import{s as yl,o as hl,n as Ge}from"../chunks/scheduler.25b97de1.js";import{S as dl,i as Ul,g as o,s as r,r as c,A as _l,h as f,f as l,c as s,j as wl,u as w,x as d,k as Jl,y as jl,a,v as J,d as T,t as y,w as h}from"../chunks/index.d9030fc9.js";import{C as _}from"../chunks/CodeBlock.b38ef023.js";import{F as Tl,M as Le}from"../chunks/Markdown.d8c1b6cc.js";import{H as g,E as gl}from"../chunks/index.676ef4be.js";function $l(j){let n,u='示例脚本从🤗 <a href="https://huggingface.co/docs/datasets/" rel="nofollow">Datasets</a>库下载并预处理数据集。然后，脚本通过<a href="https://huggingface.co/docs/transformers/main_classes/trainer" rel="nofollow">Trainer</a>使用支持摘要任务的架构对数据集进行微调。以下示例展示了如何在<a href="https://huggingface.co/datasets/cnn_dailymail" rel="nofollow">CNN/DailyMail</a>数据集上微调<a href="https://huggingface.co/google-t5/t5-small" rel="nofollow">T5-small</a>。由于T5模型的训练方式，它需要一个额外的<code>source_prefix</code>参数。这个提示让T5知道这是一个摘要任务。',i,m,p;return m=new _({props:{code:"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",highlighted:`python examples/pytorch/summarization/run_summarization.py \\
    --model_name_or_path google-t5/t5-small \\
    --do_train \\
    --do_eval \\
    --dataset_name cnn_dailymail \\
    --dataset_config <span class="hljs-string">&quot;3.0.0&quot;</span> \\
    --source_prefix <span class="hljs-string">&quot;summarize: &quot;</span> \\
    --output_dir /tmp/tst-summarization \\
    --per_device_train_batch_size=4 \\
    --per_device_eval_batch_size=4 \\
    --overwrite_output_dir \\
    --predict_with_generate`,wrap:!1}}),{c(){n=o("p"),n.innerHTML=u,i=r(),c(m.$$.fragment)},l(M){n=f(M,"P",{"data-svelte-h":!0}),d(n)!=="svelte-1nzsvd0"&&(n.innerHTML=u),i=s(M),w(m.$$.fragment,M)},m(M,U){a(M,n,U),a(M,i,U),J(m,M,U),p=!0},p:Ge,i(M){p||(T(m.$$.fragment,M),p=!0)},o(M){y(m.$$.fragment,M),p=!1},d(M){M&&(l(n),l(i)),h(m,M)}}}function bl(j){let n,u;return n=new Le({props:{$$slots:{default:[$l]},$$scope:{ctx:j}}}),{c(){c(n.$$.fragment)},l(i){w(n.$$.fragment,i)},m(i,m){J(n,i,m),u=!0},p(i,m){const p={};m&2&&(p.$$scope={dirty:m,ctx:i}),n.$set(p)},i(i){u||(T(n.$$.fragment,i),u=!0)},o(i){y(n.$$.fragment,i),u=!1},d(i){h(n,i)}}}function Cl(j){let n,u='示例脚本从  🤗 <a href="https://huggingface.co/docs/datasets/" rel="nofollow">Datasets</a> 库下载并预处理数据集。然后，脚本使用 Keras 在支持摘要的架构上微调数据集。以下示例展示了如何在 <a href="https://huggingface.co/datasets/cnn_dailymail" rel="nofollow">CNN/DailyMail</a> 数据集上微调 <a href="https://huggingface.co/google-t5/t5-small" rel="nofollow">T5-small</a>。T5 模型由于训练方式需要额外的 <code>source_prefix</code> 参数。这个提示让 T5 知道这是一个摘要任务。',i,m,p;return m=new _({props:{code:"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",highlighted:`python examples/tensorflow/summarization/run_summarization.py  \\
    --model_name_or_path google-t5/t5-small \\
    --dataset_name cnn_dailymail \\
    --dataset_config <span class="hljs-string">&quot;3.0.0&quot;</span> \\
    --output_dir /tmp/tst-summarization  \\
