import{s as sl,c as ml,u as dl,g as pl,d as gl,o as fl,n as an}from"../chunks/scheduler.9991993c.js";import{S as ll,i as il,r as g,u as f,v as h,d,t as p,w as u,g as o,m as al,s as r,h as s,j as v,n as nl,f as n,c as a,k as $,a as c,y as t,o as hl,A as ul,x as i}from"../chunks/index.7fc9a5e7.js";import{T as cl}from"../chunks/Tip.9de92fc6.js";import{D as k}from"../chunks/Docstring.ef7d0149.js";import{C as rn}from"../chunks/CodeBlock.e11cba92.js";import{E as ol}from"../chunks/ExampleCodeBlock.0db1a011.js";import{H as Zr,E as bl}from"../chunks/EditOnGithub.84ab7f0e.js";function _l(E){let m,M,b,w,x;const T=E[1].default,y=ml(T,E,E[2],null);return{c(){m=o("p"),M=al("Deprecated in "),b=al(E[0]),w=r(),y&&y.c(),this.h()},l(L){m=s(L,"P",{class:!0});var I=v(m);M=nl(I,"Deprecated in "),b=nl(I,E[0]),I.forEach(n),w=a(L),y&&y.l(L),this.h()},h(){$(m,"class","font-medium")},m(L,I){c(L,m,I),t(m,M),t(m,b),c(L,w,I),y&&y.m(L,I),x=!0},p(L,I){(!x||I&1)&&hl(b,L[0]),y&&y.p&&(!x||I&4)&&dl(y,T,L,L[2],x?gl(T,L[2],I,null):pl(L[2]),null)},i(L){x||(d(y,L),x=!0)},o(L){p(y,L),x=!1},d(L){L&&(n(m),n(w)),y&&y.d(L)}}}function vl(E){let m,M;return m=new cl({props:{warning:!0,$$slots:{default:[_l]},$$scope:{ctx:E}}}),{c(){g(m.$$.fragment)},l(b){f(m.$$.fragment,b)},m(b,w){h(m,b,w),M=!0},p(b,[w]){const x={};w&5&&(x.$$scope={dirty:w,ctx:b}),m.$set(x)},i(b){M||(d(m.$$.fragment,b),M=!0)},o(b){p(m.$$.fragment,b),M=!1},d(b){u(m,b)}}}function Tl(E,m,M){let{$$slots:b={},$$scope:w}=m,{version:x}=m;return E.$$set=T=>{"version"in T&&M(0,x=T.version),"$$scope"in T&&M(2,w=T.$$scope)},[x,b,w]}class $l extends ll{constructor(m){super(),il(this,m,Tl,vl,sl,{version:0})}}function Cl(E){let m,M="Setting <code>WANDB_LOG_MODEL</code> as <code>bool</code> will be deprecated in version 5 of 🤗 Transformers.";return{c(){m=o("p"),m.innerHTML=M},l(b){m=s(b,"P",{"data-svelte-h":!0}),i(m)!=="svelte-fxlq1n"&&(m.innerHTML=M)},m(b,w){c(b,m,w)},p:an,d(b){b&&n(m)}}}function kl(E){let m,M="Example:",b,w,x;return w=new rn({props:{code:"JTIzJTIwTm90ZSUzQSUyMFRoaXMlMjBleGFtcGxlJTIwc2tpcHMlMjBvdmVyJTIwc29tZSUyMHNldHVwJTIwc3RlcHMlMjBmb3IlMjBicmV2aXR5LiUwQWZyb20lMjBmbHl0ZWtpdCUyMGltcG9ydCUyMGN1cnJlbnRfY29udGV4dCUyQyUyMHRhc2slMEElMEElMEElNDB0YXNrJTBBZGVmJTIwdHJhaW5faGZfdHJhbnNmb3JtZXIoKSUzQSUwQSUyMCUyMCUyMCUyMGNwJTIwJTNEJTIwY3VycmVudF9jb250ZXh0KCkuY2hlY2twb2ludCUwQSUyMCUyMCUyMCUyMHRyYWluZXIlMjAlM0QlMjBUcmFpbmVyKC4uLiUyQyUyMGNhbGxiYWNrcyUzRCU1QkZseXRlQ2FsbGJhY2soKSU1RCklMEElMjAlMjAlMjAlMjBvdXRwdXQlMjAlM0QlMjB0cmFpbmVyLnRyYWluKHJlc3VtZV9mcm9tX2NoZWNrcG9pbnQlM0RjcC5yZXN0b3JlKCkp",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=o("p"),m.textContent=M,b=r(),g(w.$$.fragment)},l(T){m=s(T,"P",{"data-svelte-h":!0}),i(m)!=="svelte-11lpom8"&&(m.textContent=M),b=a(T),f(w.$$.fragment,T)},m(T,y){c(T,m,y),c(T,b,y),h(w,T,y),x=!0},p:an,i(T){x||(d(w.$$.fragment,T),x=!0)},o(T){p(w.