import{S as it,i as dt,s as pt,e as r,k as f,w,t as p,M as mt,c as n,d as a,m as b,a as l,x as v,h as m,b as u,G as t,g as _,y as $,q as T,o as M,B as J,v as ht,L as Ae}from"../../chunks/vendor-hf-doc-builder.js";import{D as ct}from"../../chunks/Docstring-hf-doc-builder.js";import{C as De}from"../../chunks/CodeBlock-hf-doc-builder.js";import{I as Ke}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{E as Xe}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";function ut(Z){let s,h;return s=new De({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_metric

rouge_metric = load_metric(<span class="hljs-string">&quot;rouge&quot;</span>)


<span class="hljs-keyword">def</span> <span class="hljs-title function_">rouge_fn</span>(<span class="hljs-params">predictions, labels</span>):
    decoded_predictions = tokenizer.batch_decode(predictions, skip_special_tokens=<span class="hljs-literal">True</span>)
    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=<span class="hljs-literal">True</span>)
    result = rouge_metric.compute(predictions=decoded_predictions, references=decoded_labels)
    <span class="hljs-keyword">return</span> {key: value.mid.fmeasure * <span class="hljs-number">100</span> <span class="hljs-keyword">for</span> key, value <span class="hljs-keyword">in</span> result.items()}`}}),{c(){w(s.$$.fragment)},l(o){v(s.$$.fragment,o)},m(o,d){$(s,o,d),h=!0},p:Ae,i(o){h||(T(s.$$.fragment,o),h=!0)},o(o){M(s.$$.fragment,o),h=!1},d(o){J(s,o)}}}function ft(Z){let s,h,o,d,E;return d=new De({props:{code:"JTdCJ3JvdWdlMSclM0ElMjAzNy40MTk5JTJDJTIwJ3JvdWdlMiclM0ElMjAxMy45NzY4JTJDJTIwJ3JvdWdlTCclM0ElMjAzNC4zNjElMkMlMjAncm91Z2VMc3VtJyUzQSUyMDM1LjA3ODE=",highlighted:'{&#x27;rouge1&#x27;: <span class="hljs-number">37.4199</span>, &#x27;rouge2&#x27;: <span class="hljs-number">13.9768</span>, &#x27;rougeL&#x27;: <span class="hljs-number">34.361</span>, &#x27;rougeLsum&#x27;: <span class="hljs-number">35.0781</span>'}}),{c(){s=r("p"),h=p("The above function will return a dict containing values which will be logged like any other Keras metric:"),o=f(),w(d.$$.fragment)},l(c){s=n(c,"P",{});var x=l(s);h=m(x,"The above function will return a dict containing values which will be logged like any other Keras metric:"),x.forEach(a),o=b(c),v(d.$$.fragment,c)},m(c,x){_(c,s,x),t(s,h),_(c,o,x),$(d,c,x),E=!0},p:Ae,i(c){E||(T(d.$$.fragment,c),E=!0)},o(c){M(d.$$.fragment,c),E=!1},d(c){c&&a(s),c&&a(o),J(d,c)}}}function bt(Z){let s,h;return s=new De({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycy5rZXJhc19jYWxsYmFja3MlMjBpbXBvcnQlMjBQdXNoVG9IdWJDYWxsYmFjayUwQSUwQXB1c2hfdG9faHViX2NhbGxiYWNrJTIwJTNEJTIwUHVzaFRvSHViQ2FsbGJhY2soJTBBJTIwJTIwJTIwJTIwb3V0cHV0X2RpciUzRCUyMi4lMkZtb2RlbF9zYXZlJTIyJTJDJTBBJTIwJTIwJTIwJTIwdG9rZW5pemVyJTNEdG9rZW5pemVyJTJDJTBBJTIwJTIwJTIwJTIwaHViX21vZGVsX2lkJTNEJTIyZ3B0NS03eGxhcmdlJTIyJTJDJTBBKSUwQSUwQW1vZGVsLmZpdCh0cmFpbl9kYXRhc2V0JTJDJTIwY2FsbGJhY2tzJTNEJTVCcHVzaF90b19odWJfY2FsbGJhY2slNUQp",highlighted:`<span class="hljs-keyword">from</span> transformers.keras_callbacks <span class="hljs-keyword">import</span> PushToHubCallback

push_to_hub_callback = PushToHubCallback(
    output_dir=<span class="hljs-string">&quot;./model_save&quot;</span>,
    tokenizer=tokenizer,
    hub_model_id=<span class="hljs-string">&quot;gpt5-7xlarge&quot;</span>,
)

model.fit(train_dataset, callbacks=[push_to_hub_callback])`}}),{c(){w(s.$$.fragment)},l(o){v(s.$$.fragment,o)},m(o,d){$(s,o,d),h=!0},p:Ae,i(o){h||(T(s.$$.fragment,o),h=!0)},o(o){M(s.$$.fragment,o),h=!1},d(o){J(s,o)}}}function gt(Z){let s,h,o,d,E,c,x,R,be,ce,A,ge,ie,j,z,Y,B,_e,q,ke,de,g,G,ye,k,we,L,ve,$e,O,Te,Me,Q,Je,Ee,ee,xe,Ce,We,te,je,Ie,H,Ne,V,pe,I,U,ae,S,Ze,oe,ze,me,y,K,He,N,Ve,se,Ue,Pe,re,Be,Ge,Se,P,he;return c=new Ke({}),B=new Ke({}),G=new ct({props:{name:"class transformers.KerasMetricCallback",anchor:"transformers.KerasMetricCallback",parameters:[{name:"metric_fn",val:": typing.Callable"},{name:"eval_dataset",val:": typing.Union[tensorflow.python.data.ops.dataset_ops.DatasetV2, numpy.ndarray, tensorflow.python.framework.ops.Tensor, tuple, dict]"},{name:"output_cols",val:": typing.Optional[typing.List[str]] = None"},{name:"label_cols",val:": typing.Optional[typing.List[str]] = None"},{name:"batch_size",val:": typing.Optional[int] = None"},{name:"predict_with_generate",val:": bool = False"},{name:"use_xla_generation",val:": bool = False"},{name:"generate_kwargs",val:": typing.Optional[dict] = None"}],parametersDescription:[{anchor:"transformers.KerasMetricCallback.metric_fn",description:`<strong>metric_fn</strong> (<code>Callable</code>) &#x2014;