    --per_device_train_batch_size 8 \\
    --per_device_eval_batch_size 16 \\
    --num_train_epochs 3 \\
    --do_train \\
    --do_eval`,wrap:!1}}),{c(){n=o("p"),n.innerHTML=u,i=r(),c(m.$$.fragment)},l(M){n=f(M,"P",{"data-svelte-h":!0}),d(n)!=="svelte-l86hpd"&&(n.innerHTML=u),i=s(M),w(m.$$.fragment,M)},m(M,U){a(M,n,U),a(M,i,U),J(m,M,U),p=!0},p:Ge,i(M){p||(T(m.$$.fragment,M),p=!0)},o(M){y(m.$$.fragment,M),p=!1},d(M){M&&(l(n),l(i)),h(m,M)}}}function Xl(j){let n,u;return n=new Le({props:{$$slots:{default:[Cl]},$$scope:{ctx:j}}}),{c(){c(n.$$.fragment)},l(i){w(n.$$.fragment,i)},m(i,m){J(n,i,m),u=!0},p(i,m){const p={};m&2&&(p.$$scope={dirty:m,ctx:i}),n.$set(p)},i(i){u||(T(n.$$.fragment,i),u=!0)},o(i){y(n.$$.fragment,i),u=!1},d(i){h(n,i)}}}function Il(j){let n,u='张量处理单元（TPUs）是专门设计用于加速性能的。PyTorch使用<a href="https://www.tensorflow.org/xla" rel="nofollow">XLA</a>深度学习编译器支持TPU（更多细节请参见<a href="https://github.com/pytorch/xla/blob/master/README.md" rel="nofollow">这里</a>）。要使用TPU，请启动<code>xla_spawn.py</code>脚本并使用<code>num_cores</code>参数设置要使用的TPU核心数量。',i,m,p;return m=new _({props:{code:"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",highlighted:`python xla_spawn.py --num_cores 8 \\
    summarization/run_summarization.py \\
    --model_name_or_path google-t5/t5-small \\
    --do_train \\
    --do_eval \\
    --dataset_name cnn_dailymail \\
    --dataset_config <span class="hljs-string">&quot;3.0.0&quot;</span> \\
    --source_prefix <span class="hljs-string">&quot;summarize: &quot;</span> \\
    --output_dir /tmp/tst-summarization \\
    --per_device_train_batch_size=4 \\
    --per_device_eval_batch_size=4 \\
    --overwrite_output_dir \\
    --predict_with_generate`,wrap:!1}}),{c(){n=o("p"),n.innerHTML=u,i=r(),c(m.$$.fragment)},l(M){n=f(M,"P",{"data-svelte-h":!0}),d(n)!=="svelte-1a7iv41"&&(n.innerHTML=u),i=s(M),w(m.$$.fragment,M)},m(M,U){a(M,n,U),a(M,i,U),J(m,M,U),p=!0},p:Ge,i(M){p||(T(m.$$.fragment,M),p=!0)},o(M){y(m.$$.fragment,M),p=!1},d(M){M&&(l(n),l(i)),h(m,M)}}}function Al(j){let n,u;return n=new Le({props:{$$slots:{default:[Il]},$$scope:{ctx:j}}}),{c(){c(n.$$.fragment)},l(i){w(n.$$.fragment,i)},m(i,m){J(n,i,m),u=!0},p(i,m){const p={};m&2&&(p.$$scope={dirty:m,ctx:i}),n.$set(p)},i(i){u||(T(n.$$.fragment,i),u=!0)},o(i){y(n.$$.fragment,i),u=!1},d(i){h(n,i)}}}function vl(j){let n,u='张量处理单元（TPUs）是专门设计用于加速性能的。TensorFlow脚本使用<a href="https://www.tensorflow.org/guide/distributed_training#tpustrategy" rel="nofollow"><code>TPUStrategy</code></a>在TPU上进行训练。要使用TPU，请将TPU资源的名称传递给<code>tpu</code>参数。',i,m,p;return m=new _({props:{code:"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",highlighted:`python run_summarization.py  \\
    --tpu name_of_tpu_resource \\
    --model_name_or_path google-t5/t5-small \\
    --dataset_name cnn_dailymail \\
    --dataset_config <span class="hljs-string">&quot;3.0.0&quot;</span> \\
    --output_dir /tmp/tst-summarization  \\
    --per_device_train_batch_size 8 \\
