Buckets:
| import{s as ci,n as pi,o as gi}from"../chunks/scheduler.7da89386.js";import{S as di,i as ui,g as l,s as a,r as m,A as hi,h as i,f as t,c as n,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.c9f3fc15.js";import{H as $,E as vi}from"../chunks/getInferenceSnippets.7cf363b6.js";function fi(xl){let E,rs,es,ss,Le,as,we,ns,Me,ls,ke,De,is,Te,os,P,Ee,on,mr,yl="Metric computed over the whole corpora, with computations happening at the aggregation phase",ms,Pe,cs,N,Ne,mn,cr,Cl="Metric computed per sample, then aggregated over the corpus",ps,Re,gs,R,je,cn,pr,Ll=`Some metrics are more advantageous to compute together at once. | |
| For example, if a costly preprocessing is the same for all metrics, it makes more sense to compute it once.`,ds,Ie,us,j,Se,pn,gr,wl="MetricGrouping computed over the whole corpora, with computations happening at the aggregation phase",hs,Ae,vs,I,Ve,gn,dr,Ml="MetricGrouping are computed per sample, then aggregated over the corpus",fs,ze,_s,Be,$s,S,Je,dn,ee,Fe,un,ur,kl="Computes the metric score over all the corpus generated items, by using the scikit learn implementation.",bs,Ue,xs,A,Oe,hn,te,Ge,vn,hr,Dl="Computes the metric score over all the corpus generated items.",ys,qe,Cs,V,He,fn,re,Ke,_n,vr,Tl="Computes the metric score over all the corpus generated items, by using the sacrebleu implementation.",Ls,Xe,ws,z,Ze,$n,se,Qe,bn,fr,El='Computes the Matthews Correlation Coefficient, using scikit learn (<a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html" rel="nofollow">doc</a>).',Ms,We,ks,Ye,Ds,C,et,xn,ae,tt,yn,_r,Pl="Computes the metric over a list of golds and predictions for one single sample.",Cn,ne,rt,Ln,$r,Nl="Compares two strings only.",Ts,st,Es,L,at,wn,le,nt,Mn,br,Rl="Computes the metric over a list of golds and predictions for one single sample.",kn,ie,lt,Dn,xr,jl="Compares two strings only.",Ps,it,Ns,B,ot,Tn,oe,mt,En,yr,Il=`Computes the log likelihood accuracy: is the choice with the highest logprob in <code>choices_logprob</code> present | |
| in the <code>gold_ixs</code>?`,Rs,ct,js,J,pt,Pn,me,gt,Nn,Cr,Sl="Computes the log likelihood probability: chance of choosing the best choice.",Is,dt,Ss,F,ut,Rn,ce,ht,jn,Lr,Al="Computes the log likelihood probability: chance of choosing the best choice.",As,vt,Vs,U,ft,In,pe,_t,Sn,wr,Vl=`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,$t,Bs,O,bt,An,ge,xt,Vn,Mr,zl="Mean reciprocal rank. Measures the quality of a ranking of choices (ordered by correctness).",Js,yt,Fs,G,Ct,zn,de,Lt,Bn,kr,Bl="Computes the metric(s) over a list of golds and predictions for one single sample.",Us,wt,Os,q,Mt,Jn,ue,kt,Fn,Dr,Jl="Computes the prediction, recall and f1 score using the bert scorer.",Gs,Dt,qs,H,Tt,Un,w,Et,On,Tr,Fl="Compute the extractiveness of the predictions.",Gn,Er,Ul=`This method calculates coverage, density, and compression scores for a single | |
| prediction against the input text.`,Hs,Pt,Ks,K,Nt,qn,M,Rt,Hn,Pr,Ol="Compute the faithfulness of the predictions.",Kn,Nr,Gl="The SummaCZS (Summary Content Zero-Shot) model is used with configurable granularity and model variation.",Xs,jt,Zs,X,It,Xn,he,St,Zn,Rr,ql="Uses the stored BLEURT scorer to compute the score on the current sample.",Qs,At,Ws,Z,Vt,Qn,ve,zt,Wn,jr,Hl="Computes the sentence level BLEU between the golds and each prediction, then takes the average.",Ys,Bt,ea,y,Jt,Yn,fe,Ft,el,Ir,Kl="Computes all the requested metrics on the golds and prediction.",tl,k,Ut,rl,Sr,Xl="Compute the edit similarity between two lists of strings.",sl,Ar,Zl=`Edit similarity is also used in the paper | |
| Lee, Katherine, et al. | |
| “Deduplicating training data makes language models better.” | |