Metric function provided by the user. It will be called with two arguments - <code>predictions</code> and <code>labels</code>.
These contain the model&#x2019;s outputs and matching labels from the dataset. It should return a dict mapping
metric names to numerical values.`,name:"metric_fn"},{anchor:"transformers.KerasMetricCallback.eval_dataset",description:`<strong>eval_dataset</strong> (<code>tf.data.Dataset</code> or <code>dict</code> or <code>tuple</code> or <code>np.ndarray</code> or <code>tf.Tensor</code>) &#x2014;
Validation data to be used to generate predictions for the <code>metric_fn</code>.`,name:"eval_dataset"},{anchor:"transformers.KerasMetricCallback.output_cols",description:"<strong>output_cols</strong> (`List[str], <em>optional</em>) &#x2014;\nA list of columns to be retained from the model output as the predictions. Defaults to all.",name:"output_cols"},{anchor:"transformers.KerasMetricCallback.label_cols",description:`<strong>label_cols</strong> (&#x2019;<code>List[str]</code>, <em>optional</em>&#x2019;) &#x2014;
A list of columns to be retained from the input dataset as the labels. Will be autodetected if this is not
supplied.`,name:"label_cols"},{anchor:"transformers.KerasMetricCallback.batch_size",description:`<strong>batch_size</strong> (<code>int</code>, <em>optional</em>) &#x2014;
Batch size. Only used when the data is not a pre-batched <code>tf.data.Dataset</code>.`,name:"batch_size"},{anchor:"transformers.KerasMetricCallback.predict_with_generate",description:`<strong>predict_with_generate</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether we should use <code>model.generate()</code> to get outputs for the model.`,name:"predict_with_generate"},{anchor:"transformers.KerasMetricCallback.use_xla_generation",description:`<strong>use_xla_generation</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
If we&#x2019;re generating, whether to compile model generation with XLA. This can massively increase the speed of
generation (up to 100X speedup) but will require a new XLA compilation for each input shape. When using XLA
generation, it&#x2019;s a good idea to pad your inputs to the same size, or to use the <code>pad_to_multiple_of</code>
argument in your <code>tokenizer</code> or <code>DataCollator</code>, which will reduce the number of unique input shapes and
save a lot of compilation time. This option has no effect is <code>predict_with_generate</code> is <code>False</code>.`,name:"use_xla_generation"},{anchor:"transformers.KerasMetricCallback.generate_kwargs",description:`<strong>generate_kwargs</strong> (<code>dict</code>, <em>optional</em>) &#x2014;
Keyword arguments to pass to <code>model.generate()</code> when generating. Has no effect if <code>predict_with_generate</code>
is <code>False</code>.`,name:"generate_kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.31.0/src/transformers/keras_callbacks.py#L21"}}),H=new Xe({props:{anchor:"transformers.KerasMetricCallback.example",$$slots:{default:[ut]},$$scope:{ctx:Z}}}),V=new Xe({props:{anchor:"transformers.KerasMetricCallback.example-2",$$slots:{default:[ft]},$$scope:{ctx:Z}}}),S=new Ke({}),K=new ct({props:{name:"class transformers.PushToHubCallback",anchor:"transformers.PushToHubCallback",parameters:[{name:"output_dir",val:": typing.Union[str, pathlib.Path]"},{name:"save_strategy",val:": typing.Union[str, transformers.trainer_utils.IntervalStrategy] = 'epoch'"},{name:"save_steps",val:": typing.Optional[int] = None"},{name:"tokenizer",val:": typing.Optional[transformers.tokenization_utils_base.PreTrainedTokenizerBase] = None"},{name:"hub_model_id",val:": typing.Optional[str] = None"},{name:"hub_token",val:": typing.Optional[str] = None"},{name:"checkpoint",val:": bool = False"},{name:"**model_card_args",val:""}],parametersDescription:[{anchor:"transformers.PushToHubCallback.output_dir",description:`<strong>output_dir</strong> (<code>str</code>) &#x2014;
