https://github.com/huggingface/lighteval/blob/vr_953/src/lighteval/metrics/utils/llm_as_judge.py#L48"}}),ar=new _({props:{name:"dict_of_lists_to_list_of_dicts",anchor:"lighteval.metrics.utils.llm_as_judge.JudgeLM.dict_of_lists_to_list_of_dicts",parameters:[{name:"dict_of_lists",val:""}],parametersDescription:[{anchor:"lighteval.metrics.utils.llm_as_judge.JudgeLM.dict_of_lists_to_list_of_dicts.dict_of_lists",description:`<strong>dict_of_lists</strong> &#x2014; A dictionary where each value is a list.
All lists are expected to have the same length.`,name:"dict_of_lists"}],source:"https://github.com/huggingface/lighteval/blob/vr_953/src/lighteval/metrics/utils/llm_as_judge.py#L187",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A list of dictionaries.</p>
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<p>A tuple containing the score, prompts, and judgment.</p>
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