import{S as ta,i as aa,s as oa,e as s,k as p,w as x,t as r,M as sa,c as l,d as a,m as d,a as n,x as T,h as i,b as g,G as t,g as f,y as k,q,o as j,B as P,v as la,L as Zt}from"../../chunks/vendor-hf-doc-builder.js";import{D as Ee}from"../../chunks/Docstring-hf-doc-builder.js";import{C as ea}from"../../chunks/CodeBlock-hf-doc-builder.js";import{I as Xt}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{E as Yt}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";function na(X){let m,E,v,u,_;return u=new ea({props:{code:`from evaluate import evaluator

# Sentiment analysis evaluator
evaluator("sentiment-analysis")`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> evaluate <span class="hljs-keyword">import</span> evaluator

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Sentiment analysis evaluator</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>evaluator(<span class="hljs-string">&quot;sentiment-analysis&quot;</span>)`}}),{c(){m=s("p"),E=r("Examples:"),v=p(),x(u.$$.fragment)},l(o){m=l(o,"P",{});var h=n(m);E=i(h,"Examples:"),h.forEach(a),v=d(o),T(u.$$.fragment,o)},m(o,h){f(o,m,h),t(m,E),f(o,v,h),k(u,o,h),_=!0},p:Zt,i(o){_||(q(u.$$.fragment,o),_=!0)},o(o){j(u.$$.fragment,o),_=!1},d(o){o&&a(m),o&&a(v),P(u,o)}}}function ra(X){let m,E,v,u,_;return u=new ea({props:{code:`from evaluate import evaluator
from datasets import Dataset, load_dataset

e = evaluator("text-classification")
data =  Dataset.from_dict(load_dataset("imdb")["test"][:2])

results = e.compute(
    model_or_pipeline="huggingface/prunebert-base-uncased-6-finepruned-w-distil-mnli",
    data=data,
    metric="accuracy",
    input_column="text",
    label_column="label",
    label_mapping={"LABEL_0": 0.0, "LABEL_1": 1.0},
    strategy="bootstrap",
    n_resamples=10,
    random_state=0
)`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> evaluate <span class="hljs-keyword">import</span> evaluator
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> Dataset, load_dataset

<span class="hljs-meta">&gt;&gt;&gt; </span>e = evaluator(<span class="hljs-string">&quot;text-classification&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>data =  Dataset.from_dict(load_dataset(<span class="hljs-string">&quot;imdb&quot;</span>)[<span class="hljs-string">&quot;test&quot;</span>][:<span class="hljs-number">2</span>])

<span class="hljs-meta">&gt;&gt;&gt; </span>results = e.compute(
<span class="hljs-meta">&gt;&gt;&gt; </span>    model_or_pipeline=<span class="hljs-string">&quot;huggingface/prunebert-base-uncased-6-finepruned-w-distil-mnli&quot;</span>,
<span class="hljs-meta">&gt;&gt;&gt; </span>    data=data,
<span class="hljs-meta">&gt;&gt;&gt; </span>    metric=<span class="hljs-string">&quot;accuracy&quot;</span>,
<span class="hljs-meta">&gt;&gt;&gt; </span>    input_column=<span class="hljs-string">&quot;text&quot;</span>,
<span class="hljs-meta">&gt;&gt;&gt; </span>    label_column=<span class="hljs-string">&quot;label&quot;</span>,
<span class="hljs-meta">&gt;&gt;&gt; </span>    label_mapping={<span class="hljs-string">&quot;LABEL_0&quot;</span>: <span class="hljs-number">0.0</span>, <span class="hljs-string">&quot;LABEL_1&quot;</span>: <span class="hljs-number">1.0</span>},