The output directory where the model predictions and checkpoints will be written and synced with the
repository on the Hub.`,name:"output_dir"},{anchor:"transformers.PushToHubCallback.save_strategy",description:`<strong>save_strategy</strong> (<code>str</code> or <a href="/docs/transformers/v4.31.0/en/internal/trainer_utils#transformers.IntervalStrategy">IntervalStrategy</a>, <em>optional</em>, defaults to <code>&quot;epoch&quot;</code>) &#x2014;
The checkpoint save strategy to adopt during training. Possible values are:</p>
<ul>
<li><code>&quot;no&quot;</code>: Save is done at the end of training.</li>
<li><code>&quot;epoch&quot;</code>: Save is done at the end of each epoch.</li>
<li><code>&quot;steps&quot;</code>: Save is done every <code>save_steps</code></li>
</ul>`,name:"save_strategy"},{anchor:"transformers.PushToHubCallback.save_steps",description:`<strong>save_steps</strong> (<code>int</code>, <em>optional</em>) &#x2014;
The number of steps between saves when using the &#x201C;steps&#x201D; <code>save_strategy</code>.`,name:"save_steps"},{anchor:"transformers.PushToHubCallback.tokenizer",description:`<strong>tokenizer</strong> (<code>PreTrainedTokenizerBase</code>, <em>optional</em>) &#x2014;
The tokenizer used by the model. If supplied, will be uploaded to the repo alongside the weights.`,name:"tokenizer"},{anchor:"transformers.PushToHubCallback.hub_model_id",description:`<strong>hub_model_id</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The name of the repository to keep in sync with the local <code>output_dir</code>. It can be a simple model ID in
which case the model will be pushed in your namespace. Otherwise it should be the whole repository name,
for instance <code>&quot;user_name/model&quot;</code>, which allows you to push to an organization you are a member of with
<code>&quot;organization_name/model&quot;</code>.</p>
<p>Will default to the name of <code>output_dir</code>.`,name:"hub_model_id"},{anchor:"transformers.PushToHubCallback.hub_token",description:`<strong>hub_token</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The token to use to push the model to the Hub. Will default to the token in the cache folder obtained with
<code>huggingface-cli login</code>.`,name:"hub_token"},{anchor:"transformers.PushToHubCallback.checkpoint",description:`<strong>checkpoint</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether to save full training checkpoints (including epoch and optimizer state) to allow training to be
resumed. Only usable when <code>save_strategy</code> is <code>&quot;epoch&quot;</code>.`,name:"checkpoint"}],source:"https://github.com/huggingface/transformers/blob/v4.31.0/src/transformers/keras_callbacks.py#L270"}}),P=new Xe({props:{anchor:"transformers.PushToHubCallback.example",$$slots:{default:[bt]},$$scope:{ctx:Z}}}),{c(){s=r("meta"),h=f(),o=r("h1"),d=r("a"),E=r("span"),w(c.$$.fragment),x=f(),R=r("span"),be=p("Keras callbacks"),ce=f(),A=r("p"),ge=p(`When training a Transformers model with Keras, there are some library-specific callbacks available to automate common
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metrics and return a dict mapping metric names to metric values.`),We=f(),te=r("p"),je=p(`We provide an example of a suitable metric_fn that computes ROUGE scores for a summarization model below. Note that
this example skips some post-processing for readability and simplicity, and should probably not be used as-is!`),Ie=f(),w(H.$$.fragment),Ne=f(),w(V.$$.fragment),pe=f(),I=r("h2"),U=r("a"),ae=r("span"),w(S.$$.fragment),Ze=f(),oe=r("span"),ze=p("PushToHubCallback"),me=f(),y=r("div"),w(K.$$.fragment),He=f(),N=r("p"),Ve=p(`Callback that will save and push the model to the Hub regularly. By default, it pushes once per epoch, but this can
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