    --per_device_eval_batch_size 16 \\
    --num_train_epochs 3 \\
    --do_train \\
    --do_eval`,wrap:!1}}),{c(){n=o("p"),n.innerHTML=u,i=r(),c(m.$$.fragment)},l(M){n=f(M,"P",{"data-svelte-h":!0}),d(n)!=="svelte-1aunkch"&&(n.innerHTML=u),i=s(M),w(m.$$.fragment,M)},m(M,U){a(M,n,U),a(M,i,U),J(m,M,U),p=!0},p:Ge,i(M){p||(T(m.$$.fragment,M),p=!0)},o(M){y(m.$$.fragment,M),p=!1},d(M){M&&(l(n),l(i)),h(m,M)}}}function Wl(j){let n,u;return n=new Le({props:{$$slots:{default:[vl]},$$scope:{ctx:j}}}),{c(){c(n.$$.fragment)},l(i){w(n.$$.fragment,i)},m(i,m){J(n,i,m),u=!0},p(i,m){const p={};m&2&&(p.$$scope={dirty:m,ctx:i}),n.$set(p)},i(i){u||(T(n.$$.fragment,i),u=!0)},o(i){y(n.$$.fragment,i),u=!1},d(i){h(n,i)}}}function Zl(j){let n,u,i,m,p,M,U,Re='除了 🤗 Transformers <a href="./notebooks">notebooks</a>，还有示例脚本演示了如何使用<a href="https://github.com/huggingface/transformers/tree/main/examples/pytorch" rel="nofollow">PyTorch</a>、<a href="https://github.com/huggingface/transformers/tree/main/examples/tensorflow" rel="nofollow">TensorFlow</a>或<a href="https://github.com/huggingface/transformers/tree/main/examples/flax" rel="nofollow">JAX/Flax</a>训练模型以解决特定任务。',It,C,Ve='您还可以在这些示例中找到我们在<a href="https://github.com/huggingface/transformers-research-projects/" rel="nofollow">研究项目</a>和<a href="https://github.com/huggingface/transformers/tree/main/examples/legacy" rel="nofollow">遗留示例</a>中使用过的脚本，这些脚本主要是由社区贡献的。这些脚本已不再被积极维护，需要使用特定版本的🤗 Transformers， 可能与库的最新版本不兼容。',At,X,xe="示例脚本可能无法在初始配置下直接解决每个问题，您可能需要根据要解决的问题调整脚本。为了帮助您，大多数脚本都完全暴露了数据预处理的方式，允许您根据需要对其进行编辑。",vt,I,Ne='如果您想在示例脚本中实现任何功能，请在<a href="https://discuss.huggingface.co/" rel="nofollow">论坛</a>或<a href="https://github.com/huggingface/transformers/issues" rel="nofollow">issue</a>上讨论，然后再提交Pull Request。虽然我们欢迎修复错误，但不太可能合并添加更多功能的Pull Request，因为这会降低可读性。',Wt,A,ze='本指南将向您展示如何在<a href="https://github.com/huggingface/transformers/tree/main/examples/pytorch/summarization" rel="nofollow">PyTorch</a>和<a href="https://github.com/huggingface/transformers/tree/main/examples/tensorflow/summarization" rel="nofollow">TensorFlow</a>中运行示例摘要训练脚本。除非另有说明，否则所有示例都可以在两个框架中工作。',Zt,v,Gt,W,Be="要成功运行示例脚本的最新版本，您必须在新虚拟环境中<strong>从源代码安装 🤗 Transformers</strong>：",Lt,Z,Rt,G,Fe="对于旧版本的示例脚本，请点击下面的切换按钮：",Vt,L,Ye='<summary>老版本🤗 Transformers示例</summary> <ul><li><a href="https://github.com/huggingface/transformers/tree/v4.5.1/examples">v4.5.1</a></li> <li><a href="https://github.com/huggingface/transformers/tree/v4.4.2/examples">v4.4.2</a></li> <li><a href="https://github.com/huggingface/transformers/tree/v4.3.3/examples">v4.3.3</a></li> <li><a href="https://github.com/huggingface/transformers/tree/v4.2.2/examples">v4.2.2</a></li> <li><a href="https://github.com/huggingface/transformers/tree/v4.1.1/examples">v4.1.1</a></li> <li><a href="https://github.com/huggingface/transformers/tree/v4.0.1/examples">v4.0.1</a></li> <li><a href="https://github.com/huggingface/transformers/tree/v3.5.1/examples">v3.5.1</a></li> <li><a