import{s as Wo,c as Vo,u as Go,g as Yo,d as qo,o as Oo,n as Va}from"../chunks/scheduler.9bc65507.js";import{S as zo,i as Bo,r as f,u as g,v as h,d,t as p,w as u,g as s,m as Fo,s as r,h as o,j as v,n as So,f as n,c as a,k as $,a as c,y as t,o as Xo,A as Zo,x as i}from"../chunks/index.707bf1b6.js";import{T as Ro}from"../chunks/Tip.c2ecdbf4.js";import{D as C}from"../chunks/Docstring.17db21ae.js";import{C as Ra}from"../chunks/CodeBlock.54a9f38d.js";import{E as Po}from"../chunks/ExampleCodeBlock.4f515aa9.js";import{H as Br}from"../chunks/Heading.342b1fa6.js";function Qo(j){let m,x,b,k,M;const _=j[1].default,y=Vo(_,j,j[2],null);return{c(){m=s("p"),x=Fo("Deprecated in "),b=Fo(j[0]),k=r(),y&&y.c(),this.h()},l(L){m=o(L,"P",{class:!0});var I=v(m);x=So(I,"Deprecated in "),b=So(I,j[0]),I.forEach(n),k=a(L),y&&y.l(L),this.h()},h(){$(m,"class","font-medium")},m(L,I){c(L,m,I),t(m,x),t(m,b),c(L,k,I),y&&y.m(L,I),M=!0},p(L,I){(!M||I&1)&&Xo(b,L[0]),y&&y.p&&(!M||I&4)&&Go(y,_,L,L[2],M?qo(_,L[2],I,null):Yo(L[2]),null)},i(L){M||(d(y,L),M=!0)},o(L){p(y,L),M=!1},d(L){L&&(n(m),n(k)),y&&y.d(L)}}}function Ko(j){let m,x;return m=new Ro({props:{warning:!0,$$slots:{default:[Qo]},$$scope:{ctx:j}}}),{c(){f(m.$$.fragment)},l(b){g(m.$$.fragment,b)},m(b,k){h(m,b,k),x=!0},p(b,[k]){const M={};k&5&&(M.$$scope={dirty:k,ctx:b}),m.$set(M)},i(b){x||(d(m.$$.fragment,b),x=!0)},o(b){p(m.$$.fragment,b),x=!1},d(b){u(m,b)}}}function el(j,m,x){let{$$slots:b={},$$scope:k}=m,{version:M}=m;return j.$$set=_=>{"version"in _&&x(0,M=_.version),"$$scope"in _&&x(2,k=_.$$scope)},[M,b,k]}class tl extends zo{constructor(m){super(),Bo(this,m,el,Ko,Wo,{version:0})}}function rl(j){let m,x="Setting <code>WANDB_LOG_MODEL</code> as <code>bool</code> will be deprecated in version 5 of 🤗 Transformers.";return{c(){m=s("p"),m.innerHTML=x},l(b){m=o(b,"P",{"data-svelte-h":!0}),i(m)!=="svelte-fxlq1n"&&(m.innerHTML=x)},m(b,k){c(b,m,k)},p:Va,d(b){b&&n(m)}}}function al(j){let m,x="Example:",b,k,M;return k=new Ra({props:{code:"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",highlighted:`<span class="hljs-comment"># Note: This example skips over some setup steps for brevity.</span>
<span class="hljs-keyword">from</span> flytekit <span class="hljs-keyword">import</span> current_context, task


<span class="hljs-meta">@task</span>
<span class="hljs-keyword">def</span> <span class="hljs-title function_">train_hf_transformer</span>():
    cp = current_context().checkpoint
    trainer = Trainer(..., callbacks=[FlyteCallback()])
    output = trainer.train(resume_from_checkpoint=cp.restore())`,wrap:!1}}),{c(){m=s("p"),m.textContent=x,b=r(),f(k.$$.fragment)},l(_){m=o(_,"P",{"data-svelte-h":!0}),i(m)!=="svelte-11lpom8"&&(m.textContent=x),b=a(_),g(k.$$.fragment,_)},m(_,y){c(_,m,y),c(_,b,y),h(k,_,y),M=!0},p:Va,i(_){M||(d(k.$$.fragment,_),M=!0)},o(_){p(k.$$.fragment,_),M=!1},d(_){_&&(n(m),n(b)),u(k,_)}}}function nl(j){let m,x="Example:",b,k,M;return k=new Ra({props:{code:"Y2xhc3MlMjBQcmludGVyQ2FsbGJhY2soVHJhaW5lckNhbGxiYWNrKSUzQSUwQSUyMCUyMCUyMCUyMGRlZiUyMG9uX2xvZyhzZWxmJTJDJTIwYXJncyUyQyUyMHN0YXRlJTJDJTIwY29udHJvbCUyQyUyMGxvZ3MlM0ROb25lJTJDJTIwKiprd2FyZ3MpJTNBJTBBJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwXyUyMCUzRCUyMGxvZ3MucG9wKCUyMnRvdGFsX2Zsb3MlMjIlMkMlMjBOb25lKSUwQSUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMGlmJTIwc3RhdGUuaXNfbG9jYWxfcHJvY2Vzc196ZXJvJTNBJTBBJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwcHJpbnQobG9ncyk=",highlighted:`<span class="hljs-keyword">class</span> <span class="hljs-title class_">PrinterCallback</span>(<span class="hljs-title class_ inherited__">TrainerCallback</span>):
    <span class="hljs-keyword">def</span> <span class="hljs-title function_">on_log</span>(<span class="hljs-params">self, args, state, control, logs=<span class="hljs-literal">None</span>, **kwargs</span>):
        _ = logs.pop(<span class="hljs-string">&quot;total_flos&quot;</span>, <span class="hljs-literal">None</span>)
        <span class="hljs-keyword">if</span> state.is_local_process_zero:
            <span class="hljs-built_in">print</span>(logs)`,wrap:!1}}),{c(){m=s("p"),m.textContent=x,b=r(),f(k.$$.fragment)},l(_){m=o(_,"P",{"data-svelte-h":!0}),i(m)!=="svelte-11lpom8"&&(m.textContent=x),b=a(_),g(k.$$.fragment,_)},m(_,y){c(_,m,y),c(_,b,y),h(k,_,y),M=!0},p:Va,i(_){M||(d(k.$$.fragment,_),M=!0)},o(_){p(k.$$.fragment,_),M=!1},d(_){_&&(n(m),n(b)),u(k,_)}}}function sl(j){let m,x=`In all this class, one step is to be understood as one update step. When using gradient accumulation, one update
step may require several forward and backward passes: if you use <code>gradient_accumulation_steps=n</code>, then one update
step requires going through <em>n</em> batches.`;return{c(){m=s("p"),m.innerHTML=x},l(b){m=o(b,"P",{"data-svelte-h":!0}),i(m)!=="svelte-rhwh6p"&&(m.innerHTML=x)},m(b,k){c(b,m,k)},p:Va,d(b){b&&n(m)}}}function ol(j){let m,x,b,k,M,_,y,L=`コールバックは、PyTorch のトレーニング ループの動作をカスタマイズできるオブジェクトです。
トレーニング ループを検査できる <a href="/docs/transformers/main/ja/main_classes/trainer#transformers.Trainer">Trainer</a> (この機能は TensorFlow にはまだ実装されていません)
状態を確認し (進捗レポート、TensorBoard または他の ML プラットフォームへのログ記録など)、決定を下します (初期段階など)。
停止中）。`,I,je,ks=`コールバックは、返される <a href="/docs/transformers/main/ja/main_classes/callback#transformers.TrainerControl">TrainerControl</a> オブジェクトを除けば、「読み取り専用」のコード部分です。
トレーニング ループ内では何も変更できません。トレーニング ループの変更が必要なカスタマイズの場合は、次のことを行う必要があります。
<a href="/docs/transformers/main/ja/main_classes/trainer#transformers.Trainer">Trainer</a> をサブクラス化し、必要なメソッドをオーバーライドします (例については、<a href="trainer">trainer</a> を参照してください)。`,Rr,Ie,ws='デフォルトでは、<code>TrainingArguments.report_to</code> は <code>&quot;all&quot;</code> に設定されているため、<a href="/docs/transformers/main/ja/main_classes/trainer#transformers.Trainer">Trainer</a> は次のコールバックを使用します。',Vr,Ee,Ms=`<li><a href="/docs/transformers/main/ja/main_classes/callback#transformers.DefaultFlowCallback">DefaultFlowCallback</a> は、ログ記録、保存、評価のデフォルトの動作を処理します。</li> <li><a href="/docs/transformers/main/ja/main_classes/callback#transformers.PrinterCallback">PrinterCallback</a> または <a href="/docs/transformers/main/ja/main_classes/callback#transformers.ProgressCallback">ProgressCallback</a> で進行状況を表示し、
ログ (最初のログは、<a href="/docs/transformers/main/ja/main_classes/trainer#transformers.TrainingArguments">TrainingArguments</a> を通じて tqdm を非アクティブ化する場合に使用され、そうでない場合に使用されます)
