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`,highlighted:`<span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset
<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, DataCollatorWithPadding

raw_datasets = load_dataset(<span class="hljs-string">&quot;glue&quot;</span>, <span class="hljs-string">&quot;mrpc&quot;</span>)
checkpoint = <span class="hljs-string">&quot;bert-base-uncased&quot;</span>
tokenizer = AutoTokenizer.from_pretrained(checkpoint)


<span class="hljs-keyword">def</span> <span class="hljs-title function_">tokenize_function</span>(<span class="hljs-params">example</span>):
    <span class="hljs-keyword">return</span> tokenizer(example[<span class="hljs-string">&quot;sentence1&quot;</span>], example[<span class="hljs-string">&quot;sentence2&quot;</span>], truncation=<span class="hljs-literal">True</span>)


tokenized_datasets = raw_datasets.<span class="hljs-built_in">map</span>(tokenize_function, batched=<span class="hljs-literal">True</span>)
data_collator = DataCollatorWithPadding(tokenizer=tokenizer)`}}),le=new k({props:{code:`tokenized_datasets = tokenized_datasets.remove_columns(["sentence1", "sentence2", "idx"])
tokenized_datasets = tokenized_datasets.rename_column("label", "labels")
tokenized_datasets.set_format("torch")
tokenized_datasets["train"].column_names`,highlighted:`tokenized_datasets = tokenized_datasets.remove_columns([<span class="hljs-string">&quot;sentence1&quot;</span>, <span class="hljs-string">&quot;sentence2&quot;</span>, <span class="hljs-string">&quot;idx&quot;</span>])
tokenized_datasets = tokenized_datasets.rename_column(<span class="hljs-string">&quot;label&quot;</span>, <span class="hljs-string">&quot;labels&quot;</span>)
tokenized_datasets.set_format(<span class="hljs-string">&quot;torch&quot;</span>)
tokenized_datasets[<span class="hljs-string">&quot;train&quot;</span>].column_names`}}),re=new k({props:{code:'["attention_mask", "input_ids", "labels", "token_type_ids"]',highlighted:'[<span class="hljs-string">&quot;attention_mask&quot;</span>, <span class="hljs-string">&quot;input_ids&quot;</span>, <span class="hljs-string">&quot;labels&quot;</span>, <span class="hljs-string">&quot;token_type_ids&quot;</span>]'}}),oe=new k({props:{code:"",highlighted:`<span class="hljs-keyword">from</span> torch.utils.data <span class="hljs-keyword">import</span> DataLoader

train_dataloader = DataLoader(
    tokenized_datasets[<span class="hljs-string">&quot;train&quot;</span>], shuffle=<span class="hljs-literal">True</span>, batch_size=<span class="hljs-number">8</span>, collate_fn=data_collator
)
eval_dataloader = DataLoader(
    tokenized_datasets[<span class="hljs-string">&quot;validation&quot;</span>], batch_size=<span class="hljs-number">8</span>, collate_fn=data_collator
)`}}),pe=new k({props:{code:`for batch in train_dataloader:
    break
{k: v.shape for k, v in batch.items()}`,highlighted:`<span class="hljs-keyword">for</span> batch <span class="hljs-keyword">in</span> train_dataloader:
    <span class="hljs-keyword">break</span>
{k: v.shape <span class="hljs-keyword">for</span> k, v <span class="hljs-keyword">in</span> batch.items()}`}}),ne=new k({props:{code:`{'attention_mask': torch.Size([8, 65]),
 'input_ids': torch.Size([8, 65]),
 'labels': torch.Size([8]),
 'token_type_ids': torch.Size([8, 65])}`,highlighted:`{<span class="hljs-string">&#x27;attention_mask&#x27;</span>: torch.Size([<span class="hljs-number">8</span>, <span class="hljs-number">65</span>]),
 <span class="hljs-string">&#x27;input_ids&#x27;</span>: torch.Size([<span class="hljs-number">8</span>, <span class="hljs-number">65</span>]),
 <span class="hljs-string">&#x27;labels&#x27;</span>: torch.Size([<span class="hljs-number">8</span>]),
 <span class="hljs-string">&#x27;token_type_ids&#x27;</span>: torch.Size([<span class="hljs-number">8</span>, <span class="hljs-number">65</span>])}`}}),ie=new k({props:{code:"",highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForSequenceClassification

model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=<span class="hljs-number">2</span>)`}}),ce=new k({props:{code:`outputs = model(**batch)
print(outputs.loss, outputs.logits.shape)`,highlighted:`outputs = model(**batch)
<span class="hljs-built_in">print</span>(outputs.loss, outputs.logits.shape)`}}),fe=new k({props:{code:"tensor(0.5441, grad_fn=<NllLossBackward>) torch.Size([8, 2])",highlighted:'tensor(<span class="hljs-number">0.5441</span>, grad_fn=&lt;NllLossBackward&gt;) torch.Size([<span class="hljs-number">8</span>, <span class="hljs-number">2</span>])'}}),de=new k({props:{code:"",highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AdamW