<span class="hljs-meta">&gt;&gt;&gt; </span>    strategy=<span class="hljs-string">&quot;bootstrap&quot;</span>,
<span class="hljs-meta">&gt;&gt;&gt; </span>    n_resamples=<span class="hljs-number">10</span>,
<span class="hljs-meta">&gt;&gt;&gt; </span>    random_state=<span class="hljs-number">0</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>)`}}),{c(){m=s("p"),E=r("Examples:"),v=p(),x(u.$$.fragment)},l(o){m=l(o,"P",{});var h=n(m);E=i(h,"Examples:"),h.forEach(a),v=d(o),T(u.$$.fragment,o)},m(o,h){f(o,m,h),t(m,E),f(o,v,h),k(u,o,h),_=!0},p:Zt,i(o){_||(q(u.$$.fragment,o),_=!0)},o(o){j(u.$$.fragment,o),_=!1},d(o){o&&a(m),o&&a(v),P(u,o)}}}function ia(X){let m,E,v,u,_,o,h,re,Fe,we,Y,Ue,xe,N,M,ie,R,Ie,ce,Oe,Te,Z,Se,ke,$,B,ze,V,Re,ee,Be,Ve,He,L,Ge,pe,Je,We,de,Ke,Qe,Xe,F,qe,te,Ye,je,y,H,Ze,ue,et,tt,me,at,ot,U,G,st,A,lt,fe,nt,rt,ve,it,ct,Pe,ae,pt,Ce,b,J,dt,he,ut,mt,w,ft,oe,vt,ht,ge,gt,_t,_e,bt,$t,yt,W,Et,be,wt,xt,Tt,C,K,kt,$e,qt,jt,I,De;return o=new Xt({}),R=new Xt({}),B=new Ee({props:{name:"evaluate.evaluator",anchor:"evaluate.evaluator",parameters:[{name:"task",val:": str = None"}],parametersDescription:[{anchor:"evaluate.evaluator.task",description:`<strong>task</strong> (<code>str</code>) &#x2014;
The task defining which evaluator will be returned. Currently accepted tasks are:</p>
<ul>
<li><code>&quot;text-classification&quot;</code> (alias <code>&quot;sentiment-analysis&quot;</code> available): will return a <a href="/docs/evaluate/pr_137/en/package_reference/evaluator_classes#evaluate.TextClassificationEvaluator">TextClassificationEvaluator</a>.</li>
</ul>`,name:"task"}],source:"https://github.com/huggingface/evaluate/blob/vr_137/src/evaluate/evaluator.py#L339",returnDescription:`
<p>An evaluator suitable for the task.</p>
`,returnType:`
<p><a
  href="/docs/evaluate/pr_137/en/package_reference/evaluator_classes#evaluate.Evaluator"
>Evaluator</a></p>
`}}),F=new Yt({props:{anchor:"evaluate.evaluator.example",$$slots:{default:[na]},$$scope:{ctx:X}}}),H=new Ee({props:{name:"class evaluate.Evaluator",anchor:"evaluate.Evaluator",parameters:[{name:"task",val:": str"},{name:"default_metric_name",val:": str = None"}],source:"https://github.com/huggingface/evaluate/blob/vr_137/src/evaluate/evaluator.py#L50"}}),G=new Ee({props:{name:"compute",anchor:"evaluate.Evaluator.compute",parameters:[{name:"model_or_pipeline",val:": typing.Union[str, ForwardRef('Pipeline'), typing.Callable, ForwardRef('PreTrainedModel'), ForwardRef('TFPreTrainedModel')] = None"},{name:"data",val:": typing.Union[str, datasets.arrow_dataset.Dataset] = None"},{name:"metric",val:": typing.Union[str, evaluate.module.EvaluationModule] = None"},{name:"tokenizer",val:": typing.Union[str, ForwardRef('PreTrainedTokenizer'), NoneType] = None"},{name:"strategy",val:": typing.Literal['simple', 'bootstrap'] = 'simple'"},{name:"confidence_level",val:": float = 0.95"},{name:"n_resamples",val:": int = 9999"},{name:"**compute_parameters",val:": typing.Dict"}],source:"https://github.com/huggingface/evaluate/blob/vr_137/src/evaluate/evaluator.py#L108"}}),J=new Ee({props:{name:"class