Buckets:
| import{s as an,n as nn,o as on}from"../chunks/scheduler.7da89386.js";import{S as mn,i as cn,g as a,s as i,r as m,A as pn,h as n,f as t,c as l,j as h,u as c,x as _,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 f}from"../chunks/Docstring.47f425ac.js";import{H as $,E as gn}from"../chunks/getInferenceSnippets.d539cff9.js";function dn(va){let D,es,Qr,ts,Le,rs,Ce,ss,Me,is,we,Ee,ls,Te,as,P,ke,il,nr,fa="Metric computed over the whole corpora, with computations happening at the aggregation phase",ns,De,os,N,Pe,ll,or,_a="Metric computed per sample, then aggregated over the corpus",ms,Ne,cs,I,Ie,al,mr,$a=`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.`,ps,je,gs,j,Ae,nl,cr,ba="MetricGrouping computed over the whole corpora, with computations happening at the aggregation phase",ds,Re,us,A,ze,ol,pr,xa="MetricGrouping are computed per sample, then aggregated over the corpus",hs,Se,vs,Ve,fs,R,Je,ml,ee,Ue,cl,gr,ya="Computes the metric score over all the corpus generated items, by using the scikit learn implementation.",_s,Be,$s,z,Fe,pl,te,Oe,gl,dr,La="Computes the metric score over all the corpus generated items.",bs,Ge,xs,S,qe,dl,re,He,ul,ur,Ca="Computes the metric score over all the corpus generated items, by using the sacrebleu implementation.",ys,Ke,Ls,V,Ye,hl,hr,Ma='Computes the Matthews Correlation Coefficient, using scikit learn (<a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html" rel="nofollow">doc</a>).',Cs,Xe,Ms,Ze,ws,L,Qe,vl,se,We,fl,vr,wa="Computes the metric over a list of golds and predictions for one single sample.",_l,ie,et,$l,fr,Ea="Compares two strings only.",Es,tt,Ts,C,rt,bl,le,st,xl,_r,Ta="Computes the metric over a list of golds and predictions for one single sample.",yl,ae,it,Ll,$r,ka="Compares two strings only.",ks,lt,Ds,J,at,Cl,ne,nt,Ml,br,Da=`Computes the log likelihood accuracy: is the choice with the highest logprob in <code>choices_logprob</code> present | |
| in the <code>gold_ixs</code>?`,Ps,ot,Ns,U,mt,wl,oe,ct,El,xr,Pa="Computes the log likelihood probability: chance of choosing the best choice.",Is,pt,js,B,gt,Tl,me,dt,kl,yr,Na="Computes the log likelihood probability: chance of choosing the best choice.",As,ut,Rs,F,ht,Dl,ce,vt,Pl,Lr,Ia=`Computes the recall at the requested depth level: looks at the <code>n</code> best predicted choices (with the | |
| highest log probabilities) and see if there is an actual gold among them.`,zs,ft,Ss,O,_t,Nl,pe,$t,Il,Cr,ja="Mean reciprocal rank. Measures the quality of a ranking of choices (ordered by correctness).",Vs,bt,Js,G,xt,jl,ge,yt,Al,Mr,Aa="Computes the metric(s) over a list of golds and predictions for one single sample.",Us,Lt,Bs,q,Ct,Rl,de,Mt,zl,wr,Ra="Computes the prediction, recall and f1 score using the bert scorer.",Fs,wt,Os,H,Et,Sl,M,Tt,Vl,Er,za="Compute the extractiveness of the predictions.",Jl,Tr,Sa=`This method calculates coverage, density, and compression scores for a single | |
| prediction against the input text.`,Gs,kt,qs,K,Dt,Ul,w,Pt,Bl,kr,Va="Compute the faithfulness of the predictions.",Fl,Dr,Ja="The SummaCZS (Summary Content Zero-Shot) model is used with configurable granularity and model variation.",Hs,Nt,Ks,Y,It,Ol,ue,jt,Gl,Pr,Ua="Uses the stored BLEURT scorer to compute the score on the current sample.",Ys,At,Xs,X,Rt,ql,he,zt,Hl,Nr,Ba="Computes the sentence level BLEU between the golds and each prediction, then takes the average.",Zs,St,Qs,y,Vt,Kl,ve,Jt,Yl,Ir,Fa="Computes all the requested metrics on the golds and prediction.",Xl,E,Ut,Zl,jr,Oa="Compute the edit similarity between two lists of strings.",Ql,Ar,Ga=`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).`,Wl,fe,Bt,ea,Rr,qa="Compute the length of the longest common prefix.",Ws,Ft,ei,Ot,Gt,ti,qt,ri,Z,Ht,ta,_e,Kt,ra,zr,Ha=`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.`,si,Yt,ii,Q,Xt,sa,$e,Zt,ia,Sr,Ka=`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.`,li,Qt,ai,W,Wt,la,be,er,aa,Vr,Ya=`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.`,ni,tr,oi,rr,mi,b,sr,na,Jr,Xa="A class representing a judge for evaluating answers using either the OpenAI or Transformers library.",oa,Ur,Za=`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.`,ma,x,ir,ca,Br,Qa="Transform a dictionary of lists into a list of dictionaries.",pa,Fr,Wa=`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.