optimizer = AdamW(model.parameters(), lr=<span class="hljs-number">5e-5</span>)`}}),ue=new k({props:{code:"",highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> get_scheduler

num_epochs = <span class="hljs-number">3</span>
num_training_steps = num_epochs * <span class="hljs-built_in">len</span>(train_dataloader)
lr_scheduler = get_scheduler(
    <span class="hljs-string">&quot;linear&quot;</span>,
    optimizer=optimizer,
    num_warmup_steps=<span class="hljs-number">0</span>,
    num_training_steps=num_training_steps,
)
<span class="hljs-built_in">print</span>(num_training_steps)`}}),_e=new k({props:{code:"1377",highlighted:'<span class="hljs-number">1377</span>'}}),he=new k({props:{code:"",highlighted:`<span class="hljs-keyword">import</span> torch

device = torch.device(<span class="hljs-string">&quot;cuda&quot;</span>) <span class="hljs-keyword">if</span> torch.cuda.is_available() <span class="hljs-keyword">else</span> torch.device(<span class="hljs-string">&quot;cpu&quot;</span>)
model.to(device)
device`}}),be=new k({props:{code:"device(type='cuda')",highlighted:'device(<span class="hljs-built_in">type</span>=<span class="hljs-string">&#x27;cuda&#x27;</span>)'}}),ke=new k({props:{code:`

`,highlighted:`<span class="hljs-keyword">from</span> tqdm.auto <span class="hljs-keyword">import</span> tqdm

progress_bar = tqdm(<span class="hljs-built_in">range</span>(num_training_steps))

model.train()
<span class="hljs-keyword">for</span> epoch <span class="hljs-keyword">in</span> <span class="hljs-built_in">range</span>(num_epochs):
    <span class="hljs-keyword">for</span> batch <span class="hljs-keyword">in</span> train_dataloader:
        batch = {k: v.to(device) <span class="hljs-keyword">for</span> k, v <span class="hljs-keyword">in</span> batch.items()}
        outputs = model(**batch)
        loss = outputs.loss
        loss.backward()

        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()
        progress_bar.update(<span class="hljs-number">1</span>)`}}),ve=new k({props:{code:`

`,highlighted:`<span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_metric

metric = load_metric(<span class="hljs-string">&quot;glue&quot;</span>, <span class="hljs-string">&quot;mrpc&quot;</span>)
model.<span class="hljs-built_in">eval</span>()
<span class="hljs-keyword">for</span> batch <span class="hljs-keyword">in</span> eval_dataloader:
    batch = {k: v.to(device) <span class="hljs-keyword">for</span> k, v <span class="hljs-keyword">in</span> batch.items()}
    <span class="hljs-keyword">with</span> torch.no_grad():
        outputs = model(**batch)

    logits = outputs.logits
    predictions = torch.argmax(logits, dim=-<span class="hljs-number">1</span>)
    metric.add_batch(predictions=predictions, references=batch[<span class="hljs-string">&quot;labels&quot;</span>])

metric.compute()`}}),we=new k({props:{code:"{'accuracy': 0.8431372549019608, 'f1': 0.8907849829351535}",highlighted:'{<span class="hljs-string">&#x27;accuracy&#x27;</span>: <span class="hljs-number">0.8431372549019608</span>, <span class="hljs-string">&#x27;f1&#x27;</span>: <span class="hljs-number">0.8907849829351535</span>}'}}),B=new Lr({props:{$$slots:{default:[Rr]},$$scope:{ctx:Te}}}),Ee=new Ir({}),ye=new Mr({props:{id:"s7dy8QRgjJ0"}}),ze=new k({props:{code:`




`,highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AdamW, AutoModelForSequenceClassification, get_scheduler

model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=<span class="hljs-number">2</span>)
optimizer = AdamW(model.parameters(), lr=<span class="hljs-number">3e-5</span>)

device = torch.device(<span class="hljs-string">&quot;cuda&quot;</span>) <span class="hljs-keyword">if</span> torch.cuda.is_available() <span class="hljs-keyword">else</span> torch.device(<span class="hljs-string">&quot;cpu&quot;</span>)
model.to(device)

num_epochs = <span class="hljs-number">3</span>
num_training_steps = num_epochs * <span class="hljs-built_in">len</span>(train_dataloader)
lr_scheduler = get_scheduler(
    <span class="hljs-string">&quot;linear&quot;</span>,
    optimizer=optimizer,
    num_warmup_steps=<span class="hljs-number">0</span>,
    num_training_steps=num_training_steps,
)

progress_bar = tqdm(<span class="hljs-built_in">range</span>(num_training_steps))

model.train()
<span class="hljs-keyword">for</span> epoch <span class="hljs-keyword">in</span> <span class="hljs-built_in">range</span>(num_epochs):
    <span class="hljs-keyword">for</span> batch <span class="hljs-keyword">in</span> train_dataloader:
        batch = {k: v.to(device) <span class="hljs-keyword">for</span> k, v <span class="hljs-keyword">in</span> batch.items()}
        outputs = model(**batch)
        loss = outputs.loss
        loss.backward()