2番目です)。</li> <li><a href="/docs/transformers/main/ja/main_classes/callback#transformers.integrations.TensorBoardCallback">TensorBoardCallback</a> (PyTorch &gt;= 1.4 を介して) tensorboard にアクセスできる場合
またはテンソルボードX）。</li> <li><a href="/docs/transformers/main/ja/main_classes/callback#transformers.integrations.WandbCallback">WandbCallback</a> <a href="https://www.wandb.com/" rel="nofollow">wandb</a> がインストールされている場合。</li> <li><a href="/docs/transformers/main/ja/main_classes/callback#transformers.integrations.CometCallback">CometCallback</a> <a href="https://www.comet.ml/site/" rel="nofollow">comet_ml</a> がインストールされている場合。</li> <li><a href="https://www.mlflow.org/" rel="nofollow">mlflow</a> がインストールされている場合は <a href="/docs/transformers/main/ja/main_classes/callback#transformers.integrations.MLflowCallback">MLflowCallback</a>。</li> <li><a href="/docs/transformers/main/ja/main_classes/callback#transformers.integrations.NeptuneCallback">NeptuneCallback</a> <a href="https://neptune.ai/" rel="nofollow">neptune</a> がインストールされている場合。</li> <li><a href="/docs/transformers/main/ja/main_classes/callback#transformers.integrations.AzureMLCallback">AzureMLCallback</a> <a href="https://pypi.org/project/azureml-sdk/" rel="nofollow">azureml-sdk</a> の場合
インストールされています。</li> <li><a href="/docs/transformers/main/ja/main_classes/callback#transformers.integrations.CodeCarbonCallback">CodeCarbonCallback</a> <a href="https://pypi.org/project/codecarbon/" rel="nofollow">codecarbon</a> の場合
インストールされています。</li> <li><a href="/docs/transformers/main/ja/main_classes/callback#transformers.integrations.ClearMLCallback">ClearMLCallback</a> <a href="https://github.com/allegroai/clearml" rel="nofollow">clearml</a> がインストールされている場合。</li> <li><a href="/docs/transformers/main/ja/main_classes/callback#transformers.integrations.DagsHubCallback">DagsHubCallback</a> <a href="https://dagshub.com/" rel="nofollow">dagshub</a> がインストールされている場合。</li> <li><a href="/docs/transformers/main/ja/main_classes/callback#transformers.integrations.FlyteCallback">FlyteCallback</a> <a href="https://flyte.org/" rel="nofollow">flyte</a> がインストールされている場合。</li> <li><a href="/docs/transformers/main/ja/main_classes/callback#transformers.integrations.DVCLiveCallback">DVCLiveCallback</a> <a href="https://www.dvc.org/doc/dvclive" rel="nofollow">dvclive</a> がインストールされている場合。</li>`,Gr,He,ys="パッケージがインストールされているが、付随する統合を使用したくない場合は、<code>TrainingArguments.report_to</code> を、使用したい統合のみのリストに変更できます (例: <code>[&quot;azure_ml&quot;, &quot;wandb&quot;]</code>) 。",Yr,Je,xs=`コールバックを実装するメインクラスは <a href="/docs/transformers/main/ja/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> です。それは、
<a href="/docs/transformers/main/ja/main_classes/trainer#transformers.TrainingArguments">TrainingArguments</a> は <a href="/docs/transformers/main/ja/main_classes/trainer#transformers.Trainer">Trainer</a> をインスタンス化するために使用され、それにアクセスできます。
<a href="/docs/transformers/main/ja/main_classes/callback#transformers.TrainerState">TrainerState</a> を介してトレーナーの内部状態を取得し、トレーニング ループ上でいくつかのアクションを実行できます。
<a href="/docs/transformers/main/ja/main_classes/callback#transformers.TrainerControl">TrainerControl</a>。`,qr,De,Or,Ue,Ls='ライブラリで利用可能な <a href="/docs/transformers/main/ja/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> のリストは次のとおりです。',Xr,P,Ae,Ga,It,js='A <a href="/docs/transformers/main/ja/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> that sends the logs to <a href="https://www.comet.ml/site/" rel="nofollow">Comet ML</a>.',Ya,H,Ne,qa,Et,Is="Setup the optional Comet.ml integration.",Oa,Ht,Es="Environment:",Xa,Jt,Hs=`<li><strong>COMET_MODE</strong> (<code>str</code>, <em>optional</em>, defaults to <code>ONLINE</code>):
Whether to create an online, offline experiment or disable Comet logging. Can be <code>OFFLINE</code>, <code>ONLINE</code>, or
<code>DISABLED</code>.</li> <li><strong>COMET_PROJECT_NAME</strong> (<code>str</code>, <em>optional</em>):
Comet project name for experiments.</li> <li><strong>COMET_OFFLINE_DIRECTORY</strong> (<code>str</code>, <em>optional</em>):
Folder to use for saving offline experiments when <code>COMET_MODE</code> is <code>OFFLINE</code>.</li> <li><strong>COMET_LOG_ASSETS</strong> (<code>str</code>, <em>optional</em>, defaults to <code>TRUE</code>):
Whether or not to log training assets (tf event logs, checkpoints, etc), to Comet. Can be <code>TRUE</code>, or
<code>FALSE</code>.</li>`,Za,Dt,Js=`For a number of configurable items in the environment, see
<a href="https://www.comet.ml/docs/python-sdk/advanced/#comet-configuration-variables" rel="nofollow">here</a>.`,Zr,X,Fe,Qa,Ut,Ds='A <a href="/docs/transformers/main/ja/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> that handles the default flow of the training loop for logs, evaluation and checkpoints.',Qr,Z,Se,Ka,At,Us='A bare <a href="/docs/transformers/main/ja/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> that just prints the logs.',Kr,Q,Pe,en,Nt,As='A <a href="/docs/transformers/main/ja/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> that displays the progress of training or evaluation.',ea,W,We,tn,Ft,Ns='A <a href="/docs/transformers/main/ja/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> that handles early stopping.',rn,St,Fs=`This callback depends on <a href="/docs/transformers/main/ja/main_classes/trainer#transformers.TrainingArguments">TrainingArguments</a> argument <em>load_best_model_at_end</em> functionality to set best_metric
in <a href="/docs/transformers/main/ja/main_classes/callback#transformers.TrainerState">TrainerState</a>. Note that if the <a href="/docs/transformers/main/ja/main_classes/trainer#transformers.TrainingArguments">TrainingArguments</a> argument <em>save_steps</em> differs from <em>eval_steps</em>, the
early stopping will not occur until the next save step.`,ta,K,ze,an,Pt,Ss='A <a href="/docs/transformers/main/ja/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> that sends the logs to <a href="https://www.tensorflow.org/tensorboard" rel="nofollow">TensorBoard</a>.',ra,z,Be,nn,Wt,Ps='A <a href="/docs/transformers/main/ja/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> that logs metrics, media, model checkpoints to <a href="https://www.wandb.com/" rel="nofollow">Weight and Biases</a>.',sn,J,Re,on,zt,Ws="Setup the optional Weights &amp; Biases (<em>wandb</em>) integration.",ln,Bt,zs=`One can subclass and override this method to customize the setup if needed. Find more information