`,ga,Or,en="Example:",da,Gr,tn=`<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>`,ua,xe,lr,ha,qr,rn="Evaluates an answer using either Transformers or OpenAI API.",ci,ar,pi,Wr,gi;return Le=new $({props:{title:"Metrics",local:"metrics",headingTag:"h1"}}),Ce=new $({props:{title:"Metrics",local:"metrics",headingTag:"h2"}}),Me=new $({props:{title:"Metric",local:"lighteval.metrics.Metric",headingTag:"h3"}}),Ee=new f({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:": MetricCategory"},{name:"use_case",val:": MetricUseCase"},{name:"sample_level_fn",val:": <built-in function callable>"},{name:"corpus_level_fn",val:": <built-in function callable>"}],source:"https://github.com/huggingface/lighteval/blob/vr_722/src/lighteval/metrics/utils/metric_utils.py#L56"}}),Te=new $({props:{title:"CorpusLevelMetric",local:"lighteval.metrics.utils.metric_utils.CorpusLevelMetric",headingTag:"h3"}}),ke=new f({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:": MetricCategory"},{name:"use_case",val:": MetricUseCase"},{name:"sample_level_fn",val:": <built-in function callable>"},{name:"corpus_level_fn",val:": <built-in function callable>"}],source:"https://github.com/huggingface/lighteval/blob/vr_722/src/lighteval/metrics/utils/metric_utils.py#L89"}}),De=new $({props:{title:"SampleLevelMetric",local:"lighteval.metrics.utils.metric_utils.SampleLevelMetric",headingTag:"h3"}}),Pe=new f({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:": MetricCategory"},{name:"use_case",val:": MetricUseCase"},{name:"sample_level_fn",val:": <built-in function callable>"},{name:"corpus_level_fn",val:": <built-in function callable>"}],source:"https://github.com/huggingface/lighteval/blob/vr_722/src/lighteval/metrics/utils/metric_utils.py#L96"}}),Ne=new $({props:{title:"MetricGrouping",local:"lighteval.metrics.utils.metric_utils.MetricGrouping",headingTag:"h3"}}),Ie=new f({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:": MetricCategory"},{name:"use_case",val:": MetricUseCase"},{name:"sample_level_fn",val:": <built-in function callable>"},{name:"corpus_level_fn",val:": dict"}],source:"https://github.com/huggingface/lighteval/blob/vr_722/src/lighteval/metrics/utils/metric_utils.py#L78"}}),je=new $({props:{title:"CorpusLevelMetricGrouping",local:"lighteval.metrics.utils.metric_utils.CorpusLevelMetricGrouping",headingTag:"h3"}}),Ae=new f({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:": MetricCategory"},{name:"use_case",val:": MetricUseCase"},{name:"sample_level_fn",val:": <built-in function callable>"},{name:"corpus_level_fn",val:": dict"}],source:"https://github.com/huggingface/lighteval/blob/vr_722/src/lighteval/metrics/utils/metric_utils.py#L103"}}),Re=new $({props:{title:"SampleLevelMetricGrouping",local:"lighteval.metrics.utils.metric_utils.SampleLevelMetricGrouping",headingTag:"h3"}}),ze=new f({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:": MetricCategory"},{name:"use_case",val:": MetricUseCase"},{name:"sample_level_fn",val:": <built-in function callable>"},{name:"corpus_level_fn",val:": dict"}],source:"https://github.com/huggingface/lighteval/blob/vr_722/src/lighteval/metrics/utils/metric_utils.py#L110"}}),Se=new $({props:{title:"Corpus Metrics",local:"corpus-metrics",headingTag:"h2"}}),Ve=new $({props:{title:"CorpusLevelF1Score",local:"lighteval.metrics.metrics_corpus.CorpusLevelF1Score",headingTag:"h3"}}),Je=new f({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_722/src/lighteval/metrics/metrics_corpus.py#L62"}}),Ue=new f({props:{name:"compute",anchor:"lighteval.metrics.metrics_corpus.CorpusLevelF1Score.compute",parameters:[{name:"items",val:": list"}],source:"https://github.com/huggingface/lighteval/blob/vr_722/src/lighteval/metrics/metrics_corpus.py#L77"}}),Be=new $({props:{title:"CorpusLevelPerplexityMetric",local:"lighteval.metrics.metrics_corpus.CorpusLevelPerplexityMetric",headingTag:"h3"}}),Fe=new f({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_722/src/lighteval/metrics/metrics_corpus.py#L130"}}),Oe=new f({props:{name:"compute",anchor:"lighteval.metrics.metrics_corpus.CorpusLevelPerplexityMetric.compute",parameters:[{name:"items",val:": list"}],source:"https://github.com/huggingface/lighteval/blob/vr_722/src/lighteval/metrics/metrics_corpus.py#L148"}}),Ge=new $({props:{title:"CorpusLevelTranslationMetric",local:"lighteval.metrics.metrics_corpus.CorpusLevelTranslationMetric",headingTag:"h3"}}),qe=new f({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_722/src/lighteval/metrics/metrics_corpus.py#L93"}}),He=new f({props:{name:"compute",anchor:"lighteval.metrics.metrics_corpus.CorpusLevelTranslationMetric.compute",parameters:[{name:"items",val:": list"}],source:"https://github.com/huggingface/lighteval/blob/vr_722/src/lighteval/metrics/metrics_corpus.py#L115"}}),Ke=new $({props:{title:"matthews_corrcoef",local:"lighteval.metrics.metrics_corpus.matthews_corrcoef",headingTag:"h3"}}),Ye=new f({props:{name:"lighteval.metrics.metrics_corpus.matthews_corrcoef",anchor:"lighteval.metrics.metrics_corpus.matthews_corrcoef",parameters:[{name:"items",val:": list"}],parametersDescription:[{anchor:"lighteval.metrics.metrics_corpus.matthews_corrcoef.items",description:"<strong>items</strong> (list[dict]) — List of GenerativeCorpusMetricInput",name:"items"}],source:"https://github.com/huggingface/lighteval/blob/vr_722/src/lighteval/metrics/metrics_corpus.py#L48",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script> | |