href="https://github.com/huggingface/transformers/tree/v3.4.0/examples">v3.4.0</a></li> <li><a href="https://github.com/huggingface/transformers/tree/v3.3.1/examples">v3.3.1</a></li> <li><a href="https://github.com/huggingface/transformers/tree/v3.2.0/examples">v3.2.0</a></li> <li><a href="https://github.com/huggingface/transformers/tree/v3.1.0/examples">v3.1.0</a></li> <li><a href="https://github.com/huggingface/transformers/tree/v3.0.2/examples">v3.0.2</a></li> <li><a href="https://github.com/huggingface/transformers/tree/v2.11.0/examples">v2.11.0</a></li> <li><a href="https://github.com/huggingface/transformers/tree/v2.10.0/examples">v2.10.0</a></li> <li><a href="https://github.com/huggingface/transformers/tree/v2.9.1/examples">v2.9.1</a></li> <li><a href="https://github.com/huggingface/transformers/tree/v2.8.0/examples">v2.8.0</a></li> <li><a href="https://github.com/huggingface/transformers/tree/v2.7.0/examples">v2.7.0</a></li> <li><a href="https://github.com/huggingface/transformers/tree/v2.6.0/examples">v2.6.0</a></li> <li><a href="https://github.com/huggingface/transformers/tree/v2.5.1/examples">v2.5.1</a></li> <li><a href="https://github.com/huggingface/transformers/tree/v2.4.0/examples">v2.4.0</a></li> <li><a href="https://github.com/huggingface/transformers/tree/v2.3.0/examples">v2.3.0</a></li> <li><a href="https://github.com/huggingface/transformers/tree/v2.2.0/examples">v2.2.0</a></li> <li><a href="https://github.com/huggingface/transformers/tree/v2.1.0/examples">v2.1.1</a></li> <li><a href="https://github.com/huggingface/transformers/tree/v2.0.0/examples">v2.0.0</a></li> <li><a href="https://github.com/huggingface/transformers/tree/v1.2.0/examples">v1.2.0</a></li> <li><a href="https://github.com/huggingface/transformers/tree/v1.1.0/examples">v1.1.0</a></li> <li><a href="https://github.com/huggingface/transformers/tree/v1.0.0/examples">v1.0.0</a></li></ul>',xt,R,Se="然后切换您clone的 🤗 Transformers 仓到特定的版本，例如v3.5.1：",Nt,V,zt,x,He="在安装了正确的库版本后，进入您选择的版本的<code>example</code>文件夹并安装例子要求的环境：",Bt,N,Ft,z,Yt,$,St,B,Ht,F,Ee='<a href="https://huggingface.co/docs/transformers/main_classes/trainer" rel="nofollow">Trainer</a> 支持分布式训练和混合精度，这意味着你也可以在脚本中使用它。要启用这两个功能，可以做如下设置：',Et,Y,ke="<li>添加 <code>fp16</code> 参数以启用混合精度。</li> <li>使用 <code>nproc_per_node</code> 参数设置使用的GPU数量。</li>",kt,S,Qt,H,Qe='TensorFlow脚本使用<a href="https://www.tensorflow.org/guide/distributed_training#mirroredstrategy" rel="nofollow"><code>MirroredStrategy</code></a>进行分布式训练，您无需在训练脚本中添加任何其他参数。如果可用，TensorFlow脚本将默认使用多个GPU。',Dt,E,Pt,b,qt,k,Ot,Q,De='🤗 <a href="https://huggingface.co/docs/accelerate" rel="nofollow">Accelerate</a> 是一个仅支持 PyTorch 的库，它提供了一种统一的方法来在不同类型的设置（仅 CPU、多个 GPU、多个TPU）上训练模型，同时保持对 PyTorch 训练循环的完全可见性。如果你还没有安装 🤗 Accelerate，请确保你已经安装了它：',Kt,D,Pe="<p>注意：由于 Accelerate 正在快速发展，因此必须安装 git 版本的 accelerate 来运行脚本。</p>",te,P,ee,q,qe="你需要使用<code>run_summarization_no_trainer.py</code>脚本，而不是<code>run_summarization.py</code>脚本。