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<h1 class="relative group"><a id="pretraining-transformers-with-optimum-habana" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#pretraining-transformers-with-optimum-habana"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
	<span>Pretraining Transformers with Optimum Habana
	</span></h1>

<p>Pretraining a model from Transformers, like BERT, is as easy as fine-tuning it.
The model should be instantiated from a configuration with <code>.from_config</code> and not from a pretrained checkpoint with <code>.from_pretrained</code>.
Here is how it should look like with GPT2 for instance:</p>

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	Copied</div></button></div>
	<pre><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForXXX

config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;gpt2&quot;</span>)
model = AutoModelForXXX.from_config(config)<!-- HTML_TAG_END --></pre></div>
<p>with XXX the task to perform, such as <code>ImageClassification</code> for example.</p>
<p>The following is a working example where BERT is pretrained for masked language modeling:</p>

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	Copied</div></button></div>
	<pre><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset
<span class="hljs-keyword">from</span> optimum.habana <span class="hljs-keyword">import</span> GaudiTrainer, GaudiTrainingArguments
<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForMaskedLM, AutoTokenizer, DataCollatorForLanguageModeling

<span class="hljs-comment"># Load the training set (this one has already been preprocessed)</span>
training_set = load_dataset(<span class="hljs-string">&quot;philschmid/processed_bert_dataset&quot;</span>, split=<span class="hljs-string">&quot;train&quot;</span>)
<span class="hljs-comment"># Load the tokenizer</span>
tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;philschmid/bert-base-uncased-2022-habana&quot;</span>)

<span class="hljs-comment"># Instantiate an untrained model</span>
config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-uncased&quot;</span>)
model = AutoModelForMaskedLM.from_config(config)

model.resize_token_embeddings(<span class="hljs-built_in">len</span>(tokenizer))

<span class="hljs-comment"># The data collator will take care of randomly masking the tokens</span>
data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer)

training_args = GaudiTrainingArguments(
    output_dir=<span class="hljs-string">&quot;/tmp/bert-base-uncased-mlm&quot;</span>,
    num_train_epochs=<span class="hljs-number">1</span>,
    per_device_train_batch_size=<span class="hljs-number">8</span>,
    use_habana=<span class="hljs-literal">True</span>,
    use_lazy_mode=<span class="hljs-literal">True</span>,
    gaudi_config_name=<span class="hljs-string">&quot;Habana/bert-base-uncased&quot;</span>,
)

<span class="hljs-comment"># Initialize our Trainer</span>
trainer = GaudiTrainer(
    model=model,
    args=training_args,
    train_dataset=training_set,
    tokenizer=tokenizer,
    data_collator=data_collator,
)

trainer.train()<!-- HTML_TAG_END --></pre></div>
<p>You can see another example of pretraining in <a href="https://huggingface.co/blog/pretraining-bert" rel="nofollow">this blog post</a>.</p>


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