| <p>Score</p> | |
| `,returnType:`<script context="module">export const metadata = 'undefined';<\/script> | |
| <p>float</p> | |
| `}}),Xe=new $({props:{title:"Sample Metrics",local:"sample-metrics",headingTag:"h2"}}),Ze=new $({props:{title:"ExactMatches",local:"lighteval.metrics.metrics_sample.ExactMatches",headingTag:"h3"}}),Qe=new f({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_722/src/lighteval/metrics/metrics_sample.py#L61"}}),We=new f({props:{name:"compute",anchor:"lighteval.metrics.metrics_sample.ExactMatches.compute",parameters:[{name:"golds",val:": list"},{name:"predictions",val:": list"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"lighteval.metrics.metrics_sample.ExactMatches.compute.golds",description:"<strong>golds</strong> (list[str]) — Reference targets",name:"golds"},{anchor:"lighteval.metrics.metrics_sample.ExactMatches.compute.predictions",description:"<strong>predictions</strong> (list[str]) — Predicted strings",name:"predictions"}],source:"https://github.com/huggingface/lighteval/blob/vr_722/src/lighteval/metrics/metrics_sample.py#L98",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> | |
| `}}),et=new f({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) — One of the possible references",name:"gold"},{anchor:"lighteval.metrics.metrics_sample.ExactMatches.compute_one_item.pred",description:"<strong>pred</strong> (str) — One of the possible predictions",name:"pred"}],source:"https://github.com/huggingface/lighteval/blob/vr_722/src/lighteval/metrics/metrics_sample.py#L115",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> | |
| `}}),tt=new $({props:{title:"F1_score",local:"lighteval.metrics.metrics_sample.F1_score",headingTag:"h3"}}),rt=new f({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_722/src/lighteval/metrics/metrics_sample.py#L148"}}),st=new f({props:{name:"compute",anchor:"lighteval.metrics.metrics_sample.F1_score.compute",parameters:[{name:"golds",val:": list"},{name:"predictions",val:": list"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"lighteval.metrics.metrics_sample.F1_score.compute.golds",description:"<strong>golds</strong> (list[str]) — Reference targets",name:"golds"},{anchor:"lighteval.metrics.metrics_sample.F1_score.compute.predictions",description:"<strong>predictions</strong> (list[str]) — Predicted strings",name:"predictions"}],source:"https://github.com/huggingface/lighteval/blob/vr_722/src/lighteval/metrics/metrics_sample.py#L175",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> | |
| `}}),it=new f({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) — One of the possible references",name:"gold"},{anchor:"lighteval.metrics.metrics_sample.F1_score.compute_one_item.pred",description:"<strong>pred</strong> (str) — One of the possible predictions",name:"pred"}],source:"https://github.com/huggingface/lighteval/blob/vr_722/src/lighteval/metrics/metrics_sample.py#L192",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> | |
| `}}),lt=new $({props:{title:"LoglikelihoodAcc",local:"lighteval.metrics.metrics_sample.LoglikelihoodAcc",headingTag:"h3"}}),at=new f({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_722/src/lighteval/metrics/metrics_sample.py#L218"}}),nt=new f({props:{name:"compute",anchor:"lighteval.metrics.metrics_sample.LoglikelihoodAcc.compute",parameters:[{name:"gold_ixs",val:": list"},{name:"choices_logprob",val:": list"},{name:"unconditioned_logprob",val:": list[float] | None"},{name:"choices_tokens",val:": list[list[int]] | None"},{name:"formatted_doc",val:": Doc"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"lighteval.metrics.metrics_sample.LoglikelihoodAcc.compute.gold_ixs",description:"<strong>gold_ixs</strong> (list[int]) — All the gold choices indices",name:"gold_ixs"},{anchor:"lighteval.metrics.metrics_sample.LoglikelihoodAcc.compute.choices_logprob",description:"<strong>choices_logprob</strong> (list[float]) — Summed log-probabilities of all the possible choices for the model, ordered as the choices.",name:"choices_logprob"},{anchor:"lighteval.metrics.metrics_sample.LoglikelihoodAcc.compute.unconditioned_logprob",description:"<strong>unconditioned_logprob</strong> (list[float] | None) — Unconditioned log-probabilities for PMI normalization, ordered as the choices.",name:"unconditioned_logprob"},{anchor:"lighteval.metrics.metrics_sample.LoglikelihoodAcc.compute.choices_tokens",description:"<strong>choices_tokens</strong> (list[list[int]] | None) — Tokenized choices for token normalization, ordered as the choices.",name:"choices_tokens"},{anchor:"lighteval.metrics.metrics_sample.LoglikelihoodAcc.compute.formatted_doc",description:`<strong>formatted_doc</strong> (Doc) — Original document for the sample. | |