| arXiv preprint arXiv:2107.06499 (2021).`,al,_e,Ot,nl,Vr,Ql="Compute the length of the longest common prefix.",ta,Gt,ra,qt,Ht,sa,Kt,aa,Q,Xt,ll,$e,Zt,il,zr,Wl=`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.`,na,Qt,la,W,Wt,ol,be,Yt,ml,Br,Yl=`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.`,ia,er,oa,Y,tr,cl,xe,rr,pl,Jr,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.`,ma,sr,ca,ar,pa,b,nr,gl,Fr,ti="A class representing a judge for evaluating answers using either the chosen backend.",dl,Ur,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.`,ul,x,lr,hl,Or,si="Transform a dictionary of lists into a list of dictionaries.",vl,Gr,ai=`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.`,fl,qr,ni="Example:",_l,Hr,li=`<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>`,$l,ye,ir,bl,Kr,ii="Evaluates an answer using either Transformers or OpenAI API.",ga,or,da,ts,ua;return Le=new $({props:{title:"Metrics",local:"metrics",headingTag:"h1"}}),we=new $({props:{title:"Metrics",local:"metrics",headingTag:"h2"}}),Me=new $({props:{title:"Metric",local:"lighteval.metrics.Metric",headingTag:"h3"}}),De=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:": 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_985/src/lighteval/metrics/utils/metric_utils.py#L33"}}),Te=new $({props:{title:"CorpusLevelMetric",local:"lighteval.metrics.utils.metric_utils.CorpusLevelMetric",headingTag:"h3"}}),Ee=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:": 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_985/src/lighteval/metrics/utils/metric_utils.py#L114"}}),Pe=new $({props:{title:"SampleLevelMetric",local:"lighteval.metrics.utils.metric_utils.SampleLevelMetric",headingTag:"h3"}}),Ne=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:": 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_985/src/lighteval/metrics/utils/metric_utils.py#L121"}}),Re=new $({props:{title:"MetricGrouping",local:"lighteval.metrics.utils.metric_utils.MetricGrouping",headingTag:"h3"}}),je=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:": 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_985/src/lighteval/metrics/utils/metric_utils.py#L103"}}),Ie=new $({props:{title:"CorpusLevelMetricGrouping",local:"lighteval.metrics.utils.metric_utils.CorpusLevelMetricGrouping",headingTag:"h3"}}),Se=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:": 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_985/src/lighteval/metrics/utils/metric_utils.py#L128"}}),Ae=new $({props:{title:"SampleLevelMetricGrouping",local:"lighteval.metrics.utils.metric_utils.SampleLevelMetricGrouping",headingTag:"h3"}}),Ve=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:": 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_985/src/lighteval/metrics/utils/metric_utils.py#L135"}}),ze=new $({props:{title:"Corpus Metrics",local:"corpus-metrics",headingTag:"h2"}}),Be=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_985/src/lighteval/metrics/metrics_corpus.py#L81"}}),Fe=new f({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_985/src/lighteval/metrics/metrics_corpus.py#L96"}}),Ue=new $({props:{title:"CorpusLevelPerplexityMetric",local:"lighteval.metrics.metrics_corpus.CorpusLevelPerplexityMetric",headingTag:"h3"}}),Oe=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_985/src/lighteval/metrics/metrics_corpus.py#L164"}}),Ge=new f({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_985/src/lighteval/metrics/metrics_corpus.py#L182"}}),qe=new $({props:{title:"CorpusLevelTranslationMetric",local:"lighteval.metrics.metrics_corpus.CorpusLevelTranslationMetric",headingTag:"h3"}}),He=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_985/src/lighteval/metrics/metrics_corpus.py#L116"}}),Ke=new f({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_985/src/lighteval/metrics/metrics_corpus.py#L142"}}),Xe=new $({props:{title:"MatthewsCorrCoef",local:"lighteval.metrics.metrics_corpus.MatthewsCorrCoef",headingTag:"h3"}}),Ze=new f({props:{name:"class lighteval.metrics.metrics_corpus.MatthewsCorrCoef",anchor:"lighteval.metrics.metrics_corpus.MatthewsCorrCoef",parameters:[],source:"https://github.com/huggingface/lighteval/blob/vr_985/src/lighteval/metrics/metrics_corpus.py#L66"}}),Qe=new f({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]) — List of GenerativeCorpusMetricInput",name:"items"}],source:"https://github.com/huggingface/lighteval/blob/vr_985/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> | |