$$.fragment,T),x=!1},d(T){T&&(n(m),n(b)),u(w,T)}}}function wl(E){let m,M="Example:",b,w,x;return w=new rn({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=o("p"),m.textContent=M,b=r(),g(w.$$.fragment)},l(T){m=s(T,"P",{"data-svelte-h":!0}),i(m)!=="svelte-11lpom8"&&(m.textContent=M),b=a(T),f(w.$$.fragment,T)},m(T,y){c(T,m,y),c(T,b,y),h(w,T,y),x=!0},p:an,i(T){x||(d(w.$$.fragment,T),x=!0)},o(T){p(w.$$.fragment,T),x=!1},d(T){T&&(n(m),n(b)),u(w,T)}}}function xl(E){let m,M=`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=o("p"),m.innerHTML=M},l(b){m=s(b,"P",{"data-svelte-h":!0}),i(m)!=="svelte-rhwh6p"&&(m.innerHTML=M)},m(b,w){c(b,m,w)},p:an,d(b){b&&n(m)}}}function yl(E){let m,M,b,w,x,T,y,L="Callbacks可以用来自定义PyTorch [Trainer]中训练循环行为的对象（此功能尚未在TensorFlow中实现），该对象可以检查训练循环状态（用于进度报告、在TensorBoard或其他ML平台上记录日志等），并做出决策（例如提前停止）。",I,He,So='Callbacks是“只读”的代码片段，除了它们返回的[TrainerControl]对象外，它们不能更改训练循环中的任何内容。对于需要更改训练循环的自定义，您应该继承[Trainer]并重载您需要的方法（有关示例，请参见<a href="trainer">trainer</a>）。',Qr,je,Fo="默认情况下，<code>TrainingArguments.report_to</code> 设置为”all”，然后[Trainer]将使用以下callbacks。",Kr,ze,Wo='<li><a href="/docs/transformers/main/zh/main_classes/callback#transformers.DefaultFlowCallback">DefaultFlowCallback</a>，它处理默认的日志记录、保存和评估行为</li> <li><a href="/docs/transformers/main/zh/main_classes/callback#transformers.PrinterCallback">PrinterCallback</a> 或 <a href="/docs/transformers/main/zh/main_classes/callback#transformers.ProgressCallback">ProgressCallback</a>，用于显示进度和打印日志（如果通过<code>TrainingArguments</code>停用tqdm，则使用第一个函数；否则使用第二个）。</li> <li><a href="/docs/transformers/main/zh/main_classes/callback#transformers.integrations.TensorBoardCallback">TensorBoardCallback</a>，如果TensorBoard可访问（通过PyTorch版本 &gt;= 1.4 或者 tensorboardX）。</li> <li><a href="/docs/transformers/main/zh/main_classes/callback#transformers.integrations.WandbCallback">WandbCallback</a>，如果安装了<a href="https://www.wandb.com/" rel="nofollow">wandb</a>。</li> <li><a href="/docs/transformers/main/zh/main_classes/callback#transformers.integrations.CometCallback">CometCallback</a>，如果安装了<a href="https://www.comet.com/site/" rel="nofollow">comet_ml</a>。</li> <li><a href="/docs/transformers/main/zh/main_classes/callback#transformers.integrations.MLflowCallback">MLflowCallback</a>，如果安装了<a href="https://www.mlflow.org/" rel="nofollow">mlflow</a>。</li> <li><a href="/docs/transformers/main/zh/main_classes/callback#transformers.integrations.NeptuneCallback">NeptuneCallback</a>，如果安装了<a href="https://neptune.ai/" rel="nofollow">neptune</a>。</li> <li><a href="/docs/transformers/main/zh/main_classes/callback#transformers.integrations.AzureMLCallback">AzureMLCallback</a>，如果安装了<a href="https://pypi.org/project/azureml-sdk/" rel="nofollow">azureml-sdk</a>。</li> <li><a href="/docs/transformers/main/zh/main_classes/callback#transformers.integrations.CodeCarbonCallback">CodeCarbonCallback</a>，如果安装了<a href="https://pypi.org/project/codecarbon/" rel="nofollow">codecarbon</a>。