        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()
        progress_bar.update(<span class="hljs-number">1</span>)`}}),ge=new k({props:{code:`






`,highlighted:`<span class="hljs-addition">+ from accelerate import Accelerator</span>
  from transformers import AdamW, AutoModelForSequenceClassification, get_scheduler

<span class="hljs-addition">+ accelerator = Accelerator()</span>

  model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)
  optimizer = AdamW(model.parameters(), lr=3e-5)

<span class="hljs-deletion">- device = torch.device(&quot;cuda&quot;) if torch.cuda.is_available() else torch.device(&quot;cpu&quot;)</span>
<span class="hljs-deletion">- model.to(device)</span>

<span class="hljs-addition">+ train_dataloader, eval_dataloader, model, optimizer = accelerator.prepare(</span>
<span class="hljs-addition">+     train_dataloader, eval_dataloader, model, optimizer</span>
<span class="hljs-addition">+ )</span>

  num_epochs = 3
  num_training_steps = num_epochs * len(train_dataloader)
  lr_scheduler = get_scheduler(
      &quot;linear&quot;,
      optimizer=optimizer,
      num_warmup_steps=0,
      num_training_steps=num_training_steps
  )

  progress_bar = tqdm(range(num_training_steps))

  model.train()
  for epoch in range(num_epochs):
      for batch in train_dataloader:
<span class="hljs-deletion">-         batch = {k: v.to(device) for k, v in batch.items()}</span>
          outputs = model(**batch)
          loss = outputs.loss
<span class="hljs-deletion">-         loss.backward()</span>
<span class="hljs-addition">+         accelerator.backward(loss)</span>

          optimizer.step()
          lr_scheduler.step()
          optimizer.zero_grad()
          progress_bar.update(1)`}}),Y=new Lr({props:{$$slots:{default:[Jr]},$$scope:{ctx:Te}}}),Ce=new k({props:{code:`





`,highlighted:`<span class="hljs-keyword">from</span> accelerate <span class="hljs-keyword">import</span> Accelerator
<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AdamW, AutoModelForSequenceClassification, get_scheduler

accelerator = Accelerator()

model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=<span class="hljs-number">2</span>)
optimizer = AdamW(model.parameters(), lr=<span class="hljs-number">3e-5</span>)

train_dl, eval_dl, model, optimizer = accelerator.prepare(
    train_dataloader, eval_dataloader, model, optimizer
)

num_epochs = <span class="hljs-number">3</span>
num_training_steps = num_epochs * <span class="hljs-built_in">len</span>(train_dl)
lr_scheduler = get_scheduler(
    <span class="hljs-string">&quot;linear&quot;</span>,
    optimizer=optimizer,
    num_warmup_steps=<span class="hljs-number">0</span>,
    num_training_steps=num_training_steps,
)

progress_bar = tqdm(<span class="hljs-built_in">range</span>(num_training_steps))

model.train()
<span class="hljs-keyword">for</span> epoch <span class="hljs-keyword">in</span> <span class="hljs-built_in">range</span>(num_epochs):
    <span class="hljs-keyword">for</span> batch <span class="hljs-keyword">in</span> train_dl:
        outputs = model(**batch)
        loss = outputs.loss
        accelerator.backward(loss)

        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()
        progress_bar.update(<span class="hljs-number">1</span>)`}}),Pe=new k({props:{code:"accelerate config",highlighted:"accelerate config"}}),Ae=new k({props:{code:"accelerate launch train.py",highlighted:'accelerate <span class="hljs-built_in">launch</span> train.py'}}),De=new k({props:{code:"",highlighted:`<span class="hljs-keyword">from</span> accelerate <span class="hljs-keyword">import</span> notebook_launcher