| `}}),We=new $({props:{title:"Sample Metrics",local:"sample-metrics",headingTag:"h2"}}),Ye=new $({props:{title:"ExactMatches",local:"lighteval.metrics.metrics_sample.ExactMatches",headingTag:"h3"}}),et=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_985/src/lighteval/metrics/metrics_sample.py#L81"}}),tt=new f({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) — The document containing gold references.",name:"doc"},{anchor:"lighteval.metrics.metrics_sample.ExactMatches.compute.model_response",description:"<strong>model_response</strong> (ModelResponse) — The model’s response containing predictions.",name:"model_response"},{anchor:"lighteval.metrics.metrics_sample.ExactMatches.compute.*kwargs",description:"*<strong>*kwargs</strong> — Additional keyword arguments.",name:"*kwargs"}],source:"https://github.com/huggingface/lighteval/blob/vr_985/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 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_985/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"}}),at=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_985/src/lighteval/metrics/metrics_sample.py#L170"}}),nt=new f({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) — The document containing gold references.",name:"doc"},{anchor:"lighteval.metrics.metrics_sample.F1_score.compute.model_response",description:"<strong>model_response</strong> (ModelResponse) — The model’s response containing predictions.",name:"model_response"},{anchor:"lighteval.metrics.metrics_sample.F1_score.compute.*kwargs",description:"*<strong>*kwargs</strong> — Additional keyword arguments.",name:"*kwargs"}],source:"https://github.com/huggingface/lighteval/blob/vr_985/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> | |
| `}}),lt=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_985/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 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_985/src/lighteval/metrics/metrics_sample.py#L243"}}),mt=new f({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) — The document containing choices and gold indices.",name:"doc"},{anchor:"lighteval.metrics.metrics_sample.LoglikelihoodAcc.compute.model_response",description:"<strong>model_response</strong> (ModelResponse) — The model’s response containing logprobs.",name:"model_response"},{anchor:"lighteval.metrics.metrics_sample.LoglikelihoodAcc.compute.*kwargs",description:"*<strong>*kwargs</strong> — Additional keyword arguments.",name:"*kwargs"}],source:"https://github.com/huggingface/lighteval/blob/vr_985/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 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 0x7f6e07da2930>"}],source:"https://github.com/huggingface/lighteval/blob/vr_985/src/lighteval/metrics/metrics_sample.py#L297"}}),gt=new f({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) — The document containing choices and gold indices.",name:"doc"},{anchor:"lighteval.metrics.metrics_sample.NormalizedMultiChoiceProbability.compute.model_response",description:"<strong>model_response</strong> (ModelResponse) — The model’s response containing logprobs.",name:"model_response"},{anchor:"lighteval.metrics.metrics_sample.NormalizedMultiChoiceProbability.compute.