</li> <li><a href="/docs/transformers/main/zh/main_classes/callback#transformers.integrations.ClearMLCallback">ClearMLCallback</a>，如果安装了<a href="https://github.com/allegroai/clearml" rel="nofollow">clearml</a>。</li> <li><a href="/docs/transformers/main/zh/main_classes/callback#transformers.integrations.DagsHubCallback">DagsHubCallback</a>，如果安装了<a href="https://dagshub.com/" rel="nofollow">dagshub</a>。</li> <li><a href="/docs/transformers/main/zh/main_classes/callback#transformers.integrations.FlyteCallback">FlyteCallback</a>，如果安装了<a href="https://flyte.org/" rel="nofollow">flyte</a>。</li> <li><a href="/docs/transformers/main/zh/main_classes/callback#transformers.integrations.DVCLiveCallback">DVCLiveCallback</a>，如果安装了<a href="https://dvc.org/doc/dvclive" rel="nofollow">dvclive</a>。</li>',ea,Je,Bo="如果安装了一个软件包，但您不希望使用相关的集成，您可以将 <code>TrainingArguments.report_to</code> 更改为仅包含您想要使用的集成的列表（例如 <code>[&quot;azure_ml&quot;, &quot;wandb&quot;]</code>）。",ta,Ue,Ro='实现callbacks的主要类是<a href="/docs/transformers/main/zh/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a>。它获取用于实例化<code>Trainer</code>的<code>TrainingArguments</code>，可以通过<a href="/docs/transformers/main/zh/main_classes/callback#transformers.TrainerState">TrainerState</a>访问该Trainer的内部状态，并可以通过<a href="/docs/transformers/main/zh/main_classes/callback#transformers.TrainerControl">TrainerControl</a>对训练循环执行一些操作。',ra,De,aa,Ne,Vo='这里是库里可用<a href="/docs/transformers/main/zh/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a>的列表：',na,S,Ae,nn,Dt,Go='A <a href="/docs/transformers/main/zh/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> that sends the logs to <a href="https://www.comet.com/site/" rel="nofollow">Comet ML</a>.',on,j,Pe,sn,Nt,qo="Setup the optional Comet integration.",ln,At,Yo="Environment:",cn,Pt,Oo=`<li><strong>COMET_MODE</strong> (<code>str</code>, <em>optional</em>, default to <code>get_or_create</code>):
Control whether to create and log to a new Comet experiment or append to an existing experiment.
It accepts the following values:<ul><li><code>get_or_create</code>: Decides automatically depending if
<code>COMET_EXPERIMENT_KEY</code> is set and whether an Experiment
with that key already exists or not.</li> <li><code>create</code>: Always create a new Comet Experiment.</li> <li><code>get</code>: Always try to append to an Existing Comet Experiment.
Requires <code>COMET_EXPERIMENT_KEY</code> to be set.</li> <li><code>ONLINE</code>: <strong>deprecated</strong>, used to create an online
Experiment. Use <code>COMET_START_ONLINE=1</code> instead.</li> <li><code>OFFLINE</code>: <strong>deprecated</strong>, used to created an offline
Experiment. Use <code>COMET_START_ONLINE=0</code> instead.</li> <li><code>DISABLED</code>: <strong>deprecated</strong>, used to disable Comet logging.