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\u0932\u093F\u0916\u0928\u0947 \u0915\u0947 \u0932\u093F\u090F \u0932\u0917\u092D\u0917 \u0924\u0948\u092F\u093E\u0930 \u0939\u0948\u0902! \u0939\u092E \u0915\u0947\u0935\u0932 \u0926\u094B \u091A\u0940\u091C\u0947\u0902 \u0916\u094B \u0930\u0939\u0947 \u0939\u0948\u0902: \u090F\u0915 \u0911\u092A\u094D\u091F\u093F\u092E\u093E\u0907\u091C\u093C\u0930 \u0914\u0930 \u090F\u0915 \u0932\u0930\u094D\u0928\u093F\u0902\u0917 \u0930\u0947\u091F \u0905\u0928\u0941\u0938\u0942\u091A\u0915\u0964 \u091A\u0942\u0902\u0915\u093F "),ps=p("code"),Aa=l("Trainer"),Da=l(" \u091C\u094B \u0915\u0930 \u0930\u0939\u093E \u0925\u093E \u0909\u0938\u0947 \u0939\u092E \u0916\u0941\u0926 \u0938\u0947 \u0926\u094B\u0939\u0930\u093E\u0928\u0947 \u0915\u0940 \u0915\u094B\u0936\u093F\u0936 \u0915\u0930 \u0930\u0939\u0947 \u0939\u0948\u0902, \u0924\u094B \u0939\u092E \u0909\u0928\u094D\u0939\u0940 \u0921\u093F\u092B\u093C\u0949\u0932\u094D\u091F \u0915\u093E \u0909\u092A\u092F\u094B\u0917 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\u0930\u0947\u0917\u0941\u0932\u0930\u093E\u0907\u091C\u0947\u0936\u0928\u201D"),La=l(" \u0926\u0947\u0916\u0947\u0902):"),Vs=c(),m(de.$$.fragment),Xs=c(),N=p("p"),Ma=l("\u0905\u0902\u0924 \u092E\u0947\u0902, \u0932\u0930\u094D\u0928\u093F\u0902\u0917 \u0930\u0947\u091F \u0905\u0928\u0941\u0938\u0942\u091A\u0915 \u091C\u093F\u0938\u0947 \u0921\u093F\u092B\u093C\u0949\u0932\u094D\u091F \u0930\u0942\u092A \u0938\u0947 \u0909\u092A\u092F\u094B\u0917 \u0915\u093F\u092F\u093E \u091C\u093E\u0924\u093E \u0939\u0948 \u0915\u0947\u0935\u0932 \u090F\u0915 \u0930\u0948\u0916\u093F\u0915 \u0915\u094D\u0937\u092F \u0939\u0948 \u091C\u094B \u0905\u0927\u093F\u0915\u0924\u092E \u092E\u0942\u0932\u094D\u092F (5e-5) \u0938\u0947 0 \u0924\u0915 \u0939\u0948\u0964 \u0907\u0938\u0947 \u0920\u0940\u0915 \u0938\u0947 \u092A\u0930\u093F\u092D\u093E\u0937\u093F\u0924 \u0915\u0930\u0928\u0947 \u0915\u0947 \u0932\u093F\u090F, \u0939\u092E\u0947\u0902 \u092F\u0939 \u091C\u093E\u0928\u0928\u093E \u0939\u094B\u0917\u093E \u0915\u093F \u0939\u092E \u0915\u093F\u0924\u0928\u0947 \u092A\u094D\u0930\u0936\u093F\u0915\u094D\u0937\u0923 \u0915\u0926\u092E \u0909\u0920\u093E\u090F\u0902\u0917\u0947, \u091C\u094B \u0915\u093F \u0939\u0948 \u092F\u0941\u0917\u094B\u0902 \u092F\u093E\u0928\u093F \u090F\u092A\u094B\u0915 \u0915\u0940 \u0938\u0902\u0916\u094D\u092F\u093E \u091C\u093F\u0928\u094D\u0939\u0947 \u0939\u092E\u0947 \u0930\u0928 \u0915\u0930\u0928\u093E \u0939\u0948 \u0909\u0938\u0915\u093E \u0917\u0941\u0923\u093E \u092A\u094D\u0930\u0936\u093F\u0915\u094D\u0937\u0923 \u092C\u0948\u091A\u094B\u0902 \u0915\u0940 \u0938\u0902\u0916\u094D\u092F\u093E \u0938\u0947 \u0915\u0930\u0928\u093E (\u091C\u094B \u0915\u093F \u0939\u092E\u093E\u0930\u0947 \u092A\u094D\u0930\u0936\u093F\u0915\u094D\u0937\u0923 \u0921\u0947\u091F\u093E\u0932\u094B\u0921\u0930 \u0915\u0940 \u0932\u0902\u092C\u093E\u0908 \u0939\u0948)\u0964 "),cs=p("code"),Wa=l("Trainer"),Ga=l(" \u0921\u093F\u092B\u093C\u0949\u0932\u094D\u091F \u0930\u0942\u092A \u0938\u0947 \u0924\u0940\u0928 \u092F\u0941\u0917\u094B\u0902 \u092F\u093E\u0928\u093F \u090F\u092A\u094B\u0915 \u0915\u093E \u0909\u092A\u092F\u094B\u0917 \u0915\u0930\u0924\u093E \u0939\u0948, \u0907\u0938\u0932\u093F\u090F \u0939\u092E \u0909\u0938\u0915\u093E \u0905\u0928\u0941\u0938\u0930\u0923 \u0915\u0930\u0947\u0902\u0917\u0947:"),Zs=c(),m(ue.$$.fragment),et=c(),m(_e.$$.fragment),st=c(),$e=p("h3"),Fa=l("\u091F\u094D\u0930\u0947\u0928\u093F\u0902\u0917 \u0932\u0942\u092A"),tt=c(),H=p("p"),Na=l("\u090F\u0915 \u0906\u0916\u093F\u0930\u0940 \u092C\u093E\u0924: \u0939\u092E GPU \u0915\u093E \u0909\u092A\u092F\u094B\u0917 \u0915\u0930\u0928\u093E \u091A\u093E\u0939\u0947\u0902\u0917\u0947 \u0905\u0917\u0930 \u0939\u092E\u093E\u0930\u0947 \u092A\u093E\u0938 \u090F\u0915 \u0915\u093E \u090F\u0915\u094D\u0938\u0947\u0938 \u0939\u0948 \u0924\u094B (CPU \u092A\u0930, \u092A\u094D\u0930\u0936\u093F\u0915\u094D\u0937\u0923 \u092E\u0947\u0902 \u0915\u0941\u091B \u092E\u093F\u0928\u091F\u094B\u0902 \u0915\u0947 \u092C\u091C\u093E\u092F \u0915\u0908 \u0918\u0902\u091F\u0947 \u0932\u0917 \u0938\u0915\u0924\u0947 \u0939\u0948\u0902)\u0964 \u0910\u0938\u093E \u0915\u0930\u0928\u0947 \u0915\u0947 \u0932\u093F\u090F, \u0939\u092E \u090F\u0915 "),fs=p("code"),Ha=l("device"),Ia=l(" \u0915\u094B \u092A\u0930\u093F\u092D\u093E\u0937\u093F\u0924 \u0915\u0930\u0947\u0902\u0917\u0947, \u091C\u093F\u0938 \u092A\u0930 \u0939\u092E \u0905\u092A\u0928\u0947 \u092E\u0949\u0921\u0932 \u0915\u094B \u0914\u0930 \u0905\u092A\u0928\u0947 \u092C\u0948\u091A\u094B\u0902 \u0915\u094B \u0930\u0916\u0947\u0902\u0917\u0947:"),at=c(),m(he.$$.fragment),lt=c(),m(be.$$.fragment),rt=c(),I=p("p"),Ba=l("\u0905\u092C \u0939\u092E \u092A\u094D\u0930\u0936\u093F\u0915\u094D\u0937\u0923 \u0915\u0947 \u0932\u093F\u090F \u0924\u0948\u092F\u093E\u0930 \u0939\u0948\u0902! \u092F\u0939 \u091C\u093E\u0928\u0928\u0947 \u0915\u0947 \u0932\u093F\u090F \u0915\u093F \u092A\u094D\u0930\u0936\u093F\u0915\u094D\u0937\u0923 \u0915\u092C \u0938\u092E\u093E\u092A\u094D\u0924 \u0939\u094B\u0917\u093E, \u0939\u092E "),ms=p("code"),Ra=l("tqdm"),Ja=l(" \u0932\u093E\u0907\u092C\u094D\u0930\u0947\u0930\u0940 \u0915\u093E \u0909\u092A\u092F\u094B\u0917 \u0915\u0930\u0915\u0947 \u0905\u092A\u0928\u0947 \u092A\u094D\u0930\u0936\u093F\u0915\u094D\u0937\u0923 \u091A\u0930\u0923\u094B\u0902 \u0915\u0940 \u0938\u0902\u0916\u094D\u092F\u093E \u092A\u0930 \u090F\u0915 \u092A\u094D\u0930\u0917\u0924\u093F \u092A\u091F\u094D\u091F\u0940 \u091C\u094B\u095C\u0947\u0917\u0947:"),ot=c(),m(ke.