*kwargs",description:"*<strong>*kwargs</strong> — Additional keyword arguments.",name:"*kwargs"}],source:"https://github.com/huggingface/lighteval/blob/vr_985/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 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 0x7f6e07da2930>"}],source:"https://github.com/huggingface/lighteval/blob/vr_985/src/lighteval/metrics/metrics_sample.py#L357"}}),ht=new f({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) — The document containing choices and gold indices.",name:"doc"},{anchor:"lighteval.metrics.metrics_sample.Probability.compute.model_response",description:"<strong>model_response</strong> (ModelResponse) — The model’s response containing logprobs.",name:"model_response"},{anchor:"lighteval.metrics.metrics_sample.Probability.compute.*kwargs",description:"*<strong>*kwargs</strong> — Additional keyword arguments.",name:"*kwargs"}],source:"https://github.com/huggingface/lighteval/blob/vr_985/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 $({props:{title:"Recall",local:"lighteval.metrics.metrics_sample.Recall",headingTag:"h3"}}),ft=new f({props:{name:"class lighteval.metrics.metrics_sample.Recall",anchor:"lighteval.metrics.metrics_sample.Recall",parameters:[{name:"k",val:": int"}],source:"https://github.com/huggingface/lighteval/blob/vr_985/src/lighteval/metrics/metrics_sample.py#L408"}}),_t=new f({props:{name:"compute",anchor:"lighteval.metrics.metrics_sample.Recall.compute",parameters:[{name:"doc",val:": Doc"},{name:"model_response",val:": ModelResponse"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"lighteval.metrics.metrics_sample.Recall.compute.doc",description:"<strong>doc</strong> (Doc) — The document containing choices and gold indices.",name:"doc"},{anchor:"lighteval.metrics.metrics_sample.Recall.compute.model_response",description:"<strong>model_response</strong> (ModelResponse) — The model’s response containing logprobs.",name:"model_response"},{anchor:"lighteval.metrics.metrics_sample.Recall.compute.*kwargs",description:"*<strong>*kwargs</strong> — Additional keyword arguments.",name:"*kwargs"}],source:"https://github.com/huggingface/lighteval/blob/vr_985/src/lighteval/metrics/metrics_sample.py#L418",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script> | |
| <p>Score: 1 if one of the top level predicted choices was correct, 0 otherwise.</p> | |
| `,returnType:`<script context="module">export const metadata = 'undefined';<\/script> | |
| <p>int</p> | |
| `}}),$t=new $({props:{title:"MRR",local:"lighteval.metrics.metrics_sample.MRR",headingTag:"h3"}}),bt=new 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_985/src/lighteval/metrics/metrics_sample.py#L438"}}),xt=new f({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) — The model’s response containing logprobs.",name:"model_response"},{anchor:"lighteval.metrics.metrics_sample.MRR.compute.doc",description:"<strong>doc</strong> (Doc) — The document containing choices and gold indices.",name:"doc"},{anchor:"lighteval.metrics.metrics_sample.MRR.compute.*kwargs",description:"*<strong>*kwargs</strong> — Additional keyword arguments.",name:"*kwargs"}],source:"https://github.com/huggingface/lighteval/blob/vr_985/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 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:": 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_985/src/lighteval/metrics/metrics_sample.py#L486"}}),Lt=new f({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) — The document containing gold references.",name:"doc"},{anchor:"lighteval.metrics.metrics_sample.ROUGE.compute.model_response",description:"<strong>model_response</strong> (ModelResponse) — The model’s response containing predictions.",name:"model_response"},{anchor:"lighteval.metrics.metrics_sample.ROUGE.compute.*kwargs",description:"*<strong>*kwargs</strong> — Additional keyword arguments.",name:"*kwargs"}],source:"https://github.com/huggingface/lighteval/blob/vr_985/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> | |