<a href="https://docs.wandb.ai/guides/integrations/huggingface" rel="nofollow">here</a>. You can also override the following environment
variables:`,cn,Rt,Bs="Environment:",mn,B,Ve,Vt,Rs=`<strong>WANDB_LOG_MODEL</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;false&quot;</code>):
Whether to log model and checkpoints during training. Can be <code>&quot;end&quot;</code>, <code>&quot;checkpoint&quot;</code> or <code>&quot;false&quot;</code>. If set
to <code>&quot;end&quot;</code>, the model will be uploaded at the end of training. If set to <code>&quot;checkpoint&quot;</code>, the checkpoint
will be uploaded every <code>args.save_steps</code> . If set to <code>&quot;false&quot;</code>, the model will not be uploaded. Use along
with <code>load_best_model_at_end()</code> to upload best model.`,dn,ne,pn,Gt,Vs=`<p><strong>WANDB_WATCH</strong> (<code>str</code>, <em>optional</em> defaults to <code>&quot;false&quot;</code>):
Can be <code>&quot;gradients&quot;</code>, <code>&quot;all&quot;</code>, <code>&quot;parameters&quot;</code>, or <code>&quot;false&quot;</code>. Set to <code>&quot;all&quot;</code> to log gradients and
parameters.</p>`,fn,Yt,Gs=`<p><strong>WANDB_PROJECT</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;huggingface&quot;</code>):
Set this to a custom string to store results in a different project.</p>`,gn,qt,Ys=`<p><strong>WANDB_DISABLED</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>):
Whether to disable wandb entirely. Set <code>WANDB_DISABLED=true</code> to disable.</p>`,aa,R,Ge,hn,Ot,qs=`A <a href="/docs/transformers/main/ja/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> that sends the logs to <a href="https://www.mlflow.org/" rel="nofollow">MLflow</a>. Can be disabled by setting
environment variable <code>DISABLE_MLFLOW_INTEGRATION = TRUE</code>.`,un,N,Ye,bn,Xt,Os="Setup the optional MLflow integration.",_n,Zt,Xs="Environment:",vn,Qt,Zs=`<li><strong>HF_MLFLOW_LOG_ARTIFACTS</strong> (<code>str</code>, <em>optional</em>):
Whether to use MLflow <code>.log_artifact()</code> facility to log artifacts. This only makes sense if logging to a
remote server, e.g. s3 or GCS. If set to <code>True</code> or <em>1</em>, will copy each saved checkpoint on each save in
<a href="/docs/transformers/main/ja/main_classes/trainer#transformers.TrainingArguments">TrainingArguments</a>’s <code>output_dir</code> to the local or remote artifact storage. Using it without a remote
storage will just copy the files to your artifact location.</li> <li><strong>MLFLOW_TRACKING_URI</strong> (<code>str</code>, <em>optional</em>):
Whether to store runs at a specific path or remote server. Unset by default, which skips setting the
tracking URI entirely.</li> <li><strong>MLFLOW_EXPERIMENT_NAME</strong> (<code>str</code>, <em>optional</em>, defaults to <code>None</code>):
Whether to use an MLflow experiment_name under which to launch the run. Default to <code>None</code> which will point
to the <code>Default</code> experiment in MLflow. Otherwise, it is a case sensitive name of the experiment to be
activated. If an experiment with this name does not exist, a new experiment with this name is created.</li> <li><strong>MLFLOW_TAGS</strong> (<code>str</code>, <em>optional</em>):
A string dump of a dictionary of key/value pair to be added to the MLflow run as tags. Example:
<code>os.environ[&#39;MLFLOW_TAGS&#39;]=&#39;{&quot;release.candidate&quot;: &quot;RC1&quot;, &quot;release.version&quot;: &quot;2.2.0&quot;}&#39;</code>.</li> <li><strong>MLFLOW_NESTED_RUN</strong> (<code>str</code>, <em>optional</em>):
Whether to use MLflow nested runs. If set to <code>True</code> or <em>1</em>, will create a nested run inside the current
run.</li> <li><strong>MLFLOW_RUN_ID</strong> (<code>str</code>, <em>optional</em>):
Allow to reattach to an existing run which can be usefull when resuming training from a checkpoint. When
<code>MLFLOW_RUN_ID</code> environment variable is set, <code>start_run</code> attempts to resume a run with the specified run ID
and other parameters are ignored.</li> <li><strong>MLFLOW_FLATTEN_PARAMS</strong> (<code>str</code>, <em>optional</em>, defaults to <code>False</code>):
Whether to flatten the parameters dictionary before logging.</li>`,na,ee,qe,Tn,Kt,Qs='A <a href="/docs/transformers/main/ja/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> that sends the logs to <a href="https://pypi.org/project/azureml-sdk/" rel="nofollow">AzureML</a>.',sa,te,Oe,$n,er,Ks='A <a href="/docs/transformers/main/ja/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> that tracks the CO2 emission of training.',oa,V,Xe,Cn,tr,eo='TrainerCallback that sends the logs to <a href="https://app.neptune.ai" rel="nofollow">Neptune</a>.',kn,rr,to=`For instructions and examples, see the <a href="https://docs.neptune.ai/integrations/transformers" rel="nofollow">Transformers integration
guide</a> in the Neptune documentation.`,la,D,Ze,wn,ar,ro='A <a href="/docs/transformers/main/ja/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> that sends the logs to <a href="https://clear.ml/" rel="nofollow">ClearML</a>.',Mn,nr,ao="Environment:",yn,sr,no=`<li><strong>CLEARML_PROJECT</strong> (<code>str</code>, <em>optional</em>, defaults to <code>HuggingFace Transformers</code>):
ClearML project name.</li> <li><strong>CLEARML_TASK</strong> (<code>str</code>, <em>optional</em>, defaults to <code>Trainer</code>):
ClearML task name.</li> <li><strong>CLEARML_LOG_MODEL</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>):
Whether to log models as artifacts during training.</li>`,ia,G,Qe,xn,or,so='A <a href="/docs/transformers/main/ja/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> that logs to <a href="https://dagshub.com/" rel="nofollow">DagsHub</a>. Extends <code>MLflowCallback</code>',Ln,F,Ke,jn,lr,oo="Setup the DagsHub’s Logging integration.",In,ir,lo="Environment:",En,cr,io=`<li><strong>HF_DAGSHUB_LOG_ARTIFACTS</strong> (<code>str</code>, <em>optional</em>):
Whether to save the data and model artifacts for the experiment. Default to <code>False</code>.</li>`,ca,Y,et,Hn,mr,co=`A <a href="/docs/transformers/main/ja/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> that sends the logs to <a href="https://flyte.org/" rel="nofollow">Flyte</a>.