evaluate.TextClassificationEvaluator",anchor:"evaluate.TextClassificationEvaluator",parameters:[{name:"task",val:" = 'text-classification'"},{name:"default_metric_name",val:" = None"}],source:"https://github.com/huggingface/evaluate/blob/vr_137/src/evaluate/evaluator.py#L127"}}),K=new Ee({props:{name:"compute",anchor:"evaluate.TextClassificationEvaluator.compute",parameters:[{name:"model_or_pipeline",val:": typing.Union[str, ForwardRef('Pipeline'), typing.Callable, ForwardRef('PreTrainedModel'), ForwardRef('TFPreTrainedModel')] = None"},{name:"data",val:": typing.Union[str, datasets.arrow_dataset.Dataset] = None"},{name:"metric",val:": typing.Union[str, evaluate.module.EvaluationModule] = None"},{name:"tokenizer",val:": typing.Union[str, ForwardRef('PreTrainedTokenizer'), NoneType] = None"},{name:"strategy",val:": typing.Literal['simple', 'bootstrap'] = 'simple'"},{name:"confidence_level",val:": float = 0.95"},{name:"n_resamples",val:": int = 9999"},{name:"random_state",val:": typing.Optional[int] = None"},{name:"input_column",val:": str = 'text'"},{name:"label_column",val:": str = 'label'"},{name:"label_mapping",val:": typing.Union[typing.Dict[str, numbers.Number], NoneType] = None"},{name:"**compute_parameters",val:": typing.Dict"}],parametersDescription:[{anchor:"evaluate.TextClassificationEvaluator.compute.model_or_pipeline",description:"<strong>model_or_pipeline</strong> (<code>str</code> or <code>Pipeline</code> or <code>Callable</code> or <code>PreTrainedModel</code> or <code>TFPreTrainedModel</code>, &#x2014;",name:"model_or_pipeline"},{anchor:"evaluate.TextClassificationEvaluator.compute.defaults",description:`<strong>defaults</strong> to <code>None</code>) &#x2014;
If the argument in not specified, we initialize the default pipeline for the task (in this case
<code>text-classification</code> or its alias - <code>sentiment-analysis</code>). If the argument is of the type <code>str</code> or
is a model instance, we use it to initialize a new <code>Pipeline</code> with the given model. Otherwise we assume the
argument specifies a pre-initialized pipeline.`,name:"defaults"},{anchor:"evaluate.TextClassificationEvaluator.compute.data",description:"<strong>data</strong> (<code>str</code> or <code>Dataset</code>, defaults to <code>None) -- Specifies the dataset we will run evaluation on. If it is of type </code>str`, we treat it as the dataset\nname, and load it. Otherwise we assume it represents a pre-loaded dataset.",name:"data"},{anchor:"evaluate.TextClassificationEvaluator.compute.metric",description:`<strong>metric</strong> (<code>str</code> or <code>EvaluationModule</code>, defaults to <code>None</code>&#x201D; &#x2014;
Specifies the metric we use in evaluator. If it is of type <code>str</code>, we treat it as the metric name, and
load it. Otherwise we assume it represents a pre-loaded metric.
tokenizer &#x2014; (<code>str</code> or <code>PreTrainedTokenizer</code>, <em>optional</em>, defaults to <code>None</code>):
Argument can be used to overwrite a default tokenizer if <code>model_or_pipeline</code> represents a model for
which we build a pipeline. If <code>model_or_pipeline</code> is <code>None</code> or a pre-initialized pipeline, we ignore
this argument.