| Used to get the original choices’ length for possible normalization`,name:"formatted_doc"}],source:"https://github.com/huggingface/lighteval/blob/vr_722/src/lighteval/metrics/metrics_sample.py#L229",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> | |
| `}}),ot=new $({props:{title:"NormalizedMultiChoiceProbability",local:"lighteval.metrics.metrics_sample.NormalizedMultiChoiceProbability",headingTag:"h3"}}),mt=new f({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 0x7ffb0113a970>"}],source:"https://github.com/huggingface/lighteval/blob/vr_722/src/lighteval/metrics/metrics_sample.py#L269"}}),ct=new f({props:{name:"compute",anchor:"lighteval.metrics.metrics_sample.NormalizedMultiChoiceProbability.compute",parameters:[{name:"gold_ixs",val:": list"},{name:"choices_logprob",val:": list"},{name:"unconditioned_logprob",val:": list[float] | None"},{name:"choices_tokens",val:": list[list[int]] | None"},{name:"formatted_doc",val:": Doc"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"lighteval.metrics.metrics_sample.NormalizedMultiChoiceProbability.compute.gold_ixs",description:"<strong>gold_ixs</strong> (list[int]) — All the gold choices indices",name:"gold_ixs"},{anchor:"lighteval.metrics.metrics_sample.NormalizedMultiChoiceProbability.compute.choices_logprob",description:"<strong>choices_logprob</strong> (list[float]) — Summed log-probabilities of all the possible choices for the model, ordered as the choices.",name:"choices_logprob"},{anchor:"lighteval.metrics.metrics_sample.NormalizedMultiChoiceProbability.compute.unconditioned_logprob",description:"<strong>unconditioned_logprob</strong> (list[float] | None) — Unconditioned log-probabilities for PMI normalization, ordered as the choices.",name:"unconditioned_logprob"},{anchor:"lighteval.metrics.metrics_sample.NormalizedMultiChoiceProbability.compute.choices_tokens",description:"<strong>choices_tokens</strong> (list[list[int]] | None) — Tokenized choices for token normalization, ordered as the choices.",name:"choices_tokens"},{anchor:"lighteval.metrics.metrics_sample.NormalizedMultiChoiceProbability.compute.formatted_doc",description:`<strong>formatted_doc</strong> (Doc) — Original document for the sample. | |
| Used to get the original choices’ length for possible normalization`,name:"formatted_doc"}],source:"https://github.com/huggingface/lighteval/blob/vr_722/src/lighteval/metrics/metrics_sample.py#L285",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> | |
| `}}),pt=new $({props:{title:"Probability",local:"lighteval.metrics.metrics_sample.Probability",headingTag:"h3"}}),gt=new f({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 0x7ffb0113a970>"}],source:"https://github.com/huggingface/lighteval/blob/vr_722/src/lighteval/metrics/metrics_sample.py#L326"}}),dt=new f({props:{name:"compute",anchor:"lighteval.metrics.metrics_sample.Probability.compute",parameters:[{name:"logprobs",val:": list"},{name:"target_tokens",val:": list[list[int]] | None = None"},{name:"reference_texts",val:": list[str] | None = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"lighteval.metrics.metrics_sample.Probability.compute.gold_ixs",description:"<strong>gold_ixs</strong> (list[int]) — All the gold choices indices",name:"gold_ixs"},{anchor:"lighteval.metrics.metrics_sample.Probability.compute.choices_logprob",description:"<strong>choices_logprob</strong> (list[float]) — Summed log-probabilities of all the possible choices for the model, ordered as the choices.",name:"choices_logprob"},{anchor:"lighteval.metrics.metrics_sample.Probability.compute.unconditioned_logprob",description:"<strong>unconditioned_logprob</strong> (list[float] | None) — Unconditioned log-probabilities for PMI normalization, ordered as the choices.",name:"unconditioned_logprob"},{anchor:"lighteval.metrics.metrics_sample.Probability.compute.choices_tokens",description:"<strong>choices_tokens</strong> (list[list[int]] | None) — Tokenized choices for token normalization, ordered as the choices.",name:"choices_tokens"},{anchor:"lighteval.metrics.metrics_sample.Probability.compute.reference_texts",description:"<strong>reference_texts</strong> (list[str] | None) — Reference texts for token normalization, ordered as the choices.",name:"reference_texts"}],source:"https://github.com/huggingface/lighteval/blob/vr_722/src/lighteval/metrics/metrics_sample.py#L342",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> | |