NOTE: This callback only works within a Flyte task.`,Jn,se,ma,U,tt,Dn,dr,mo='A <a href="/docs/transformers/main/ja/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> that sends the logs to <a href="https://www.dvc.org/doc/dvclive" rel="nofollow">DVCLive</a>.',Un,pr,po=`Use the environment variables below in <code>setup</code> to configure the integration. To customize this callback beyond
those environment variables, see <a href="https://dvc.org/doc/dvclive/ml-frameworks/huggingface" rel="nofollow">here</a>.`,An,S,rt,Nn,fr,fo=`Setup the optional DVCLive integration. To customize this callback beyond the environment variables below, see
<a href="https://dvc.org/doc/dvclive/ml-frameworks/huggingface" rel="nofollow">here</a>.`,Fn,gr,go="Environment:",Sn,hr,ho=`<li><strong>HF_DVCLIVE_LOG_MODEL</strong> (<code>str</code>, <em>optional</em>):
Whether to use <code>dvclive.Live.log_artifact()</code> to log checkpoints created by <a href="/docs/transformers/main/ja/main_classes/trainer#transformers.Trainer">Trainer</a>. If set to <code>True</code> or
<em>1</em>, the final checkpoint is logged at the end of training. If set to <code>all</code>, the entire
<a href="/docs/transformers/main/ja/main_classes/trainer#transformers.TrainingArguments">TrainingArguments</a>’s <code>output_dir</code> is logged at each checkpoint.</li>`,da,at,pa,T,nt,Pn,ur,uo=`A class for objects that will inspect the state of the training loop at some events and take some decisions. At
each of those events the following arguments are available:`,Wn,br,bo=`The <code>control</code> object is the only one that can be changed by the callback, in which case the event that changes it
should return the modified version.`,zn,_r,_o=`The argument <code>args</code>, <code>state</code> and <code>control</code> are positionals for all events, all the others are grouped in <code>kwargs</code>.
You can unpack the ones you need in the signature of the event using them. As an example, see the code of the
simple <a href="/docs/transformers/main/ja/main_classes/callback#transformers.PrinterCallback">PrinterCallback</a>.`,Bn,oe,Rn,le,st,Vn,vr,vo="Event called at the beginning of an epoch.",Gn,ie,ot,Yn,Tr,To="Event called at the end of an epoch.",qn,ce,lt,On,$r,$o="Event called after an evaluation phase.",Xn,me,it,Zn,Cr,Co='Event called at the end of the initialization of the <a href="/docs/transformers/main/ja/main_classes/trainer#transformers.Trainer">Trainer</a>.',Qn,de,ct,Kn,kr,ko="Event called after logging the last logs.",es,pe,mt,ts,wr,wo="Event called after a successful prediction.",rs,fe,dt,as,Mr,Mo="Event called after a prediction step.",ns,ge,pt,ss,yr,yo="Event called after a checkpoint save.",os,he,ft,ls,xr,xo=`Event called at the beginning of a training step. If using gradient accumulation, one training step might take
several inputs.`,is,ue,gt,cs,Lr,Lo=`Event called at the end of a training step. If using gradient accumulation, one training step might take
several inputs.`,ms,be,ht,ds,jr,jo="Event called at the end of an substep during gradient accumulation.",ps,_e,ut,fs,Ir,Io="Event called at the beginning of training.",gs,ve,bt,hs,Er,Eo="Event called at the end of training.",fa,_t,Ho='以下は、カスタム コールバックを PyTorch <a href="/docs/transformers/main/ja/main_classes/trainer#transformers.Trainer">Trainer</a> に登録する方法の例です。',ga,vt,ha,Tt,Jo="コールバックを登録する別の方法は、次のように <code>trainer.add_callback()</code> を呼び出すことです。",ua,$t,ba,Ct,_a,E,kt,us,Hr,Do=`A class containing the <a href="/docs/transformers/main/ja/main_classes/trainer#transformers.Trainer">Trainer</a> inner state that will be saved along the model and optimizer when checkpointing
and passed to the <a href="/docs/transformers/main/ja/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a>.`,bs,Te,_s,$e,wt,vs,Jr,Uo="Create an instance from the content of <code>json_path</code>.",Ts,Ce,Mt,$s,Dr,Ao="Save the content of this instance in JSON format inside <code>json_path</code>.",va,yt,Ta,re,xt,Cs,Ur,No=`A class that handles the <a href="/docs/transformers/main/ja/main_classes/trainer#transformers.Trainer">Trainer</a> control flow. This class is used by the <a href="/docs/transformers/main/ja/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> to activate some
switches in the training loop.`,$a,zr,Ca;return M=new Br({props:{title:"コールバック数",local:"コールバック数",headingTag:"h1"}}),De=new Br({props:{title:"利用可能なコールバック",local:"transformers.integrations.CometCallback",headingTag:"h2"}}),Ae=new C({props:{name:"class transformers.integrations.CometCallback",anchor:"transformers.integrations.CometCallback",parameters:[],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/integrations/integration_utils.py#L846"}}),Ne=new C({props:{name:"setup",anchor:"transformers.integrations.CometCallback.setup",parameters:[{name:"args",val:""},{name:"state",val:""},{name:"model",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/integrations/integration_utils.py#L857"}}),Fe=new C({props:{name:"class transformers.DefaultFlowCallback",anchor:"transformers.DefaultFlowCallback",parameters:[],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/trainer_callback.py#L432"}}),Se=new C({props:{name:"class transformers.PrinterCallback",anchor:"transformers.PrinterCallback",parameters:[],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/trainer_callback.py#L532"}}),Pe=new C({props:{name:"class transformers.ProgressCallback",anchor:"transformers.ProgressCallback",parameters:[],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/trainer_callback.py#L482"}}),We=new C({props:{name:"class transformers.EarlyStoppingCallback",anchor:"transformers.EarlyStoppingCallback",parameters:[{name:"early_stopping_patience",val:": int = 1"},{name:"early_stopping_threshold",val:": Optional = 0.0"}],parametersDescription:[{anchor:"transformers.EarlyStoppingCallback.early_stopping_patience",description:`<strong>early_stopping_patience</strong> (<code>int</code>) &#x2014;
Use with <code>metric_for_best_model</code> to stop training when the specified metric worsens for
<code>early_stopping_patience</code> evaluation calls.`,name:"early_stopping_patience"},{anchor:"transformers.EarlyStoppingCallback.early_stopping_threshold(float,",description:`<strong>early_stopping_threshold(<code>float</code>,</strong> <em>optional</em>) &#x2014;
Use with TrainingArguments <code>metric_for_best_model</code> and <code>early_stopping_patience</code> to denote how much the
specified metric must improve to satisfy early stopping conditions. \``,name:"early_stopping_threshold(float,"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/trainer_callback.py#L543"}}),ze=new C({props:{name:"class transformers.integrations.TensorBoardCallback",anchor:"transformers.integrations.TensorBoardCallback",parameters:[{name:"tb_writer",val:" = None"}],parametersDescription:[{anchor:"transformers.integrations.TensorBoardCallback.tb_writer",description:`<strong>tb_writer</strong> (<code>SummaryWriter</code>, <em>optional</em>) &#x2014;
The writer to use. Will instantiate one if not set.`,name:"tb_writer"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/integrations/integration_utils.py#L580"}}),Be=new C({props:{name:"class transformers.integrations.WandbCallback",anchor:"transformers.integrations.WandbCallback",parameters:[],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/integrations/integration_utils.py#L666"}}),Re=new C({props:{name:"setup",anchor:"transformers.integrations.WandbCallback.setup",parameters:[{name:"args",val:""},{name:"state",val:""},{name:"model",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/integrations/integration_utils.py#L691"}}),ne=new tl({props:{version:"5.0",$$slots:{default:[rl]},$$scope:{ctx:j}}}),Ge=new C({props:{name:"class transformers.integrations.MLflowCallback",anchor:"transformers.integrations.MLflowCallback",parameters:[],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/integrations/integration_utils.py#L946"}}),Ye=new C({props:{name:"setup",anchor:"transformers.integrations.MLflowCallback.setup",parameters:[{name:"args",val:""},{name:"state",val:""},{name:"model",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/integrations/integration_utils.py#L965"}}),qe=new C({props:{name:"class transformers.integrations.AzureMLCallback",anchor:"transformers.integrations.AzureMLCallback",parameters:[{name:"azureml_run",val:" = None"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/integrations/integration_utils.py#L923"}}),Oe=new C({props:{name:"class transformers.integrations.CodeCarbonCallback",anchor:"transformers.integrations.CodeCarbonCallback",parameters:[],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/integrations/integration_utils.py#L1445"}}),Xe=new C({props:{name:"class transformers.integrations.NeptuneCallback",anchor:"transformers.integrations.NeptuneCallback",parameters:[{name:"api_token",val:": Optional = None"},{name:"project",val:": Optional = None"},{name:"name",val:": Optional = None"},{name:"base_namespace",val:": str = 'finetuning'"},{name:"run",val:" = None"},{name:"log_parameters",val:": bool = True"},{name:"log_checkpoints",val:": Optional = None"},{name:"**neptune_run_kwargs",val:""}],parametersDescription:[{anchor:"transformers.integrations.NeptuneCallback.api_token",description:`<strong>api_token</strong> (<code>str</code>, <em>optional</em>) &#x2014; Neptune API token obtained upon registration.