| `}}),wt=new $({props:{title:"BertScore",local:"lighteval.metrics.metrics_sample.BertScore",headingTag:"h3"}}),Mt=new f({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_985/src/lighteval/metrics/metrics_sample.py#L598"}}),kt=new f({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) — The document containing gold references.",name:"doc"},{anchor:"lighteval.metrics.metrics_sample.BertScore.compute.model_response",description:"<strong>model_response</strong> (ModelResponse) — The model’s response containing predictions.",name:"model_response"},{anchor:"lighteval.metrics.metrics_sample.BertScore.compute.*kwargs",description:"*<strong>*kwargs</strong> — Additional keyword arguments.",name:"*kwargs"}],source:"https://github.com/huggingface/lighteval/blob/vr_985/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> | |
| `}}),Dt=new $({props:{title:"Extractiveness",local:"lighteval.metrics.metrics_sample.Extractiveness",headingTag:"h3"}}),Tt=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 0x7f6d00a91e10>"},{name:"normalize_pred",val:": <built-in function callable> = <function remove_braces_and_strip at 0x7f6d00a91ea0>"},{name:"input_column",val:": str = 'text'"},{name:"language",val:": typing.Literal['en', 'de', 'fr', 'it'] = 'en'"}],source:"https://github.com/huggingface/lighteval/blob/vr_985/src/lighteval/metrics/metrics_sample.py#L661"}}),Et=new f({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) — The document containing input text.",name:"doc"},{anchor:"lighteval.metrics.metrics_sample.Extractiveness.compute.model_response",description:"<strong>model_response</strong> (ModelResponse) — The model’s response containing predictions.",name:"model_response"},{anchor:"lighteval.metrics.metrics_sample.Extractiveness.compute.*kwargs",description:"*<strong>*kwargs</strong> — Additional keyword arguments.",name:"*kwargs"}],source:"https://github.com/huggingface/lighteval/blob/vr_985/src/lighteval/metrics/metrics_sample.py#L685",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"}}),Nt=new f({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 0x7f6d00a91e10>"},{name:"normalize_pred",val:": typing.Callable = <function remove_braces_and_strip at 0x7f6d00a91ea0>"},{name:"input_column",val:": str = 'text'"}],source:"https://github.com/huggingface/lighteval/blob/vr_985/src/lighteval/metrics/metrics_sample.py#L717"}}),Rt=new f({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) — The document containing input text.",name:"doc"},{anchor:"lighteval.metrics.metrics_sample.Faithfulness.compute.model_response",description:"<strong>model_response</strong> (ModelResponse) — The model’s response containing predictions.",name:"model_response"},{anchor:"lighteval.metrics.metrics_sample.Faithfulness.compute.*kwargs",description:"*<strong>*kwargs</strong> — Additional keyword arguments.",name:"*kwargs"}],source:"https://github.com/huggingface/lighteval/blob/vr_985/src/lighteval/metrics/metrics_sample.py#L738",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> | |
| `}}),jt=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_985/src/lighteval/metrics/metrics_sample.py#L765"}}),St=new f({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) — The document containing gold references.",name:"doc"},{anchor:"lighteval.metrics.metrics_sample.BLEURT.compute.model_response",description:"<strong>model_response</strong> (ModelResponse) — The model’s response containing predictions.",name:"model_response"},{anchor:"lighteval.metrics.metrics_sample.BLEURT.compute.*kwargs",description:"*<strong>*kwargs</strong> — Additional keyword arguments.",name:"*kwargs"}],source:"https://github.com/huggingface/lighteval/blob/vr_985/src/lighteval/metrics/metrics_sample.py#L786",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"}}),Vt=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_985/src/lighteval/metrics/metrics_sample.py#L805"}}),zt=new f({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) — The document containing gold references.",name:"doc"},{anchor:"lighteval.metrics.metrics_sample.BLEU.compute.model_response",description:"<strong>model_response</strong> (ModelResponse) — The model’s response containing predictions.",name:"model_response"},{anchor:"lighteval.metrics.metrics_sample.BLEU.compute.*kwargs",description:"*<strong>*kwargs</strong> — Additional keyword arguments.",name:"*kwargs"}],source:"https://github.com/huggingface/lighteval/blob/vr_985/src/lighteval/metrics/metrics_sample.py#L815",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> | |