$$.fragment),pt=c(),Ge=p("p"),Ya=l("\u0906\u092A \u0926\u0947\u0916 \u0938\u0915\u0924\u0947 \u0939\u0948\u0902 \u0915\u093F \u092A\u094D\u0930\u0936\u093F\u0915\u094D\u0937\u0923 \u0932\u0942\u092A \u0915\u093E \u092E\u0942\u0932 \u091C\u094B \u092A\u0930\u093F\u091A\u092F \u092E\u0947\u0902 \u0939\u0948 \u0909\u0938\u0915\u0947 \u0938\u092E\u093E\u0928 \u0926\u093F\u0916\u0924\u093E \u0939\u0948\u0964 \u0939\u092E\u0928\u0947 \u0915\u094B\u0908 \u0930\u093F\u092A\u094B\u0930\u094D\u091F\u093F\u0902\u0917 \u0928\u0939\u0940\u0902 \u092E\u093E\u0902\u0917\u0940, \u0907\u0938\u0932\u093F\u090F \u092F\u0939 \u092A\u094D\u0930\u0936\u093F\u0915\u094D\u0937\u0923 \u0932\u0942\u092A \u0939\u092E\u0947\u0902 \u0907\u0938 \u092C\u093E\u0930\u0947 \u092E\u0947\u0902 \u0915\u0941\u091B \u0928\u0939\u0940\u0902 \u092C\u0924\u093E\u090F\u0917\u093E \u0915\u093F \u092E\u0949\u0921\u0932 \u0915\u093E \u0915\u093F\u0930\u093E\u092F\u093E \u0915\u0948\u0938\u093E \u0939\u0948\u0964 \u0939\u092E\u0947\u0902 \u0909\u0938\u0915\u0947 \u0932\u093F\u090F \u090F\u0915 \u092E\u0942\u0932\u094D\u092F\u093E\u0902\u0915\u0928 \u0932\u0942\u092A \u091C\u094B\u0921\u093C\u0928\u0947 \u0915\u0940 \u091C\u0930\u0942\u0930\u0924 \u0939\u0948\u0964"),nt=c(),je=p("h3"),Qa=l("\u092E\u0942\u0932\u094D\u092F\u093E\u0902\u0915\u0928 \u0932\u0942\u092A"),it=c(),z=p("p"),Ka=l("\u091C\u0948\u0938\u093E \u0915\u093F \u0939\u092E\u0928\u0947 \u092A\u0939\u0932\u0947 \u0915\u093F\u092F\u093E \u0925\u093E, \u0939\u092E \u{1F917} \u0921\u0947\u091F\u093E\u0938\u0947\u091F \u0932\u093E\u0907\u092C\u094D\u0930\u0947\u0930\u0940 \u0926\u094D\u0935\u093E\u0930\u093E \u092A\u094D\u0930\u0926\u093E\u0928 \u0915\u093F\u090F \u0917\u090F \u092E\u0940\u091F\u094D\u0930\u093F\u0915 \u0915\u093E \u0909\u092A\u092F\u094B\u0917 \u0915\u0930\u0947\u0902\u0917\u0947\u0964 \u0939\u092E \u092A\u0939\u0932\u0947 \u0939\u0940 "),ds=p("code"),Va=l("metric.compute()"),Xa=l(" \u0935\u093F\u0927\u093F \u0926\u0947\u0916 \u091A\u0941\u0915\u0947 \u0939\u0948\u0902, \u0932\u0947\u0915\u093F\u0928 \u092E\u0947\u091F\u094D\u0930\u093F\u0915\u094D\u0938 \u0935\u093E\u0938\u094D\u0924\u0935 \u092E\u0947\u0902 \u0939\u092E\u093E\u0930\u0947 \u0932\u093F\u090F \u092C\u0948\u091A \u091C\u092E\u093E \u0915\u0930 \u0938\u0915\u0924\u0947 \u0939\u0948\u0902 \u091C\u092C \u0939\u092E \u092D\u0935\u093F\u0937\u094D\u092F\u0935\u093E\u0923\u0940 \u0932\u0942\u092A \u092A\u0930 \u091C\u093E\u0924\u0947 \u0939\u0948\u0902 "),us=p("code"),Za=l("add_batch()"),el=l(" \u0935\u093F\u0927\u093F \u0915\u0947 \u0938\u093E\u0925 \u0964 \u090F\u0915 \u092C\u093E\u0930 \u091C\u092C \u0939\u092E \u0938\u092D\u0940 \u092C\u0948\u091A\u094B\u0902 \u0915\u094B \u091C\u092E\u093E \u0915\u0930 \u0932\u0947\u0924\u0947 \u0939\u0948\u0902, \u0924\u094B \u0939\u092E "),_s=p("code"),sl=l("metric.compute()"),tl=l(" \u0915\u0947 \u0938\u093E\u0925 \u0905\u0902\u0924\u093F\u092E \u092A\u0930\u093F\u0923\u093E\u092E \u092A\u094D\u0930\u093E\u092A\u094D\u0924 \u0915\u0930 \u0938\u0915\u0924\u0947 \u0939\u0948\u0902\u0964 \u092E\u0942\u0932\u094D\u092F\u093E\u0902\u0915\u0928 \u0932\u0942\u092A \u092E\u0947\u0902 \u0907\u0928 \u0938\u092D\u0940 \u0915\u094B \u0915\u093E\u0930\u094D\u092F\u093E\u0928\u094D\u0935\u093F\u0924 \u0915\u0930\u0928\u0947 \u0915\u093E \u0924\u0930\u0940\u0915\u093E \u092F\u0939\u093E\u0902 \u0926\u093F\u092F\u093E \u0917\u092F\u093E \u0939\u0948:"),ct=c(),m(ve.