You can leave this argument out if you have saved your token to the <code>NEPTUNE_API_TOKEN</code> environment
variable (strongly recommended). See full setup instructions in the
<a href="https://docs.neptune.ai/setup/installation" rel="nofollow">docs</a>.`,name:"api_token"},{anchor:"transformers.integrations.NeptuneCallback.project",description:`<strong>project</strong> (<code>str</code>, <em>optional</em>) &#x2014; Name of an existing Neptune project, in the form &#x201C;workspace-name/project-name&#x201D;.
You can find and copy the name in Neptune from the project settings -&gt; Properties. If None (default), the
value of the <code>NEPTUNE_PROJECT</code> environment variable is used.`,name:"project"},{anchor:"transformers.integrations.NeptuneCallback.name",description:"<strong>name</strong> (<code>str</code>, <em>optional</em>) &#x2014; Custom name for the run.",name:"name"},{anchor:"transformers.integrations.NeptuneCallback.base_namespace",description:`<strong>base_namespace</strong> (<code>str</code>, optional, defaults to &#x201C;finetuning&#x201D;) &#x2014; In the Neptune run, the root namespace
that will contain all of the metadata logged by the callback.`,name:"base_namespace"},{anchor:"transformers.integrations.NeptuneCallback.log_parameters",description:`<strong>log_parameters</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
If True, logs all Trainer arguments and model parameters provided by the Trainer.`,name:"log_parameters"},{anchor:"transformers.integrations.NeptuneCallback.log_checkpoints",description:`<strong>log_checkpoints</strong> (<code>str</code>, <em>optional</em>) &#x2014; If &#x201C;same&#x201D;, uploads checkpoints whenever they are saved by the Trainer.
If &#x201C;last&#x201D;, uploads only the most recently saved checkpoint. If &#x201C;best&#x201D;, uploads the best checkpoint (among
the ones saved by the Trainer). If <code>None</code>, does not upload checkpoints.`,name:"log_checkpoints"},{anchor:"transformers.integrations.NeptuneCallback.run",description:`<strong>run</strong> (<code>Run</code>, <em>optional</em>) &#x2014; Pass a Neptune run object if you want to continue logging to an existing run.
Read more about resuming runs in the <a href="https://docs.neptune.ai/logging/to_existing_object" rel="nofollow">docs</a>.`,name:"run"},{anchor:"transformers.integrations.NeptuneCallback.*neptune_run_kwargs",description:`*<strong>*neptune_run_kwargs</strong> (<em>optional</em>) &#x2014;
Additional keyword arguments to be passed directly to the
<a href="https://docs.neptune.ai/api/neptune#init_run" rel="nofollow"><code>neptune.init_run()</code></a> function when a new run is created.`,name:"*neptune_run_kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/integrations/integration_utils.py#L1172"}}),Ze=new C({props:{name:"class transformers.integrations.ClearMLCallback",anchor:"transformers.integrations.ClearMLCallback",parameters:[],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/integrations/integration_utils.py#L1474"}}),Qe=new C({props:{name:"class transformers.integrations.DagsHubCallback",anchor:"transformers.integrations.DagsHubCallback",parameters:[],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/integrations/integration_utils.py#L1112"}}),Ke=new C({props:{name:"setup",anchor:"transformers.integrations.DagsHubCallback.setup",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/integrations/integration_utils.py#L1126"}}),et=new C({props:{name:"class transformers.integrations.FlyteCallback",anchor:"transformers.integrations.FlyteCallback",parameters:[{name:"save_log_history",val:": bool = True"},{name:"sync_checkpoints",val:": bool = True"}],parametersDescription:[{anchor:"transformers.integrations.FlyteCallback.save_log_history",description:`<strong>save_log_history</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
When set to True, the training logs are saved as a Flyte Deck.`,name:"save_log_history"},{anchor:"transformers.integrations.FlyteCallback.sync_checkpoints",description:`<strong>sync_checkpoints</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
When set to True, checkpoints are synced with Flyte and can be used to resume training in the case of an
interruption.`,name:"sync_checkpoints"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/integrations/integration_utils.py#L1727"}}),se=new Po({props:{anchor:"transformers.integrations.FlyteCallback.example",$$slots:{default:[al]},$$scope:{ctx:j}}}),tt=new C({props:{name:"class transformers.integrations.DVCLiveCallback",anchor:"transformers.integrations.DVCLiveCallback",parameters:[{name:"live",val:": Optional = None"},{name:"log_model",val:": Union = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.integrations.DVCLiveCallback.live",description:`<strong>live</strong> (<code>dvclive.Live</code>, <em>optional</em>, defaults to <code>None</code>) &#x2014;
Optional Live instance. If None, a new instance will be created using **kwargs.`,name:"live"},{anchor:"transformers.integrations.DVCLiveCallback.log_model",description:`<strong>log_model</strong> (Union[Literal[&#x201C;all&#x201D;], bool], <em>optional</em>, defaults to <code>None</code>) &#x2014;
Whether to use <code>dvclive.Live.log_artifact()</code> to log checkpoints created by <a href="/docs/transformers/main/ja/main_classes/trainer#transformers.Trainer">Trainer</a>. If set to <code>True</code>,
the final checkpoint is logged at the end of training. If set to <code>&quot;all&quot;</code>, the entire
<a href="/docs/transformers/main/ja/main_classes/trainer#transformers.TrainingArguments">TrainingArguments</a>&#x2019;s <code>output_dir</code> is logged at each checkpoint.`,name:"log_model"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/integrations/integration_utils.py#L1790"}}),rt=new C({props:{name:"setup",anchor:"transformers.integrations.DVCLiveCallback.setup",parameters:[{name:"args",val:""},{name:"state",val:""},{name:"model",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/integrations/integration_utils.py#L1831"}}),at=new Br({props:{title:"TrainerCallback",local:"transformers.TrainerCallback",headingTag:"h2"}}),nt=new C({props:{name:"class transformers.TrainerCallback",anchor:"transformers.TrainerCallback",parameters:[],parametersDescription:[{anchor:"transformers.TrainerCallback.args",description:`<strong>args</strong> (<a href="/docs/transformers/main/ja/main_classes/trainer#transformers.TrainingArguments">TrainingArguments</a>) &#x2014;
The training arguments used to instantiate the <a href="/docs/transformers/main/ja/main_classes/trainer#transformers.Trainer">Trainer</a>.`,name:"args"},{anchor:"transformers.TrainerCallback.state",description:`<strong>state</strong> (<a href="/docs/transformers/main/ja/main_classes/callback#transformers.TrainerState">TrainerState</a>) &#x2014;
The current state of the <a href="/docs/transformers/main/ja/main_classes/trainer#transformers.Trainer">Trainer</a>.`,name:"state"},{anchor:"transformers.TrainerCallback.control",description:`<strong>control</strong> (<a href="/docs/transformers/main/ja/main_classes/callback#transformers.TrainerControl">TrainerControl</a>) &#x2014;
The object that is returned to the <a href="/docs/transformers/main/ja/main_classes/trainer#transformers.Trainer">Trainer</a> and can be used to make some decisions.`,name:"control"},{anchor:"transformers.TrainerCallback.model",description:`<strong>model</strong> (<a href="/docs/transformers/main/ja/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> or <code>torch.nn.Module</code>) &#x2014;
The model being trained.`,name:"model"},{anchor:"transformers.TrainerCallback.tokenizer",description:`<strong>tokenizer</strong> (<a href="/docs/transformers/main/ja/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>) &#x2014;