Use the <code>--report_to</code> flag to control the integrations used
for logging result instead.</li></ul></li> <li><strong>COMET_PROJECT_NAME</strong> (<code>str</code>, <em>optional</em>):
Comet project name for experiments.</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>`,mn,St,Xo=`For a number of configurable items in the environment, see
<a href="https://www.comet.com/docs/v2/guides/experiment-management/configure-sdk/#explore-comet-configuration-options" rel="nofollow">here</a>.`,oa,X,Se,dn,Ft,Zo='A <a href="/docs/transformers/main/zh/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> that handles the default flow of the training loop for logs, evaluation and checkpoints.',sa,Z,Fe,pn,Wt,Qo='A bare <a href="/docs/transformers/main/zh/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> that just prints the logs.',la,Q,We,gn,Bt,Ko='A <a href="/docs/transformers/main/zh/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> that displays the progress of training or evaluation.',ia,F,Be,fn,Rt,es='A <a href="/docs/transformers/main/zh/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> that handles early stopping.',hn,Vt,ts=`This callback depends on <code>TrainingArguments</code> argument <em>load_best_model_at_end</em> functionality to set best_metric
in <a href="/docs/transformers/main/zh/main_classes/callback#transformers.TrainerState">TrainerState</a>. Note that if the <code>TrainingArguments</code> argument <em>save_steps</em> differs from <em>eval_steps</em>, the
early stopping will not occur until the next save step.`,ca,K,Re,un,Gt,rs='A <a href="/docs/transformers/main/zh/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> that sends the logs to <a href="https://www.tensorflow.org/tensorboard" rel="nofollow">TensorBoard</a>.',ma,W,Ve,bn,qt,as='A <a href="/docs/transformers/main/zh/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>.',_n,z,Ge,vn,Yt,ns="Setup the optional Weights &amp; Biases (<em>wandb</em>) integration.",Tn,Ot,os=`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:`,$n,Xt,ss="Environment:",Cn,B,qe,Zt,ls=`<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.`,kn,ne,wn,Qt,is=`<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>`,xn,Kt,cs=`<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>`,yn,er,ms=`<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>`,da,R,Ye,Mn,tr,ds=`A <a href="/docs/transformers/main/zh/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>.`,Ln,N,Oe,En,rr,ps="Setup the optional MLflow integration.",In,ar,gs="Environment:",Hn,nr,fs=`<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
<code>TrainingArguments</code>’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>`,pa,ee,Xe,jn,or,hs='A <a href="/docs/transformers/main/zh/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> that sends the logs to <a href="https://pypi.org/project/azureml-sdk/" rel="nofollow">AzureML</a>.',ga,te,Ze,zn,sr,us='A <a href="/docs/transformers/main/zh/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> that tracks the CO2 emission of training.',fa,V,Qe,Jn,lr,bs='TrainerCallback that sends the logs to <a href="https://app.neptune.ai" rel="nofollow">Neptune</a>.',Un,ir,_s=`For instructions and examples, see the <a href="https://docs.neptune.ai/integrations/transformers" rel="nofollow">Transformers integration
guide</a> in the Neptune documentation.`,ha,J,Ke,Dn,cr,vs='A <a href="/docs/transformers/main/zh/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> that sends the logs to <a href="https://clear.ml/" rel="nofollow">ClearML</a>.',Nn,mr,Ts="Environment:",An,dr,$s=`<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>`,ua,G,et,Pn,pr,Cs='A <a href="/docs/transformers/main/zh/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> that logs to <a href="https://dagshub.com/" rel="nofollow">DagsHub</a>. Extends <code>MLflowCallback</code>',Sn,A,tt,Fn,gr,ks="Setup the DagsHub’s Logging integration.",Wn,fr,ws="Environment:",Bn,hr,xs=`<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>`,ba,q,rt,Rn,ur,ys=`A <a href="/docs/transformers/main/zh/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.`,Vn,oe,_a,U,at,Gn,br,Ms='A <a href="/docs/transformers/main/zh/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> that sends the logs to <a href="https://www.dvc.org/doc/dvclive" rel="nofollow">DVCLive</a>.',qn,_r,Ls=`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>.`,Yn,P,nt,On,vr,Es=`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>.`,Xn,Tr,Is="Environment:",Zn,$r,Hs=`<li><strong>HF_DVCLIVE_LOG_MODEL</strong> (<code>str</code>, <em>optional</em>):
Whether to use <code>dvclive.Live.log_artifact()</code> to log checkpoints created by <code>Trainer</code>. If set to <code>True</code> or
<em>1</em>, the final checkpoint is logged at the end of training. If set to <code>all</code>, the entire
<code>TrainingArguments</code>’s <code>output_dir</code> is logged at each checkpoint.</li>`,va,ot,Ta,_,st,Qn,Cr,js=`A class for objects that will inspect the state of the training loop at some events and take some decisions. At
each of those events the following arguments are available:`,Kn,kr,zs=`The <code>control</code> object is the only one that can be changed by the callback, in which case the event that changes it
should return the modified version.`,eo,wr,Js=`The argument <code>args</code>, <code>state</code> and <code>control</code> are positionals for all events, all the others are grouped in <code>kwargs</code>.