$$.fragment),ft=c(),m(we.$$.fragment),mt=c(),Fe=p("p"),al=l("\u092B\u093F\u0930 \u0938\u0947, \u092E\u0949\u0921\u0932 \u0939\u0947\u0921 \u0907\u0928\u093F\u0936\u093F\u092F\u0932\u093E\u0907\u091C\u093C\u0947\u0936\u0928 \u0914\u0930 \u0921\u0947\u091F\u093E \u092B\u0947\u0930\u092C\u0926\u0932 \u092E\u0947\u0902 \u0915\u094D\u0930\u092E\u0930\u0939\u093F\u0924 \u0939\u094B\u0928\u0947 \u0915\u0947 \u0915\u093E\u0930\u0923 \u0906\u092A\u0915\u0947 \u092A\u0930\u093F\u0923\u093E\u092E \u0925\u094B\u0921\u093C\u0947 \u092D\u093F\u0928\u094D\u0928 \u0939\u094B\u0902\u0917\u0947, \u0932\u0947\u0915\u093F\u0928 \u0935\u0947 \u090F\u0915 \u0939\u0940 \u092C\u0949\u0932\u092A\u093E\u0930\u094D\u0915 \u092E\u0947\u0902 \u0939\u094B\u0928\u0947 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\u0909\u092A\u092F\u094B\u0917 \u0915\u0930\u0915\u0947, \u092C\u0938 \u0915\u0941\u091B \u0938\u092E\u093E\u092F\u094B\u091C\u0928 \u0915\u0947 \u0938\u093E\u0925 \u0939\u092E \u0915\u0908 GPUs \u092F\u093E TPUs \u092A\u0930 \u0935\u093F\u0924\u0930\u093F\u0924 \u092A\u094D\u0930\u0936\u093F\u0915\u094D\u0937\u0923 \u0915\u094B \u0938\u0915\u094D\u0937\u092E \u0915\u0930 \u0938\u0915\u0924\u0947 \u0939\u0948\u0902\u0964 \u0936\u0941\u0930\u0941\u0906\u0924 \u092A\u094D\u0930\u0936\u093F\u0915\u094D\u0937\u0923 \u0914\u0930 \u0938\u0924\u094D\u092F\u093E\u092A\u0928 \u0921\u0947\u091F\u093E \u0932\u094B\u0921\u0930 \u0915\u0947 \u0928\u093F\u0930\u094D\u092E\u093E\u0923 \u0938\u0947 \u0939\u0941\u0908, \u092F\u0939\u093E\u0901 \u0939\u092E\u093E\u0930\u093E \u092E\u0948\u0928\u0941\u0905\u0932 \u092A\u094D\u0930\u0936\u093F\u0915\u094D\u0937\u0923 \u0932\u0942\u092A \u0915\u0948\u0938\u093E \u0926\u093F\u0916\u0924\u093E 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\u092C\u091C\u093E\u092F PyTorch \u091F\u0947\u0902\u0938\u0930 \u0932\u094C\u091F\u093E\u090F\u0902\u0964"),Ql.forEach(s),Je.forEach(s),Ts=f(e),W=n(e,"P",{});var Tt=i(W);_a=r(Tt,"\u0939\u092E\u093E\u0930\u0947 "),ls=n(Tt,"CODE",{});var Kl=i(ls);$a=r(Kl,"tokenized_datasets"),Kl.forEach(s),ha=r(Tt," \u092E\u0947\u0902 \u0909\u0928\u092E\u0947 \u0938\u0947 \u092A\u094D\u0930\u0924\u094D\u092F\u0947\u0915 \u091A\u0930\u0923 \u0915\u0947 \u0932\u093F\u090F \u090F\u0915 \u0935\u093F\u0927\u093F \u0939\u0948:"),Tt.forEach(s),Ss=f(e),d(le.$$.fragment,e),Us=f(e),Se=n(e,"P",{});var Vl=i(Se);ba=r(Vl,"\u0939\u092E \u092B\u093F\u0930 \u091C\u093E\u0902\u091A \u0938\u0915\u0924\u0947 \u0939\u0948\u0902 \u0915\u093F \u092A\u0930\u093F\u0923\u093E\u092E \u092E\u0947\u0902 \u0915\u0947\u0935\u0932 \u0915\u0949\u0932\u092E \u0939\u0948 \u091C\u093F\u0928\u094D\u0939\u0947\u0902 \u0939\u092E\u093E\u0930\u093E \u092E\u0949\u0921\u0932 \u0938\u094D\u0935\u0940\u0915\u093E\u0930 \u0915\u0930\u0947\u0917\u093E:"),Vl.forEach(s),Ls=f(e),d(re.