🤗 Accelerate支持的脚本需要在文件夹中有一个<code>task_no_trainer.py</code>文件。首先运行以下命令以创建并保存配置文件：",le,O,ae,K,Oe="检测您的设置以确保配置正确：",ne,tt,ie,et,Ke="现在您可以开始训练模型了：",re,lt,se,at,Me,nt,tl="摘要脚本支持自定义数据集，只要它们是CSV或JSON Line文件。当你使用自己的数据集时，需要指定一些额外的参数：",me,it,el="<li><code>train_file</code> 和 <code>validation_file</code> 分别指定您的训练和验证文件的路径。</li> <li><code>text_column</code> 是输入要进行摘要的文本。</li> <li><code>summary_column</code> 是目标输出的文本。</li>",pe,rt,ll="使用自定义数据集的摘要脚本看起来是这样的：",oe,st,fe,Mt,ue,mt,al="通常，在提交整个数据集之前，最好先在较少的数据集示例上运行脚本，以确保一切按预期工作,因为完整数据集的处理可能需要花费几个小时的时间。使用以下参数将数据集截断为最大样本数：",ce,pt,nl="<li><code>max_train_samples</code></li> <li><code>max_eval_samples</code></li> <li><code>max_predict_samples</code></li>",we,ot,Je,ft,il="并非所有示例脚本都支持<code>max_predict_samples</code>参数。如果您不确定您的脚本是否支持此参数，请添加<code>-h</code>参数进行检查：",Te,ut,ye,ct,he,wt,rl="另一个有用的选项是从之前的checkpoint恢复训练。这将确保在训练中断时，您可以从之前停止的地方继续进行，而无需重新开始。有两种方法可以从checkpoint恢复训练。",de,Jt,sl="第一种方法使用<code>output_dir previous_output_dir</code>参数从存储在<code>output_dir</code>中的最新的checkpoint恢复训练。在这种情况下，您应该删除<code>overwrite_output_dir</code>：",Ue,Tt,_e,yt,Ml="第二种方法使用<code>resume_from_checkpoint path_to_specific_checkpoint</code>参数从特定的checkpoint文件夹恢复训练。",je,ht,ge,dt,$e,Ut,ml='所有脚本都可以将您的最终模型上传到<a href="https://huggingface.co/models" rel="nofollow">Model Hub</a>。在开始之前，请确保您已登录Hugging Face：',be,_t,Ce,jt,pl="然后，在脚本中添加<code>push_to_hub</code>参数。这个参数会创建一个带有您Hugging Face用户名和<code>output_dir</code>中指定的文件夹名称的仓库。",Xe,gt,ol="为了给您的仓库指定一个特定的名称，使用<code>push_to_hub_model_id</code>参数来添加它。该仓库将自动列出在您的命名空间下。",Ie,$t,fl="以下示例展示了如何上传具有特定仓库名称的模型：",Ae,bt,ve,Ct,We,Xt,Ze;return p=new g({props:{title:"使用脚本进行训练",local:"使用脚本进行训练",headingTag:"h1"}}),v=new g({props:{title:"设置",local:"设置",headingTag:"h2"}}),Z=new _({props:{code:"Z2l0JTIwY2xvbmUlMjBodHRwcyUzQSUyRiUyRmdpdGh1Yi5jb20lMkZodWdnaW5nZmFjZSUyRnRyYW5zZm9ybWVycyUwQWNkJTIwdHJhbnNmb3JtZXJzJTBBcGlwJTIwaW5zdGFsbCUyMC4=",highlighted:`git <span class="hljs-built_in">clone</span> https://github.com/huggingface/transformers
<span class="hljs-built_in">cd</span> transformers
pip install .`,wrap:!1}}),V=new _({props:{code:"Z2l0JTIwY2hlY2tvdXQlMjB0YWdzJTJGdjMuNS4x",highlighted:"git checkout tags/v3.5.1",wrap:!1}}),N=new _({props:{code:"cGlwJTIwaW5zdGFsbCUyMC1yJTIwcmVxdWlyZW1lbnRzLnR4dA==",highlighted:"pip install -r requirements.txt",wrap:!1}}),z=new g({props:{title:"运行脚本",local:"运行脚本",headingTag:"h2"}}),$=new Tl({props:{pytorch:!0,tensorflow:!0,jax:!1,$$slots:{tensorflow:[Xl],pytorch:[bl]},$$scope:{ctx:j}}}),B=new g({props:{title:"分布式训练和混合精度",local:"分布式训练和混合精度",headingTag:"h2"}}),S=new _({props:{code:"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",highlighted:`torchrun \\
    --nproc_per_node 8 pytorch/summarization/run_summarization.py \\
    --fp16 \\
    --model_name_or_path google-t5/t5-small \\
    --do_train \\
    --do_eval \\
    --dataset_name cnn_dailymail \\
    --dataset_config <span class="hljs-string">&quot;3.0.0&quot;</span> \\