You can unpack the ones you need in the signature of the event using them. As an example, see the code of the
simple <a href="/docs/transformers/main/zh/main_classes/callback#transformers.PrinterCallback">PrinterCallback</a>.`,to,se,ro,le,lt,ao,xr,Us="Event called at the beginning of an epoch.",no,ie,it,oo,yr,Ds="Event called at the end of an epoch.",so,ce,ct,lo,Mr,Ns="Event called after an evaluation phase.",io,me,mt,co,Lr,As="Event called at the end of the initialization of the <code>Trainer</code>.",mo,de,dt,po,Er,Ps="Event called after logging the last logs.",go,pe,pt,fo,Ir,Ss="Event called after the optimizer step but before gradients are zeroed out. Useful for monitoring gradients.",ho,ge,gt,uo,Hr,Fs="Event called before the optimizer step but after gradient clipping. Useful for monitoring gradients.",bo,fe,ft,_o,jr,Ws="Event called after a successful prediction.",vo,he,ht,To,zr,Bs="Event called after a prediction step.",$o,ue,ut,Co,Jr,Rs="Event called after a checkpoint save.",ko,be,bt,wo,Ur,Vs=`Event called at the beginning of a training step. If using gradient accumulation, one training step might take
several inputs.`,xo,_e,_t,yo,Dr,Gs=`Event called at the end of a training step. If using gradient accumulation, one training step might take
several inputs.`,Mo,ve,vt,Lo,Nr,qs="Event called at the end of an substep during gradient accumulation.",Eo,Te,Tt,Io,Ar,Ys="Event called at the beginning of training.",Ho,$e,$t,jo,Pr,Os="Event called at the end of training.",$a,Ct,Xs="以下是如何使用PyTorch注册自定义callback的示例：",Ca,kt,Zs="<code>Trainer</code>:",ka,wt,wa,xt,Qs="注册callback的另一种方式是调用 <code>trainer.add_callback()</code>，如下所示：",xa,yt,ya,Mt,Ma,H,Lt,zo,Sr,Ks=`A class containing the <code>Trainer</code> inner state that will be saved along the model and optimizer when checkpointing
and passed to the <a href="/docs/transformers/main/zh/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a>.`,Jo,Ce,Uo,ke,Et,Do,Fr,el="Create an instance from the content of <code>json_path</code>.",No,we,It,Ao,Wr,tl="Save the content of this instance in JSON format inside <code>json_path</code>.",La,Ht,Ea,re,jt,Po,Br,rl=`A class that handles the <code>Trainer</code> control flow. This class is used by the <a href="/docs/transformers/main/zh/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> to activate some
switches in the training loop.`,Ia,zt,Ha,Xr,ja;return x=new Zr({props:{title:"Callbacks",local:"callbacks",headingTag:"h1"}}),De=new Zr({props:{title:"可用的Callbacks",local:"transformers.integrations.CometCallback",headingTag:"h2"}}),Ae=new k({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#L1005"}}),Pe=new k({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#L1019"}}),Se=new k({props:{name:"class transformers.DefaultFlowCallback",anchor:"transformers.DefaultFlowCallback",parameters:[],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/trainer_callback.py#L536"}}),Fe=new k({props:{name:"class transformers.PrinterCallback",anchor:"transformers.PrinterCallback",parameters:[],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/trainer_callback.py#L647"}}),We=new k({props:{name:"class transformers.ProgressCallback",anchor:"transformers.ProgressCallback",parameters:[],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/trainer_callback.py#L589"}}),Be=new k({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#L658"}}),Re=new k({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#L628"}}),Ve=new k({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#L759"}}),Ge=new k({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#L775"}}),ne=new $l({props:{version:"5.0",$$slots:{default:[Cl]},$$scope:{ctx:E}}}),Ye=new k({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#L1172"}}),Oe=new k({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#L1191"}}),Xe=new k({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#L1149"}}),Ze=new k({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#L1673"}}),Qe=new k({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>, <em>optional</em>, 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#L1400"}}),Ke=new k({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#L1707"}}),et=new k({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#L1340"}}),tt=new k({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#L1354"}}),rt=new k({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#L1960"}}),oe=new ol({props:{anchor:"transformers.integrations.FlyteCallback.example",$$slots:{default:[kl]},$$scope:{ctx:E}}}),at=new k({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 <code>Trainer</code>. 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