| `}}),ut=new $({props:{title:"Recall",local:"lighteval.metrics.metrics_sample.Recall",headingTag:"h3"}}),ht=new f({props:{name:"class lighteval.metrics.metrics_sample.Recall",anchor:"lighteval.metrics.metrics_sample.Recall",parameters:[{name:"at",val:": int"}],source:"https://github.com/huggingface/lighteval/blob/vr_722/src/lighteval/metrics/metrics_sample.py#L377"}}),vt=new f({props:{name:"compute",anchor:"lighteval.metrics.metrics_sample.Recall.compute",parameters:[{name:"choices_logprob",val:": list"},{name:"gold_ixs",val:": list"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"lighteval.metrics.metrics_sample.Recall.compute.gold_ixs",description:"<strong>gold_ixs</strong> (list[int]) — All the gold choices indices",name:"gold_ixs"},{anchor:"lighteval.metrics.metrics_sample.Recall.compute.choices_logprob",description:"<strong>choices_logprob</strong> (list[float]) — Summed log-probabilities of all the possible choices for the model, ordered as the choices.",name:"choices_logprob"}],source:"https://github.com/huggingface/lighteval/blob/vr_722/src/lighteval/metrics/metrics_sample.py#L387",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> | |
| `}}),ft=new $({props:{title:"MRR",local:"lighteval.metrics.metrics_sample.MRR",headingTag:"h3"}}),_t=new f({props:{name:"class lighteval.metrics.metrics_sample.MRR",anchor:"lighteval.metrics.metrics_sample.MRR",parameters:[{name:"length_normalization",val:": bool = False"}],source:"https://github.com/huggingface/lighteval/blob/vr_722/src/lighteval/metrics/metrics_sample.py#L403"}}),$t=new f({props:{name:"compute",anchor:"lighteval.metrics.metrics_sample.MRR.compute",parameters:[{name:"choices_logprob",val:": list"},{name:"gold_ixs",val:": list"},{name:"formatted_doc",val:": Doc"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"lighteval.metrics.metrics_sample.MRR.compute.gold_ixs",description:"<strong>gold_ixs</strong> (list[int]) — All the gold choices indices",name:"gold_ixs"},{anchor:"lighteval.metrics.metrics_sample.MRR.compute.choices_logprob",description:"<strong>choices_logprob</strong> (list[float]) — Summed log-probabilities of all the possible choices for the model, ordered as the choices.",name:"choices_logprob"},{anchor:"lighteval.metrics.metrics_sample.MRR.compute.formatted_doc",description:`<strong>formatted_doc</strong> (Doc) — Original document for the sample. | |
| Used to get the original choices’ length for possible normalization`,name:"formatted_doc"}],source:"https://github.com/huggingface/lighteval/blob/vr_722/src/lighteval/metrics/metrics_sample.py#L412",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> | |
| `}}),bt=new $({props:{title:"ROUGE",local:"lighteval.metrics.metrics_sample.ROUGE",headingTag:"h3"}}),xt=new f({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:": <built-in function callable> = None"},{name:"normalize_pred",val:": <built-in function callable> = None"},{name:"aggregation_function",val:": <built-in function callable> = None"},{name:"tokenizer",val:": object = None"}],source:"https://github.com/huggingface/lighteval/blob/vr_722/src/lighteval/metrics/metrics_sample.py#L442"}}),yt=new f({props:{name:"compute",anchor:"lighteval.metrics.metrics_sample.ROUGE.compute",parameters:[{name:"golds",val:": list"},{name:"predictions",val:": list"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"lighteval.metrics.metrics_sample.ROUGE.compute.golds",description:"<strong>golds</strong> (list[str]) — Reference targets",name:"golds"},{anchor:"lighteval.metrics.metrics_sample.ROUGE.compute.predictions",description:"<strong>predictions</strong> (list[str]) — Predicted strings",name:"predictions"}],source:"https://github.com/huggingface/lighteval/blob/vr_722/src/lighteval/metrics/metrics_sample.py#L489",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> | |
| `}}),Lt=new $({props:{title:"BertScore",local:"lighteval.metrics.metrics_sample.BertScore",headingTag:"h3"}}),Ct=new f({props:{name:"class lighteval.metrics.metrics_sample.BertScore",anchor:"lighteval.metrics.metrics_sample.BertScore",parameters:[{name:"normalize_gold",val:": <built-in function callable> = None"},{name:"normalize_pred",val:": <built-in function callable> = None"}],source:"https://github.com/huggingface/lighteval/blob/vr_722/src/lighteval/metrics/metrics_sample.py#L550"}}),Mt=new f({props:{name:"compute",anchor:"lighteval.metrics.metrics_sample.BertScore.compute",parameters:[{name:"golds",val:": list"},{name:"predictions",val:": list"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"lighteval.metrics.metrics_sample.BertScore.compute.golds",description:"<strong>golds</strong> (list[str]) — Reference targets",name:"golds"},{anchor:"lighteval.metrics.metrics_sample.BertScore.compute.predictions",description:"<strong>predictions</strong> (list[str]) — Predicted strings",name:"predictions"}],source:"https://github.com/huggingface/lighteval/blob/vr_722/src/lighteval/metrics/metrics_sample.py#L580",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> | |