$$.fragment,e),Ms=f(e),Ue=n(e,"P",{});var Xl=i(Ue);ka=r(Xl,"\u0905\u092C \u091C\u092C \u092F\u0939 \u0939\u094B \u0917\u092F\u093E \u0939\u0948, \u0924\u094B \u0939\u092E \u0906\u0938\u093E\u0928\u0940 \u0938\u0947 \u0905\u092A\u0928\u0947 \u0921\u0947\u091F\u093E\u0932\u094B\u0921\u0930\u094D\u0938 \u0915\u094B \u092A\u0930\u093F\u092D\u093E\u0937\u093F\u0924 \u0915\u0930 \u0938\u0915\u0924\u0947 \u0939\u0948\u0902:"),Xl.forEach(s),Ws=f(e),d(oe.$$.fragment,e),Gs=f(e),Le=n(e,"P",{});var Zl=i(Le);ja=r(Zl,"\u092F\u0939 \u091C\u093E\u0902\u091A\u0928\u0947 \u0915\u0947 \u0932\u093F\u090F \u0915\u093F \u0921\u0947\u091F\u093E \u092A\u094D\u0930\u094B\u0938\u0947\u0938\u093F\u0902\u0917 \u092E\u0947\u0902 \u0915\u094B\u0908 \u0917\u0932\u0924\u0940 \u0924\u094B \u0928\u0939\u0940\u0902 \u0939\u0948, \u0939\u092E \u0907\u0938 \u0924\u0930\u0939 \u090F\u0915 \u092C\u0948\u091A \u0915\u093E 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\u0924\u0915 \u092A\u0948\u0921\u093F\u0902\u0917 \u0915\u0930 \u0930\u0939\u0947 \u0939\u0948\u0902\u0964"),St.forEach(s),Is=f(e),Me=n(e,"P",{});var sr=i(Me);ya=r(sr,"\u0905\u092C \u091C\u092C\u0915\u093F \u0939\u092E \u0921\u0947\u091F\u093E \u092A\u094D\u0930\u0940\u092A\u094D\u0930\u094B\u0938\u0947\u0938\u093F\u0902\u0917 (\u090F\u0915 \u0938\u0902\u0924\u094B\u0937\u091C\u0928\u0915 \u0932\u0947\u0915\u093F\u0928 \u092E\u093E\u092F\u093E\u0935\u0940 \u0932\u0915\u094D\u0937\u094D\u092F \u0915\u093F\u0938\u0940 \u092D\u0940 ML \u092A\u094D\u0930\u0948\u0915\u094D\u091F\u093F\u0936\u0928\u0930 \u0915\u0947 \u0932\u093F\u090F) \u0915\u0947 \u0938\u093E\u0925 \u092A\u0942\u0930\u0940 \u0924\u0930\u0939 \u0938\u0947 \u0938\u092E\u093E\u092A\u094D\u0924 \u0915\u0930 \u091A\u0941\u0915\u0947 \u0939\u0948\u0902, \u0906\u0907\u090F \u092E\u0949\u0921\u0932 \u0915\u0940 \u0913\u0930 \u092E\u0941\u0921\u093C\u0947\u0902\u0964 \u0939\u092E \u0907\u0938\u0947 \u0920\u0940\u0915 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T=i(y);Pa=r(T,"\u0939\u092E \u0905\u092A\u0928\u093E \u092A\u094D\u0930\u0936\u093F\u0915\u094D\u0937\u0923 \u0932\u0942\u092A \u0932\u093F\u0916\u0928\u0947 \u0915\u0947 \u0932\u093F\u090F \u0932\u0917\u092D\u0917 \u0924\u0948\u092F\u093E\u0930 \u0939\u0948\u0902! \u0939\u092E \u0915\u0947\u0935\u0932 \u0926\u094B \u091A\u0940\u091C\u0947\u0902 \u0916\u094B \u0930\u0939\u0947 \u0939\u0948\u0902: \u090F\u0915 \u0911\u092A\u094D\u091F\u093F\u092E\u093E\u0907\u091C\u093C\u0930 \u0914\u0930 \u090F\u0915 \u0932\u0930\u094D\u0928\u093F\u0902\u0917 \u0930\u0947\u091F \u0905\u0928\u0941\u0938\u0942\u091A\u0915\u0964 \u091A\u0942\u0902\u0915\u093F "),ps=n(T,"CODE",{});var lr=i(ps);Aa=r(lr,"Trainer"),lr.forEach(s),Da=r(T," \u091C\u094B \u0915\u0930 \u0930\u0939\u093E \u0925\u093E \u0909\u0938\u0947 \u0939\u092E \u0916\u0941\u0926 \u0938\u0947 \u0926\u094B\u0939\u0930\u093E\u0928\u0947 \u0915\u0940 \u0915\u094B\u0936\u093F\u0936 \u0915\u0930 \u0930\u0939\u0947 \u0939\u0948\u0902, \u0924\u094B 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