The tokenizer used for encoding the data.`,name:"tokenizer"},{anchor:"transformers.TrainerCallback.optimizer",description:`<strong>optimizer</strong> (<code>torch.optim.Optimizer</code>) &#x2014;
The optimizer used for the training steps.`,name:"optimizer"},{anchor:"transformers.TrainerCallback.lr_scheduler",description:`<strong>lr_scheduler</strong> (<code>torch.optim.lr_scheduler.LambdaLR</code>) &#x2014;
The scheduler used for setting the learning rate.`,name:"lr_scheduler"},{anchor:"transformers.TrainerCallback.train_dataloader",description:`<strong>train_dataloader</strong> (<code>torch.utils.data.DataLoader</code>, <em>optional</em>) &#x2014;
The current dataloader used for training.`,name:"train_dataloader"},{anchor:"transformers.TrainerCallback.eval_dataloader",description:`<strong>eval_dataloader</strong> (<code>torch.utils.data.DataLoader</code>, <em>optional</em>) &#x2014;
The current dataloader used for training.`,name:"eval_dataloader"},{anchor:"transformers.TrainerCallback.metrics",description:`<strong>metrics</strong> (<code>Dict[str, float]</code>) &#x2014;
The metrics computed by the last evaluation phase.</p>
<p>Those are only accessible in the event <code>on_evaluate</code>.`,name:"metrics"},{anchor:"transformers.TrainerCallback.logs",description:`<strong>logs</strong>  (<code>Dict[str, float]</code>) &#x2014;
The values to log.</p>
<p>Those are only accessible in the event <code>on_log</code>.`,name:"logs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/trainer_callback.py#L175"}}),oe=new Po({props:{anchor:"transformers.TrainerCallback.example",$$slots:{default:[nl]},$$scope:{ctx:j}}}),st=new C({props:{name:"on_epoch_begin",anchor:"transformers.TrainerCallback.on_epoch_begin",parameters:[{name:"args",val:": TrainingArguments"},{name:"state",val:": TrainerState"},{name:"control",val:": TrainerControl"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/trainer_callback.py#L244"}}),ot=new C({props:{name:"on_epoch_end",anchor:"transformers.TrainerCallback.on_epoch_end",parameters:[{name:"args",val:": TrainingArguments"},{name:"state",val:": TrainerState"},{name:"control",val:": TrainerControl"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/trainer_callback.py#L250"}}),lt=new C({props:{name:"on_evaluate",anchor:"transformers.TrainerCallback.on_evaluate",parameters:[{name:"args",val:": TrainingArguments"},{name:"state",val:": TrainerState"},{name:"control",val:": TrainerControl"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/trainer_callback.py#L276"}}),it=new C({props:{name:"on_init_end",anchor:"transformers.TrainerCallback.on_init_end",parameters:[{name:"args",val:": TrainingArguments"},{name:"state",val:": TrainerState"},{name:"control",val:": TrainerControl"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/trainer_callback.py#L226"}}),ct=new C({props:{name:"on_log",anchor:"transformers.TrainerCallback.on_log",parameters:[{name:"args",val:": TrainingArguments"},{name:"state",val:": TrainerState"},{name:"control",val:": TrainerControl"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/trainer_callback.py#L294"}}),mt=new C({props:{name:"on_predict",anchor:"transformers.TrainerCallback.on_predict",parameters:[{name:"args",val:": TrainingArguments"},{name:"state",val:": TrainerState"},{name:"control",val:": TrainerControl"},{name:"metrics",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/trainer_callback.py#L282"}}),dt=new C({props:{name:"on_prediction_step",anchor:"transformers.TrainerCallback.on_prediction_step",parameters:[{name:"args",val:": TrainingArguments"},{name:"state",val:": TrainerState"},{name:"control",val:": TrainerControl"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/trainer_callback.py#L300"}}),pt=new C({props:{name:"on_save",anchor:"transformers.TrainerCallback.on_save",parameters:[{name:"args",val:": TrainingArguments"},{name:"state",val:": TrainerState"},{name:"control",val:": TrainerControl"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/trainer_callback.py#L288"}}),ft=new C({props:{name:"on_step_begin",anchor:"transformers.TrainerCallback.on_step_begin",parameters:[{name:"args",val:": TrainingArguments"},{name:"state",val:": TrainerState"},{name:"control",val:": TrainerControl"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/trainer_callback.py#L256"}}),gt=new C({props:{name:"on_step_end",anchor:"transformers.TrainerCallback.on_step_end",parameters:[{name:"args",val:": TrainingArguments"},{name:"state",val:": TrainerState"},{name:"control",val:": TrainerControl"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/trainer_callback.py#L269"}}),ht=new C({props:{name:"on_substep_end",anchor:"transformers.TrainerCallback.on_substep_end",parameters:[{name:"args",val:": TrainingArguments"},{name:"state",val:": TrainerState"},{name:"control",val:": TrainerControl"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/trainer_callback.py#L263"}}),ut=new C({props:{name:"on_train_begin",anchor:"transformers.TrainerCallback.on_train_begin",parameters:[{name:"args",val:": TrainingArguments"},{name:"state",val:": TrainerState"},{name:"control",val:": TrainerControl"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/trainer_callback.py#L232"}}),bt=new C({props:{name:"on_train_end",anchor:"transformers.TrainerCallback.on_train_end",parameters:[{name:"args",val:": TrainingArguments"},{name:"state",val:": TrainerState"},{name:"control",val:": TrainerControl"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/trainer_callback.py#L238"}}),vt=new Ra({props:{code:"Y2xhc3MlMjBNeUNhbGxiYWNrKFRyYWluZXJDYWxsYmFjayklM0ElMEElMjAlMjAlMjAlMjAlMjJBJTIwY2FsbGJhY2slMjB0aGF0JTIwcHJpbnRzJTIwYSUyMG1lc3NhZ2UlMjBhdCUyMHRoZSUyMGJlZ2lubmluZyUyMG9mJTIwdHJhaW5pbmclMjIlMEElMEElMjAlMjAlMjAlMjBkZWYlMjBvbl90cmFpbl9iZWdpbihzZWxmJTJDJTIwYXJncyUyQyUyMHN0YXRlJTJDJTIwY29udHJvbCUyQyUyMCoqa3dhcmdzKSUzQSUwQSUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMHByaW50KCUyMlN0YXJ0aW5nJTIwdHJhaW5pbmclMjIpJTBBJTBBJTBBdHJhaW5lciUyMCUzRCUyMFRyYWluZXIoJTBBJTIwJTIwJTIwJTIwbW9kZWwlMkMlMEElMjAlMjAlMjAlMjBhcmdzJTJDJTBBJTIwJTIwJTIwJTIwdHJhaW5fZGF0YXNldCUzRHRyYWluX2RhdGFzZXQlMkMlMEElMjAlMjAlMjAlMjBldmFsX2RhdGFzZXQlM0RldmFsX2RhdGFzZXQlMkMlMEElMjAlMjAlMjAlMjBjYWxsYmFja3MlM0QlNUJNeUNhbGxiYWNrJTVEJTJDJTIwJTIwJTIzJTIwV2UlMjBjYW4lMjBlaXRoZXIlMjBwYXNzJTIwdGhlJTIwY2FsbGJhY2slMjBjbGFzcyUyMHRoaXMlMjB3YXklMjBvciUyMGFuJTIwaW5zdGFuY2UlMjBvZiUyMGl0JTIwKE15Q2FsbGJhY2soKSklMEEp",highlighted:`<span class="hljs-keyword">class</span> <span class="hljs-title class_">MyCallback</span>(<span class="hljs-title class_ inherited__">TrainerCallback</span>):
    <span class="hljs-string">&quot;A callback that prints a message at the beginning of training&quot;</span>

    <span class="hljs-keyword">def</span> <span class="hljs-title function_">on_train_begin</span>(<span class="hljs-params">self, args, state, control, **kwargs</span>):
        <span class="hljs-built_in">print</span>(<span class="hljs-string">&quot;Starting training&quot;</span>)


trainer = Trainer(
    model,
    args,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    callbacks=[MyCallback],  <span class="hljs-comment"># We can either pass the callback class this way or an instance of it (MyCallback())</span>
)`,wrap:!1}}),$t=new Ra({props:{code:"dHJhaW5lciUyMCUzRCUyMFRyYWluZXIoLi4uKSUwQXRyYWluZXIuYWRkX2NhbGxiYWNrKE15Q2FsbGJhY2spJTBBJTIzJTIwQWx0ZXJuYXRpdmVseSUyQyUyMHdlJTIwY2FuJTIwcGFzcyUyMGFuJTIwaW5zdGFuY2UlMjBvZiUyMHRoZSUyMGNhbGxiYWNrJTIwY2xhc3MlMEF0cmFpbmVyLmFkZF9jYWxsYmFjayhNeUNhbGxiYWNrKCkp",highlighted:`trainer = Trainer(...)