| `}}),wt=new $({props:{title:"Extractiveness",local:"lighteval.metrics.metrics_sample.Extractiveness",headingTag:"h3"}}),Et=new f({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 0x7ff9ff858160>"},{name:"normalize_pred",val:": <built-in function callable> = <function remove_braces_and_strip at 0x7ff9ff8581f0>"},{name:"input_column",val:": str = 'text'"}],source:"https://github.com/huggingface/lighteval/blob/vr_722/src/lighteval/metrics/metrics_sample.py#L609"}}),Tt=new f({props:{name:"compute",anchor:"lighteval.metrics.metrics_sample.Extractiveness.compute",parameters:[{name:"predictions",val:": list"},{name:"formatted_doc",val:": Doc"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"lighteval.metrics.metrics_sample.Extractiveness.compute.predictions",description:"<strong>predictions</strong> (list[str]) — Predicted strings, a list of length 1.",name:"predictions"},{anchor:"lighteval.metrics.metrics_sample.Extractiveness.compute.formatted_doc",description:"<strong>formatted_doc</strong> (Doc) — The formatted document.",name:"formatted_doc"}],source:"https://github.com/huggingface/lighteval/blob/vr_722/src/lighteval/metrics/metrics_sample.py#L631",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> | |
| `}}),kt=new $({props:{title:"Faithfulness",local:"lighteval.metrics.metrics_sample.Faithfulness",headingTag:"h3"}}),Dt=new f({props:{name:"class lighteval.metrics.metrics_sample.Faithfulness",anchor:"lighteval.metrics.metrics_sample.Faithfulness",parameters:[{name:"normalize_input",val:": <built-in function callable> = <function remove_braces at 0x7ff9ff858160>"},{name:"normalize_pred",val:": <built-in function callable> = <function remove_braces_and_strip at 0x7ff9ff8581f0>"},{name:"input_column",val:": str = 'text'"}],source:"https://github.com/huggingface/lighteval/blob/vr_722/src/lighteval/metrics/metrics_sample.py#L663"}}),Pt=new f({props:{name:"compute",anchor:"lighteval.metrics.metrics_sample.Faithfulness.compute",parameters:[{name:"predictions",val:": list"},{name:"formatted_doc",val:": Doc"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"lighteval.metrics.metrics_sample.Faithfulness.compute.predictions",description:"<strong>predictions</strong> (list[str]) — Predicted strings, a list of length 1.",name:"predictions"},{anchor:"lighteval.metrics.metrics_sample.Faithfulness.compute.formatted_doc",description:"<strong>formatted_doc</strong> (Doc) — The formatted document.",name:"formatted_doc"}],source:"https://github.com/huggingface/lighteval/blob/vr_722/src/lighteval/metrics/metrics_sample.py#L685",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> | |
| `}}),Nt=new $({props:{title:"BLEURT",local:"lighteval.metrics.metrics_sample.BLEURT",headingTag:"h3"}}),It=new f({props:{name:"class lighteval.metrics.metrics_sample.BLEURT",anchor:"lighteval.metrics.metrics_sample.BLEURT",parameters:[],source:"https://github.com/huggingface/lighteval/blob/vr_722/src/lighteval/metrics/metrics_sample.py#L709"}}),jt=new f({props:{name:"compute",anchor:"lighteval.metrics.metrics_sample.BLEURT.compute",parameters:[{name:"golds",val:": list"},{name:"predictions",val:": list"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"lighteval.metrics.metrics_sample.BLEURT.compute.golds",description:"<strong>golds</strong> (list[str]) — Reference targets",name:"golds"},{anchor:"lighteval.metrics.metrics_sample.BLEURT.compute.predictions",description:"<strong>predictions</strong> (list[str]) — Predicted strings",name:"predictions"}],source:"https://github.com/huggingface/lighteval/blob/vr_722/src/lighteval/metrics/metrics_sample.py#L730",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> | |