    --source_prefix <span class="hljs-string">&quot;summarize: &quot;</span> \\
    --output_dir /tmp/tst-summarization \\
    --per_device_train_batch_size=4 \\
    --per_device_eval_batch_size=4 \\
    --overwrite_output_dir \\
    --predict_with_generate`,wrap:!1}}),E=new g({props:{title:"在TPU上运行脚本",local:"在tpu上运行脚本",headingTag:"h2"}}),b=new Tl({props:{pytorch:!0,tensorflow:!0,jax:!1,$$slots:{tensorflow:[Wl],pytorch:[Al]},$$scope:{ctx:j}}}),k=new g({props:{title:"基于🤗 Accelerate运行脚本",local:"基于-accelerate运行脚本",headingTag:"h2"}}),P=new _({props:{code:"cGlwJTIwaW5zdGFsbCUyMGdpdCUyQmh0dHBzJTNBJTJGJTJGZ2l0aHViLmNvbSUyRmh1Z2dpbmdmYWNlJTJGYWNjZWxlcmF0ZQ==",highlighted:"pip install git+https://github.com/huggingface/accelerate",wrap:!1}}),O=new _({props:{code:"YWNjZWxlcmF0ZSUyMGNvbmZpZw==",highlighted:"accelerate config",wrap:!1}}),tt=new _({props:{code:"YWNjZWxlcmF0ZSUyMHRlc3Q=",highlighted:'accelerate <span class="hljs-built_in">test</span>',wrap:!1}}),lt=new _({props:{code:"YWNjZWxlcmF0ZSUyMGxhdW5jaCUyMHJ1bl9zdW1tYXJpemF0aW9uX25vX3RyYWluZXIucHklMjAlNUMlMEElMjAlMjAlMjAlMjAtLW1vZGVsX25hbWVfb3JfcGF0aCUyMGdvb2dsZS10NSUyRnQ1LXNtYWxsJTIwJTVDJTBBJTIwJTIwJTIwJTIwLS1kYXRhc2V0X25hbWUlMjBjbm5fZGFpbHltYWlsJTIwJTVDJTBBJTIwJTIwJTIwJTIwLS1kYXRhc2V0X2NvbmZpZyUyMCUyMjMuMC4wJTIyJTIwJTVDJTBBJTIwJTIwJTIwJTIwLS1zb3VyY2VfcHJlZml4JTIwJTIyc3VtbWFyaXplJTNBJTIwJTIyJTIwJTVDJTBBJTIwJTIwJTIwJTIwLS1vdXRwdXRfZGlyJTIwfiUyRnRtcCUyRnRzdC1zdW1tYXJpemF0aW9u",highlighted:`accelerate launch run_summarization_no_trainer.py \\
    --model_name_or_path google-t5/t5-small \\
    --dataset_name cnn_dailymail \\
    --dataset_config <span class="hljs-string">&quot;3.0.0&quot;</span> \\
    --source_prefix <span class="hljs-string">&quot;summarize: &quot;</span> \\
    --output_dir ~/tmp/tst-summarization`,wrap:!1}}),at=new g({props:{title:"使用自定义数据集",local:"使用自定义数据集",headingTag:"h2"}}),st=new _({props:{code:"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",highlighted:`python examples/pytorch/summarization/run_summarization.py \\
    --model_name_or_path google-t5/t5-small \\
    --do_train \\
    --do_eval \\
    --train_file path_to_csv_or_jsonlines_file \\
    --validation_file path_to_csv_or_jsonlines_file \\
    --text_column text_column_name \\
    --summary_column summary_column_name \\
    --source_prefix <span class="hljs-string">&quot;summarize: &quot;</span> \\
    --output_dir /tmp/tst-summarization \\
    --overwrite_output_dir \\
    --per_device_train_batch_size=4 \\
    --per_device_eval_batch_size=4 \\
    --predict_with_generate`,wrap:!1}}),Mt=new g({props:{title:"测试脚本",local:"测试脚本",headingTag:"h2"}}),ot=new _({props:{code:"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",highlighted:`python examples/pytorch/summarization/run_summarization.py \\
    --model_name_or_path google-t5/t5-small \\
    --max_train_samples 50 \\
    --max_eval_samples 50 \\