| `}}),Bt=new $({props:{title:"StringDistance",local:"lighteval.metrics.metrics_sample.StringDistance",headingTag:"h3"}}),Jt=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_985/src/lighteval/metrics/metrics_sample.py#L847"}}),Ft=new f({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) — The document containing gold references.",name:"doc"},{anchor:"lighteval.metrics.metrics_sample.StringDistance.compute.model_response",description:"<strong>model_response</strong> (ModelResponse) — The model’s response containing predictions.",name:"model_response"},{anchor:"lighteval.metrics.metrics_sample.StringDistance.compute.*kwargs",description:"*<strong>*kwargs</strong> — Additional keyword arguments.",name:"*kwargs"}],source:"https://github.com/huggingface/lighteval/blob/vr_985/src/lighteval/metrics/metrics_sample.py#L869",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_985/src/lighteval/metrics/metrics_sample.py#L927",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> | |
| `}}),Ot=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_985/src/lighteval/metrics/metrics_sample.py#L920"}}),Gt=new $({props:{title:"JudgeLLM",local:"lighteval.metrics.metrics_sample.JudgeLLM",headingTag:"h3"}}),Ht=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:": 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"},{name:"backend_options",val:": dict | None = None"}],source:"https://github.com/huggingface/lighteval/blob/vr_985/src/lighteval/metrics/metrics_sample.py#L942"}}),Kt=new $({props:{title:"JudgeLLMMTBench",local:"lighteval.metrics.metrics_sample.JudgeLLMMTBench",headingTag:"h3"}}),Xt=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:": 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"},{name:"backend_options",val:": dict | None = None"}],source:"https://github.com/huggingface/lighteval/blob/vr_985/src/lighteval/metrics/metrics_sample.py#L1046"}}),Zt=new f({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_985/src/lighteval/metrics/metrics_sample.py#L1047"}}),Qt=new $({props:{title:"JudgeLLMMixEval",local:"lighteval.metrics.metrics_sample.JudgeLLMMixEval",headingTag:"h3"}}),Wt=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:": 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"},{name:"backend_options",val:": dict | None = None"}],source:"https://github.com/huggingface/lighteval/blob/vr_985/src/lighteval/metrics/metrics_sample.py#L1078"}}),Yt=new f({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_985/src/lighteval/metrics/metrics_sample.py#L1079"}}),er=new $({props:{title:"MajAtK",local:"lighteval.metrics.metrics_sample.MajAtK",headingTag:"h3"}}),tr=new f({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_985/src/lighteval/metrics/metrics_sample.py#L1216"}}),rr=new f({props:{name:"compute",anchor:"lighteval.metrics.metrics_sample.MajAtK.compute",parameters:[{name:"doc",val:": Doc"},{name:"model_response",val:": ModelResponse"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"lighteval.metrics.metrics_sample.MajAtK.compute.doc",description:"<strong>doc</strong> (Doc) — The document containing gold references.",name:"doc"},{anchor:"lighteval.metrics.metrics_sample.MajAtK.compute.model_response",description:"<strong>model_response</strong> (ModelResponse) — The model’s response containing predictions.",name:"model_response"},{anchor:"lighteval.metrics.metrics_sample.MajAtK.compute.