| `}}),At=new $({props:{title:"BLEU",local:"lighteval.metrics.metrics_sample.BLEU",headingTag:"h3"}}),Rt=new f({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_722/src/lighteval/metrics/metrics_sample.py#L746"}}),zt=new f({props:{name:"compute",anchor:"lighteval.metrics.metrics_sample.BLEU.compute",parameters:[{name:"golds",val:": list"},{name:"predictions",val:": list"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"lighteval.metrics.metrics_sample.BLEU.compute.golds",description:"<strong>golds</strong> (list[str]) — Reference targets",name:"golds"},{anchor:"lighteval.metrics.metrics_sample.BLEU.compute.predictions",description:"<strong>predictions</strong> (list[str]) — Predicted strings",name:"predictions"}],source:"https://github.com/huggingface/lighteval/blob/vr_722/src/lighteval/metrics/metrics_sample.py#L756",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:"StringDistance",local:"lighteval.metrics.metrics_sample.StringDistance",headingTag:"h3"}}),Vt=new f({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_722/src/lighteval/metrics/metrics_sample.py#L782"}}),Jt=new f({props:{name:"compute",anchor:"lighteval.metrics.metrics_sample.StringDistance.compute",parameters:[{name:"golds",val:": list"},{name:"predictions",val:": list"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"lighteval.metrics.metrics_sample.StringDistance.compute.golds",description:"<strong>golds</strong> (list[str]) — A list of possible golds. If it contains more than one item, only the first one is kept.",name:"golds"},{anchor:"lighteval.metrics.metrics_sample.StringDistance.compute.predictions",description:"<strong>predictions</strong> (list[str]) — Predicted strings.",name:"predictions"}],source:"https://github.com/huggingface/lighteval/blob/vr_722/src/lighteval/metrics/metrics_sample.py#L804",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> | |
| `}}),Ut=new f({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_722/src/lighteval/metrics/metrics_sample.py#L857"}}),Bt=new f({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_722/src/lighteval/metrics/metrics_sample.py#L850"}}),Ft=new $({props:{title:"JudgeLLM",local:"lighteval.metrics.metrics_sample.JudgeLLM",headingTag:"h3"}}),Gt=new f({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:": BaseModel = 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_722/src/lighteval/metrics/metrics_sample.py#L869"}}),qt=new $({props:{title:"JudgeLLMMTBench",local:"lighteval.metrics.metrics_sample.JudgeLLMMTBench",headingTag:"h3"}}),Ht=new f({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:": BaseModel = 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_722/src/lighteval/metrics/metrics_sample.py#L972"}}),Kt=new f({props:{name:"compute",anchor:"lighteval.metrics.metrics_sample.JudgeLLMMTBench.compute",parameters:[{name:"predictions",val:": list"},{name:"formatted_doc",val:": Doc"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/lighteval/blob/vr_722/src/lighteval/metrics/metrics_sample.py#L973"}}),Yt=new $({props:{title:"JudgeLLMMixEval",local:"lighteval.metrics.metrics_sample.JudgeLLMMixEval",headingTag:"h3"}}),Xt=new f({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:": BaseModel = 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_722/src/lighteval/metrics/metrics_sample.py#L1004"}}),Zt=new f({props:{name:"compute",anchor:"lighteval.metrics.metrics_sample.JudgeLLMMixEval.compute",parameters:[{name:"sample_ids",val:": list"},{name:"responses",val:": list"},{name:"formatted_docs",val:": list"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/lighteval/blob/vr_722/src/lighteval/metrics/metrics_sample.py#L1005"}}),Qt=new $({props:{title:"MajAtK",local:"lighteval.metrics.metrics_sample.MajAtK",headingTag:"h3"}}),Wt=new f({props:{name:"class lighteval.metrics.metrics_sample.MajAtK",anchor:"lighteval.metrics.metrics_sample.MajAtK",parameters:[{name:"k",val:": int"},{name:"normalize_gold",val:": <built-in function callable> = None"},{name:"normalize_pred",val:": <built-in function callable> = None"},{name:"strip_strings",val:": bool = False"},{name:"type_exact_match",val:": str = 'full'"}],source:"https://github.com/huggingface/lighteval/blob/vr_722/src/lighteval/metrics/metrics_sample.py#L1032"}}),er=new f({props:{name:"compute",anchor:"lighteval.metrics.metrics_sample.MajAtK.compute",parameters:[{name:"golds",val:": list"},{name:"predictions",val:": list"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"lighteval.metrics.metrics_sample.MajAtK.compute.golds",description:"<strong>golds</strong> (list[str]) — Reference targets",name:"golds"},{anchor:"lighteval.metrics.metrics_sample.MajAtK.compute.predictions",description:"<strong>predictions</strong> (list[str]) — k predicted strings",name:"predictions"}],source:"https://github.com/huggingface/lighteval/blob/vr_722/src/lighteval/metrics/metrics_sample.py#L1067",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> | |