    --max_predict_samples 50 \\
    --do_train \\
    --do_eval \\
    --dataset_name cnn_dailymail \\
    --dataset_config <span class="hljs-string">&quot;3.0.0&quot;</span> \\
    --source_prefix <span class="hljs-string">&quot;summarize: &quot;</span> \\
    --output_dir /tmp/tst-summarization \\
    --per_device_train_batch_size=4 \\
    --per_device_eval_batch_size=4 \\
    --overwrite_output_dir \\
    --predict_with_generate`,wrap:!1}}),ut=new _({props:{code:"ZXhhbXBsZXMlMkZweXRvcmNoJTJGc3VtbWFyaXphdGlvbiUyRnJ1bl9zdW1tYXJpemF0aW9uLnB5JTIwLWg=",highlighted:"examples/pytorch/summarization/run_summarization.py -h",wrap:!1}}),ct=new g({props:{title:"从checkpoint恢复训练",local:"从checkpoint恢复训练",headingTag:"h2"}}),Tt=new _({props:{code:"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",highlighted:`python examples/pytorch/summarization/run_summarization.py
    --model_name_or_path google-t5/t5-small \\
    --do_train \\
    --do_eval \\
    --dataset_name cnn_dailymail \\
    --dataset_config <span class="hljs-string">&quot;3.0.0&quot;</span> \\
    --source_prefix <span class="hljs-string">&quot;summarize: &quot;</span> \\
    --output_dir /tmp/tst-summarization \\
    --per_device_train_batch_size=4 \\
    --per_device_eval_batch_size=4 \\
    --output_dir previous_output_dir \\
    --predict_with_generate`,wrap:!1}}),ht=new _({props:{code:"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",highlighted:`python examples/pytorch/summarization/run_summarization.py
    --model_name_or_path google-t5/t5-small \\
    --do_train \\
    --do_eval \\
    --dataset_name cnn_dailymail \\
    --dataset_config <span class="hljs-string">&quot;3.0.0&quot;</span> \\
    --source_prefix <span class="hljs-string">&quot;summarize: &quot;</span> \\
    --output_dir /tmp/tst-summarization \\
    --per_device_train_batch_size=4 \\
    --per_device_eval_batch_size=4 \\
    --overwrite_output_dir \\
    --resume_from_checkpoint path_to_specific_checkpoint \\
    --predict_with_generate`,wrap:!1}}),dt=new g({props:{title:"分享模型",local:"分享模型",headingTag:"h2"}}),_t=new _({props:{code:"aHVnZ2luZ2ZhY2UtY2xpJTIwbG9naW4=",highlighted:"huggingface-cli login",wrap:!1}}),bt=new _({props:{code:"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",highlighted:`python examples/pytorch/summarization/run_summarization.py
    --model_name_or_path google-t5/t5-small \\
    --do_train \\
    --do_eval \\
    --dataset_name cnn_dailymail \\
    --dataset_config <span class="hljs-string">&quot;3.0.0&quot;</span> \\
    --source_prefix <span class="hljs-string">&quot;summarize: &quot;</span> \\
    --push_to_hub \\
    --push_to_hub_model_id finetuned-t5-cnn_dailymail \\
    --output_dir /tmp/tst-summarization \\
    --per_device_train_batch_size=4 \\
    --per_device_eval_batch_size=4 \\
    --overwrite_output_dir \\
    --predict_with_generate`,wrap:!1}}),Ct=new 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