*kwargs",description:"*<strong>*kwargs</strong> — Additional keyword arguments.",name:"*kwargs"}],source:"https://github.com/huggingface/lighteval/blob/vr_985/src/lighteval/metrics/metrics_sample.py#L1229",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"}}),ar=new $({props:{title:"JudgeLM",local:"lighteval.metrics.utils.llm_as_judge.JudgeLM",headingTag:"h3"}}),nr=new f({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"},{name:"backend_options",val:": dict | None = None"}],parametersDescription:[{anchor:"lighteval.metrics.utils.llm_as_judge.JudgeLM.model",description:"<strong>model</strong> (str) — The name of the model.",name:"model"},{anchor:"lighteval.metrics.utils.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.utils.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.utils.llm_as_judge.JudgeLM.judge_backend",description:"<strong>judge_backend</strong> (Literal[“litellm”, “openai”, “transformers”, “tgi”, “vllm”, “inference-providers”]) — The backend for the judge.",name:"judge_backend"},{anchor:"lighteval.metrics.utils.llm_as_judge.JudgeLM.url",description:"<strong>url</strong> (str | None) — 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) — The API key for the OpenAI API (either OpenAI or HF key).",name:"api_key"},{anchor:"lighteval.metrics.utils.llm_as_judge.JudgeLM.max_tokens",description:"<strong>max_tokens</strong> (int) — The maximum number of tokens to generate. Defaults to 512.",name:"max_tokens"},{anchor:"lighteval.metrics.utils.llm_as_judge.JudgeLM.response_format",description:"<strong>response_format</strong> (BaseModel | None) — The format of the response from the API, used for the OpenAI and TGI backend.",name:"response_format"},{anchor:"lighteval.metrics.utils.llm_as_judge.JudgeLM.hf_provider",description:`<strong>hf_provider</strong> (Literal[“black-forest-labs”, “cerebras”, “cohere”, “fal-ai”, “fireworks-ai”, — | |
| “inference-providers”, “hyperbolic”, “nebius”, “novita”, “openai”, “replicate”, “sambanova”, “together”] | None): | |
| The HuggingFace provider when using the inference-providers backend.`,name:"hf_provider"},{anchor:"lighteval.metrics.utils.llm_as_judge.JudgeLM.backend_options",description:"<strong>backend_options</strong> (dict | None) — Options for the backend. Currently only supported for litellm.",name:"backend_options"}],source:"https://github.com/huggingface/lighteval/blob/vr_985/src/lighteval/metrics/utils/llm_as_judge.py#L67"}}),lr=new f({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> — 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_985/src/lighteval/metrics/utils/llm_as_judge.py#L204",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script> | |
| <p>A list of dictionaries.</p> | |
| `}}),ir=new f({props:{name:"evaluate_answer",anchor:"lighteval.metrics.utils.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.utils.llm_as_judge.JudgeLM.evaluate_answer.question",description:"<strong>question</strong> (str) — The prompt asked to the evaluated model.",name:"question"},{anchor:"lighteval.metrics.utils.llm_as_judge.JudgeLM.evaluate_answer.answer",description:"<strong>answer</strong> (str) — Answer given by the evaluated model.",name:"answer"},{anchor:"lighteval.metrics.utils.llm_as_judge.JudgeLM.evaluate_answer.options",description:"<strong>options</strong> (list[str] | None) — Optional list of answer options.",name:"options"},{anchor:"lighteval.metrics.utils.llm_as_judge.JudgeLM.evaluate_answer.gold",description:"<strong>gold</strong> (str | None) — Optional reference answer.",name:"gold"}],source:"https://github.com/huggingface/lighteval/blob/vr_985/src/lighteval/metrics/utils/llm_as_judge.py#L272",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script> | |
| <p>A tuple containing the score, prompts, and judgment.</p> | |
| `}}),or=new 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$i(xl){return gi(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class Li extends di{constructor(E){super(),ui(this,E,$i,fi,ci,{})}}export{Li as component}; | |
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