| `}}),tr=new $({props:{title:"LLM-as-a-Judge",local:"llm-as-a-judge",headingTag:"h2"}}),rr=new $({props:{title:"JudgeLM",local:"lighteval.metrics.llm_as_judge.JudgeLM",headingTag:"h3"}}),sr=new f({props:{name:"class lighteval.metrics.llm_as_judge.JudgeLM",anchor:"lighteval.metrics.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.llm_as_judge.JudgeLM.model",description:"<strong>model</strong> (str) — The name of the model.",name:"model"},{anchor:"lighteval.metrics.llm_as_judge.JudgeLM.templates",description:"<strong>templates</strong> (Callable) — A function taking into account the question, options, answer, and gold and returning the judge prompt.",name:"templates"},{anchor:"lighteval.metrics.llm_as_judge.JudgeLM.process_judge_response",description:"<strong>process_judge_response</strong> (Callable) — A function for processing the judge’s response.",name:"process_judge_response"},{anchor:"lighteval.metrics.llm_as_judge.JudgeLM.judge_backend",description:"<strong>judge_backend</strong> (Literal[“openai”, “transformers”, “tgi”, “vllm”]) — The backend for the judge.",name:"judge_backend"},{anchor:"lighteval.metrics.llm_as_judge.JudgeLM.url",description:"<strong>url</strong> (str | None) — The URL for the OpenAI API.",name:"url"},{anchor:"lighteval.metrics.llm_as_judge.JudgeLM.api_key",description:"<strong>api_key</strong> (str | None) — The API key for the OpenAI API (either OpenAI or HF key).",name:"api_key"},{anchor:"lighteval.metrics.llm_as_judge.JudgeLM.model",description:"<strong>model</strong> (str) — The name of the model.",name:"model"},{anchor:"lighteval.metrics.llm_as_judge.JudgeLM.template",description:"<strong>template</strong> (Callable) — A function taking into account the question, options, answer, and gold and returning the judge prompt.",name:"template"},{anchor:"lighteval.metrics.llm_as_judge.JudgeLM.API_MAX_RETRY",description:"<strong>API_MAX_RETRY</strong> (int) — The maximum number of retries for the API.",name:"API_MAX_RETRY"},{anchor:"lighteval.metrics.llm_as_judge.JudgeLM.API_RETRY_SLEEP",description:"<strong>API_RETRY_SLEEP</strong> (int) — The time to sleep between retries.",name:"API_RETRY_SLEEP"},{anchor:"lighteval.metrics.llm_as_judge.JudgeLM.client",description:"<strong>client</strong> (OpenAI | None) — The OpenAI client.",name:"client"},{anchor:"lighteval.metrics.llm_as_judge.JudgeLM.pipe",description:"<strong>pipe</strong> (LLM | AutoModel | None) — The Transformers or vllm pipeline.",name:"pipe"},{anchor:"lighteval.metrics.llm_as_judge.JudgeLM.process_judge_response",description:"<strong>process_judge_response</strong> (Callable) — A function for processing the judge’s response.",name:"process_judge_response"},{anchor:"lighteval.metrics.llm_as_judge.JudgeLM.url",description:"<strong>url</strong> (str | None) — The URL for the OpenAI API.",name:"url"},{anchor:"lighteval.metrics.llm_as_judge.JudgeLM.api_key",description:"<strong>api_key</strong> (str | None) — The API key for the OpenAI API (either OpenAI or HF key).",name:"api_key"},{anchor:"lighteval.metrics.llm_as_judge.JudgeLM.backend",description:"<strong>backend</strong> (Literal[“openai”, “transformers”, “tgi”, “vllm”]) — The backend for the judge",name:"backend"}],source:"https://github.com/huggingface/lighteval/blob/vr_722/src/lighteval/metrics/llm_as_judge.py#L48"}}),ir=new f({props:{name:"dict_of_lists_to_list_of_dicts",anchor:"lighteval.metrics.llm_as_judge.JudgeLM.dict_of_lists_to_list_of_dicts",parameters:[{name:"dict_of_lists",val:""}],parametersDescription:[{anchor:"lighteval.metrics.llm_as_judge.JudgeLM.dict_of_lists_to_list_of_dicts.dict_of_lists",description:`<strong>dict_of_lists</strong> — 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_722/src/lighteval/metrics/llm_as_judge.py#L188",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script> | |
| <p>A list of dictionaries.</p> | |
| `}}),lr=new f({props:{name:"evaluate_answer",anchor:"lighteval.metrics.llm_as_judge.JudgeLM.evaluate_answer",parameters:[{name:"question",val:": str"},{name:"answer",val:": str"},{name:"options",val:": list[str] | None = None"},{name:"gold",val:": str | None = None"}],parametersDescription:[{anchor:"lighteval.metrics.llm_as_judge.JudgeLM.evaluate_answer.questions",description:"<strong>questions</strong> (list[str]) — The prompt asked to the evaluated model",name:"questions"},{anchor:"lighteval.metrics.llm_as_judge.JudgeLM.evaluate_answer.answers",description:"<strong>answers</strong> (list[str]) — Answer given by the evaluated model",name:"answers"},{anchor:"lighteval.metrics.llm_as_judge.JudgeLM.evaluate_answer.references",description:"<strong>references</strong> (list[str]) — A list of reference answers",name:"references"}],source:"https://github.com/huggingface/lighteval/blob/vr_722/src/lighteval/metrics/llm_as_judge.py#L257",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script> | |
| <p>A tuple containing the score, prompts, and judgment.</p> | |
| `}}),ar=new 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