strategy &#x2014; (<code>Literal[&quot;simple&quot;, &quot;bootstrap&quot;]</code>, defaults to &#x201C;simple&#x201D;):
specifies the evaluation strategy. Possible values are:</p>
<ul>
<li><code>&quot;simple&quot;</code> - we evaluate the metric and return the scores.</li>
<li><code>&quot;bootstrap&quot;</code> - on top of computing the metric scores, we calculate the confidence interval for each
of the returned metric keys, using <code>scipy</code>&#x2019;s <code>bootstrap</code> method
<a href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.bootstrap.html" rel="nofollow">https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.bootstrap.html</a>.</li>
</ul>`,name:"metric"},{anchor:"evaluate.TextClassificationEvaluator.compute.confidence_level",description:`<strong>confidence_level</strong> (<code>float</code>, defaults to <code>0.95</code>) &#x2014;
The <code>confidence_level</code> value passed to <code>bootstrap</code> if <code>&quot;bootstrap&quot;</code> strategy is chosen.`,name:"confidence_level"},{anchor:"evaluate.TextClassificationEvaluator.compute.n_resamples",description:`<strong>n_resamples</strong> (<code>int</code>, defaults to <code>9999</code>) &#x2014;
The <code>n_resamples</code> value passed to <code>bootstrap</code> if <code>&quot;bootstrap&quot;</code> strategy is chosen.`,name:"n_resamples"},{anchor:"evaluate.TextClassificationEvaluator.compute.random_state",description:`<strong>random_state</strong> (<code>int</code>, <em>optional</em>, defaults to <code>None</code>) &#x2014;
The <code>random_state</code> value passed to <code>bootstrap</code> if <code>&quot;bootstrap&quot;</code> strategy is chosen. Useful for
debugging.`,name:"random_state"},{anchor:"evaluate.TextClassificationEvaluator.compute.input_column",description:`<strong>input_column</strong> (<code>str</code>, defaults to <code>&quot;text&quot;</code>) &#x2014;
the name of the column containing the text feature in the dataset specified by <code>data</code>.`,name:"input_column"},{anchor:"evaluate.TextClassificationEvaluator.compute.label_column",description:`<strong>label_column</strong> (<code>str</code>, defaults to <code>&quot;label&quot;</code>) &#x2014;
the name of the column containing the labels in the dataset specified by <code>data</code>.`,name:"label_column"},{anchor:"evaluate.TextClassificationEvaluator.compute.label_mapping",description:`<strong>label_mapping</strong> (<code>Dict[str, Number]</code>, <em>optional</em>, defaults to <code>None</code>) &#x2014;
We want to map class labels defined by the model in the pipeline to values consistent with those
defined in the <code>label_column</code> of the <code>data</code> dataset.`,name:"label_mapping"}],source:"https://github.com/huggingface/evaluate/blob/vr_137/src/evaluate/evaluator.py#L148",returnDescription:`
<p>A <code>Dict</code>. The keys represent metric keys calculated for the <code>metric</code> spefied in function arguments. For the
<code>"simple"</code> strategy, the value is the metric score. For the <code>"bootstrap"</code> strategy, the value is a <code>Dict</code>
containing the score, the confidence interval and the standard error calculated for each metric key.</p>
`}}),I=new Yt({props:{anchor:"evaluate.TextClassificationEvaluator.compute.example",$$slots:{default:[ra]},$$scope:{ctx:X}}}),{c(){m=s("meta"),E=p(),v=s("h1"),u=s("a"),_=s("span"),x(o.$$.fragment),h=p(),re=s("span"),Fe=r("Evaluator"),we=p(),Y=s("p"),Ue=r("The evaluator classes for automatic evaluation."),xe=p(),N=s("h2"),M=s("a"),ie=s("span"),x(R.$$.fragment),Ie=p(),ce=s("span"),Oe=r("Evaluator classes"),Te=p(),Z=s("p"),Se=r("The main entry point for using the evaluator:"),ke=p(),$=s("div"),x(B.$$.fragment),ze=p(),V=s("p"),Re=r("Utility factory method to build an "),ee=s("a"),Be=r("Evaluator"),Ve=r("."),He=p(),L=s("p"),Ge=r("Evaluators encapsulate a task and a default metric name. They leverate "),pe=s("code"),Je=r("pipeline"),We=r(" functionalify from "),de=s("code"),Ke=r("transformers"),Qe=r(`
to simplify the evaluation of multiple combinations of models, datasets and metrics for a given task.`),Xe=p(),x(F.$$.fragment),qe=p(),te=s("p"),Ye=r("The base class for all evaluator classes:"),je=p(),y=s("div"),x(H.$$.fragment),Ze=p(),ue=s("p"),et=r(`The Evaluator class is the class from which all evaluators inherit. Refer to this class for methods shared across
different evaluators.`),tt=p(),me=s("p"),at=r("Base class implementing evaluator operations."),ot=p(),U=s("div"),x(G.$$.fragment),st=p(),A=s("p"),lt=r("A core method of the "),fe=s("code"),nt=r("Evaluator"),rt=r(` class, computes the metric value for a pipeline and dataset compatible
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