trainer.add_callback(MyCallback)
<span class="hljs-comment"># Alternatively, we can pass an instance of the callback class</span>
trainer.add_callback(MyCallback())`,wrap:!1}}),Ct=new Br({props:{title:"TrainerState",local:"transformers.TrainerState",headingTag:"h2"}}),kt=new C({props:{name:"class transformers.TrainerState",anchor:"transformers.TrainerState",parameters:[{name:"epoch",val:": Optional = None"},{name:"global_step",val:": int = 0"},{name:"max_steps",val:": int = 0"},{name:"logging_steps",val:": int = 500"},{name:"eval_steps",val:": int = 500"},{name:"save_steps",val:": int = 500"},{name:"train_batch_size",val:": int = None"},{name:"num_train_epochs",val:": int = 0"},{name:"num_input_tokens_seen",val:": int = 0"},{name:"total_flos",val:": float = 0"},{name:"log_history",val:": List = None"},{name:"best_metric",val:": Optional = None"},{name:"best_model_checkpoint",val:": Optional = None"},{name:"is_local_process_zero",val:": bool = True"},{name:"is_world_process_zero",val:": bool = True"},{name:"is_hyper_param_search",val:": bool = False"},{name:"trial_name",val:": str = None"},{name:"trial_params",val:": Dict = None"}],parametersDescription:[{anchor:"transformers.TrainerState.epoch",description:`<strong>epoch</strong> (<code>float</code>, <em>optional</em>) &#x2014;
Only set during training, will represent the epoch the training is at (the decimal part being the
percentage of the current epoch completed).`,name:"epoch"},{anchor:"transformers.TrainerState.global_step",description:`<strong>global_step</strong> (<code>int</code>, <em>optional</em>, defaults to 0) &#x2014;
During training, represents the number of update steps completed.`,name:"global_step"},{anchor:"transformers.TrainerState.max_steps",description:`<strong>max_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 0) &#x2014;
The number of update steps to do during the current training.`,name:"max_steps"},{anchor:"transformers.TrainerState.logging_steps",description:`<strong>logging_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 500) &#x2014;
Log every X updates steps`,name:"logging_steps"},{anchor:"transformers.TrainerState.eval_steps",description:`<strong>eval_steps</strong> (<code>int</code>, <em>optional</em>) &#x2014;
Run an evaluation every X steps.`,name:"eval_steps"},{anchor:"transformers.TrainerState.save_steps",description:`<strong>save_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 500) &#x2014;
Save checkpoint every X updates steps.`,name:"save_steps"},{anchor:"transformers.TrainerState.train_batch_size",description:`<strong>train_batch_size</strong> (<code>int</code>, <em>optional</em>) &#x2014;
The batch size for the training dataloader. Only needed when
<code>auto_find_batch_size</code> has been used.`,name:"train_batch_size"},{anchor:"transformers.TrainerState.num_input_tokens_seen",description:`<strong>num_input_tokens_seen</strong> (<code>int</code>, <em>optional</em>, defaults to 0) &#x2014;
The number of tokens seen during training (number of input tokens, not the number of prediction tokens).`,name:"num_input_tokens_seen"},{anchor:"transformers.TrainerState.total_flos",description:`<strong>total_flos</strong> (<code>float</code>, <em>optional</em>, defaults to 0) &#x2014;
The total number of floating operations done by the model since the beginning of training (stored as floats
to avoid overflow).`,name:"total_flos"},{anchor:"transformers.TrainerState.log_history",description:`<strong>log_history</strong> (<code>List[Dict[str, float]]</code>, <em>optional</em>) &#x2014;
The list of logs done since the beginning of training.`,name:"log_history"},{anchor:"transformers.TrainerState.best_metric",description:`<strong>best_metric</strong> (<code>float</code>, <em>optional</em>) &#x2014;
When tracking the best model, the value of the best metric encountered so far.`,name:"best_metric"},{anchor:"transformers.TrainerState.best_model_checkpoint",description:`<strong>best_model_checkpoint</strong> (<code>str</code>, <em>optional</em>) &#x2014;
When tracking the best model, the value of the name of the checkpoint for the best model encountered so
far.`,name:"best_model_checkpoint"},{anchor:"transformers.TrainerState.is_local_process_zero",description:`<strong>is_local_process_zero</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not this process is the local (e.g., on one machine if training in a distributed fashion on
several machines) main process.`,name:"is_local_process_zero"},{anchor:"transformers.TrainerState.is_world_process_zero",description:`<strong>is_world_process_zero</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not this process is the global main process (when training in a distributed fashion on several
machines, this is only going to be <code>True</code> for one process).`,name:"is_world_process_zero"},{anchor:"transformers.TrainerState.is_hyper_param_search",description:`<strong>is_hyper_param_search</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether we are in the process of a hyper parameter search using Trainer.hyperparameter_search. This will
impact the way data will be logged in TensorBoard.`,name:"is_hyper_param_search"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/trainer_callback.py#L34"}}),Te=new Ro({props:{$$slots:{default:[sl]},$$scope:{ctx:j}}}),wt=new C({props:{name:"load_from_json",anchor:"transformers.TrainerState.load_from_json",parameters:[{name:"json_path",val:": str"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/trainer_callback.py#L117"}}),Mt=new C({props:{name:"save_to_json",anchor:"transformers.TrainerState.save_to_json",parameters:[{name:"json_path",val:": str"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/trainer_callback.py#L111"}}),yt=new Br({props:{title:"TrainerControl",local:"transformers.TrainerControl",headingTag:"h2"}}),xt=new C({props:{name:"class transformers.TrainerControl",anchor:"transformers.TrainerControl",parameters:[{name:"should_training_stop",val:": bool = False"},{name:"should_epoch_stop",val:": bool = False"},{name:"should_save",val:": bool = False"},{name:"should_evaluate",val:": bool = False"},{name:"should_log",val:": bool = False"}],parametersDescription:[{anchor:"transformers.TrainerControl.should_training_stop",description:`<strong>should_training_stop</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not the training should be interrupted.</p>