<code>TrainingArguments</code>&#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#L2023"}}),nt=new k({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#L2064"}}),ot=new Zr({props:{title:"TrainerCallback",local:"transformers.TrainerCallback",headingTag:"h2"}}),st=new k({props:{name:"class transformers.TrainerCallback",anchor:"transformers.TrainerCallback",parameters:[],parametersDescription:[{anchor:"transformers.TrainerCallback.args",description:`<strong>args</strong> (<code>TrainingArguments</code>) &#x2014;
The training arguments used to instantiate the <code>Trainer</code>.`,name:"args"},{anchor:"transformers.TrainerCallback.state",description:`<strong>state</strong> (<a href="/docs/transformers/main/zh/main_classes/callback#transformers.TrainerState">TrainerState</a>) &#x2014;
The current state of the <code>Trainer</code>.`,name:"state"},{anchor:"transformers.TrainerCallback.control",description:`<strong>control</strong> (<a href="/docs/transformers/main/zh/main_classes/callback#transformers.TrainerControl">TrainerControl</a>) &#x2014;
The object that is returned to the <code>Trainer</code> and can be used to make some decisions.`,name:"control"},{anchor:"transformers.TrainerCallback.model",description:`<strong>model</strong> (<a href="/docs/transformers/main/zh/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/zh/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>) &#x2014;
The tokenizer used for encoding the data. This is deprecated in favour of <code>processing_class</code>.`,name:"tokenizer"},{anchor:"transformers.TrainerCallback.processing_class",description:`<strong>processing_class</strong> ([<code>PreTrainedTokenizer</code> or <code>BaseImageProcessor</code> or <code>ProcessorMixin</code> or <code>FeatureExtractionMixin</code>]) &#x2014;
The processing class used for encoding the data. Can be a tokenizer, a processor, an image processor or a feature extractor.`,name:"processing_class"},{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 evaluation.`,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#L259"}}),se=new ol({props:{anchor:"transformers.TrainerCallback.example",$$slots:{default:[wl]},$$scope:{ctx:E}}}),lt=new k({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#L330"}}),it=new k({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#L336"}}),ct=new k({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#L374"}}),mt=new k({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#L312"}}),dt=new k({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#L392"}}),pt=new k({props:{name:"on_optimizer_step",anchor:"transformers.TrainerCallback.on_optimizer_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#L355"}}),gt=new k({props:{name:"on_pre_optimizer_step",anchor:"transformers.TrainerCallback.on_pre_optimizer_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#L349"}}),ft=new k({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#L380"}}),ht=new k({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#L398"}}),ut=new k({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#L386"}}),bt=new k({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#L342"}}),_t=new k({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#L367"}}),vt=new k({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#L361"}}),Tt=new k({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#L318"}}),$t=new k({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#L324"}}),wt=new rn({props:{code:"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",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}}),yt=new rn({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}}),Mt=new Zr({props:{title:"TrainerState",local:"transformers.TrainerState",headingTag:"h2"}}),Lt=new k({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"},{name:"stateful_callbacks",val:": List = 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"},{anchor:"transformers.TrainerState.stateful_callbacks",description:`<strong>stateful_callbacks</strong> (<code>List[StatefulTrainerCallback]</code>, <em>optional</em>) &#x2014;
Callbacks attached to the <code>Trainer</code> that should have their states be saved or restored.
Relevent callbacks should implement a <code>state</code> and <code>from_state</code> function.`,name:"stateful_callbacks"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/trainer_callback.py#L35"}}),Ce=new cl({props:{$$slots:{default:[xl]},$$scope:{ctx:E}}}),Et=new k({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#L145"}}),It=new k({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#L139"}}),Ht=new Zr({props:{title:"TrainerControl",local:"transformers.TrainerControl",headingTag:"h2"}}),jt=new k({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#L197"}}),zt=new 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