<p>If <code>True</code>, this variable will not be set back to <code>False</code>. The training will just stop.`,name:"should_training_stop"},{anchor:"transformers.TrainerControl.should_epoch_stop",description:`<strong>should_epoch_stop</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not the current epoch should be interrupted.</p>
<p>If <code>True</code>, this variable will be set back to <code>False</code> at the beginning of the next epoch.`,name:"should_epoch_stop"},{anchor:"transformers.TrainerControl.should_save",description:`<strong>should_save</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not the model should be saved at this step.</p>
<p>If <code>True</code>, this variable will be set back to <code>False</code> at the beginning of the next step.`,name:"should_save"},{anchor:"transformers.TrainerControl.should_evaluate",description:`<strong>should_evaluate</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not the model should be evaluated at this step.</p>
<p>If <code>True</code>, this variable will be set back to <code>False</code> at the beginning of the next step.`,name:"should_evaluate"},{anchor:"transformers.TrainerControl.should_log",description:`<strong>should_log</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not the logs should be reported at this step.</p>
<p>If <code>True</code>, this variable will be set back to <code>False</code> at the beginning of the next step.`,name:"should_log"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/trainer_callback.py#L125"}}),{c(){m=s("meta"),x=r(),b=s("p"),k=r(),f(M.$$.fragment),_=r(),y=s("p"),y.innerHTML=L,I=r(),je=s("p"),je.innerHTML=ks,Rr=r(),Ie=s("p"),Ie.innerHTML=ws,Vr=r(),Ee=s("ul"),Ee.innerHTML=Ms,Gr=r(),He=s("p"),He.innerHTML=ys,Yr=r(),Je=s("p"),Je.innerHTML=xs,qr=r(),f(De.$$.fragment),Or=r(),Ue=s("p"),Ue.innerHTML=Ls,Xr=r(),P=s("div"),f(Ae.$$.fragment),Ga=r(),It=s("p"),It.innerHTML=js,Ya=r(),H=s("div"),f(Ne.$$.fragment),qa=r(),Et=s("p"),Et.textContent=Is,Oa=r(),Ht=s("p"),Ht.textContent=Es,Xa=r(),Jt=s("ul"),Jt.innerHTML=Hs,Za=r(),Dt=s("p"),Dt.innerHTML=Js,Zr=r(),X=s("div"),f(Fe.$$.fragment),Qa=r(),Ut=s("p"),Ut.innerHTML=Ds,Qr=r(),Z=s("div"),f(Se.$$.fragment),Ka=r(),At=s("p"),At.innerHTML=Us,Kr=r(),Q=s("div"),f(Pe.$$.fragment),en=r(),Nt=s("p"),Nt.innerHTML=As,ea=r(),W=s("div"),f(We.$$.fragment),tn=r(),Ft=s("p"),Ft.innerHTML=Ns,rn=r(),St=s("p"),St.innerHTML=Fs,ta=r(),K=s("div"),f(ze.$$.fragment),an=r(),Pt=s("p"),Pt.innerHTML=Ss,ra=r(),z=s("div"),f(Be.$$.fragment),nn=r(),Wt=s("p"),Wt.innerHTML=Ps,sn=r(),J=s("div"),f(Re.$$.fragment),on=r(),zt=s("p"),zt.innerHTML=Ws,ln=r(),Bt=s("p"),Bt.innerHTML=zs,cn=r(),Rt=s("p"),Rt.textContent=Bs,mn=r(),B=s("ul"),Ve=s("li"),Vt=s("p"),Vt.innerHTML=Rs,dn=r(),f(ne.$$.fragment),pn=r(),Gt=s("li"),Gt.innerHTML=Vs,fn=r(),Yt=s("li"),Yt.innerHTML=Gs,gn=r(),qt=s("li"),qt.innerHTML=Ys,aa=r(),R=s("div"),f(Ge.$$.fragment),hn=r(),Ot=s("p"),Ot.innerHTML=qs,un=r(),N=s("div"),f(Ye.$$.fragment),bn=r(),Xt=s("p"),Xt.textContent=Os,_n=r(),Zt=s("p"),Zt.textContent=Xs,vn=r(),Qt=s("ul"),Qt.innerHTML=Zs,na=r(),ee=s("div"),f(qe.$$.fragment),Tn=r(),Kt=s("p"),Kt.innerHTML=Qs,sa=r(),te=s("div"),f(Oe.$$.fragment),$n=r(),er=s("p"),er.innerHTML=Ks,oa=r(),V=s("div"),f(Xe.$$.fragment),Cn=r(),tr=s("p"),tr.innerHTML=eo,kn=r(),rr=s("p"),rr.innerHTML=to,la=r(),D=s("div"),f(Ze.$$.fragment),wn=r(),ar=s("p"),ar.innerHTML=ro,Mn=r(),nr=s("p"),nr.textContent=ao,yn=r(),sr=s("ul"),sr.innerHTML=no,ia=r(),G=s("div"),f(Qe.$$.fragment),xn=r(),or=s("p"),or.innerHTML=so,Ln=r(),F=s("div"),f(Ke.$$.fragment),jn=r(),lr=s("p"),lr.textContent=oo,In=r(),ir=s("p"),ir.textContent=lo,En=r(),cr=s("ul"),cr.innerHTML=io,ca=r(),Y=s("div"),f(et.$$.fragment),Hn=r(),mr=s("p"),mr.innerHTML=co,Jn=r(),f(se.$$.fragment),ma=r(),U=s("div"),f(tt.$$.fragment),Dn=r(),dr=s("p"),dr.innerHTML=mo,Un=r(),pr=s("p"),pr.innerHTML=po,An=r(),S=s("div"),f(rt.$$.fragment),Nn=r(),fr=s("p"),fr.innerHTML=fo,Fn=r(),gr=s("p"),gr.textContent=go,Sn=r(),hr=s("ul"),hr.innerHTML=ho,da=r(),f(at.$$.fragment),pa=r(),T=s("div"),f(nt.$$.fragment),Pn=r(),ur=s("p"),ur.textContent=uo,Wn=r(),br=s("p"),br.innerHTML=bo,zn=r(),_r=s("p"),_r.innerHTML=_o,Bn=r(),f(oe.$$.fragment),Rn=r(),le=s("div"),f(st.$$.fragment),Vn=r(),vr=s("p"),vr.textContent=vo,Gn=r(),ie=s("div"),f(ot.$$.fragment),Yn=r(),Tr=s("p"),Tr.textContent=To,qn=r(),ce=s("div"),f(lt.$$.fragment),On=r(),$r=s("p"),$r.textContent=$o,Xn=r(),me=s("div"),f(it.$$.fragment),Zn=r(),Cr=s("p"),Cr.innerHTML=Co,Qn=r(),de=s("div"),f(ct.$$.fragment),Kn=r(),kr=s("p"),kr.textContent=ko,es=r(),pe=s("div"),f(mt.$$.fragment),ts=r(),wr=s("p"),wr.textContent=wo,rs=r(),fe=s("div"),f(dt.$$.fragment),as=r(),Mr=s("p"),Mr.textContent=Mo,ns=r(),ge=s("div"),f(pt.$$.fragment),ss=r(),yr=s("p"),yr.textContent=yo,os=r(),he=s("div"),f(ft.$$.fragment),ls=r(),xr=s("p"),xr.textContent=xo,is=r(),ue=s("div"),f(gt.$$.fragment),cs=r(),Lr=s("p"),Lr.textContent=Lo,ms=r(),be=s("div"),f(ht.$$.fragment),ds=r(),jr=s("p"),jr.textContent=jo,ps=r(),_e=s("div"),f(ut.$$.fragment),fs=r(),Ir=s("p"),Ir.textContent=Io,gs=r(),ve=s("div"),f(bt.$$.fragment),hs=r(),Er=s("p"),Er.textContent=Eo,fa=r(),_t=s("p"),_t.innerHTML=Ho,ga=r(),f(vt.$$.fragment),ha=r(),Tt=s("p"),Tt.innerHTML=Jo,ua=r(),f($t.$$.fragment),ba=r(),f(Ct.$$.fragment),_a=r(),E=s("div"),f(kt.$$.fragment),us=r(),Hr=s("p"),Hr.innerHTML=Do,bs=r(),f(Te.$$.fragment),_s=r(),$e=s("div"),f(wt.$$.fragment),vs=r(),Jr=s("p"),Jr.innerHTML=Uo,Ts=r(),Ce=s("div"),f(Mt.$$.fragment),$s=r(),Dr=s("p"),Dr.innerHTML=Ao,va=r(),f(yt.$$.fragment),Ta=r(),re=s("div"),f(xt.$$.fragment),Cs=r(),Ur=s("p"),Ur.innerHTML=No,$a=r(),zr=s("p"),this.h()},l(e){const 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