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 <link rel="modulepreload" href="/docs/evaluate/main/en/_app/immutable/chunks/CodeBlock.dc1e8be0.js"><!-- HEAD_svelte-u9bgzb_START --><meta name="hf:doc:metadata" content="{&quot;title&quot;:&quot;🤗 Transformers&quot;,&quot;local&quot;:&quot;-transformers&quot;,&quot;sections&quot;:[{&quot;title&quot;:&quot;Trainer&quot;,&quot;local&quot;:&quot;trainer&quot;,&quot;sections&quot;:[],&quot;depth&quot;:2},{&quot;title&quot;:&quot;Seq2SeqTrainer&quot;,&quot;local&quot;:&quot;seq2seqtrainer&quot;,&quot;sections&quot;:[],&quot;depth&quot;:2}],&quot;depth&quot;:1}"><!-- HEAD_svelte-u9bgzb_END --> <p></p> <div class="items-center shrink-0 min-w-[100px] max-sm:min-w-[50px] justify-end ml-auto flex" style="float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"><div class="inline-flex rounded-md max-sm:rounded-sm"><button class="inline-flex items-center gap-1 h-7 max-sm:h-7 px-2 max-sm:px-1.5 text-sm font-medium text-gray-800 border border-r-0 rounded-l-md max-sm:rounded-l-sm border-gray-200 bg-white hover:shadow-inner dark:border-gray-850 dark:bg-gray-950 dark:text-gray-200 dark:hover:bg-gray-800" aria-live="polite"><span class="inline-flex items-center justify-center rounded-md p-0.5 max-sm:p-0 hover:text-gray-800 dark:hover:text-gray-200"><svg class="sm:size-3.5 size-3" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg></span> <span>Copy page</span></button> <button class="inline-flex items-center justify-center w-6 max-sm:w-5 h-7 max-sm:h-7 disabled:pointer-events-none text-sm text-gray-500 hover:text-gray-700 dark:hover:text-white rounded-r-md max-sm:rounded-r-sm border border-l transition border-gray-200 bg-white hover:shadow-inner dark:border-gray-850 dark:bg-gray-950 dark:text-gray-200 dark:hover:bg-gray-800" aria-haspopup="menu" aria-expanded="false" aria-label="Open copy menu"><svg class="transition-transform text-gray-400 overflow-visible sm:size-3.5 size-3 rotate-0" width="1em" height="1em" viewBox="0 0 12 7" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M1 1L6 6L11 1" stroke="currentColor"></path></svg></button></div> </div> <h1 class="relative group"><a id="-transformers" 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="#-transformers"><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>🤗 Transformers</span></h1> <p data-svelte-h="svelte-hhpzuu">To run the 🤗 Transformers examples make sure you have installed the following libraries:</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START -->pip install datasets transformers torch evaluate nltk rouge_score<!-- HTML_TAG_END --></pre></div> <h2 class="relative group"><a id="trainer" 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="#trainer"><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>Trainer</span></h2> <p data-svelte-h="svelte-yjqw5c">The metrics in <code>evaluate</code> can be easily integrated with the <a href="https://huggingface.co/docs/transformers/main/en/main_classes/trainer#transformers.Trainer" rel="nofollow">Trainer</a>. The <code>Trainer</code> accepts a <code>compute_metrics</code> keyword argument that passes a function to compute metrics. One can specify the evaluation interval with <code>eval_strategy</code> in the <code>TrainerArguments</code>, and based on that, the model is evaluated accordingly, and the predictions and labels passed to <code>compute_metrics</code>.</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START --><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, AutoModelForSequenceClassification, TrainingArguments, Trainer
<span class="hljs-keyword">import</span> numpy <span class="hljs-keyword">as</span> np
<span class="hljs-keyword">import</span> evaluate
<span class="hljs-comment"># Prepare and tokenize dataset</span>
dataset = load_dataset(<span class="hljs-string">&quot;yelp_review_full&quot;</span>)
tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-keyword">def</span> <span class="hljs-title function_">tokenize_function</span>(<span class="hljs-params">examples</span>):
 <span class="hljs-keyword">return</span> tokenizer(examples[<span class="hljs-string">&quot;text&quot;</span>], padding=<span class="hljs-string">&quot;max_length&quot;</span>, truncation=<span class="hljs-literal">True</span>)
tokenized_datasets = dataset.<span class="hljs-built_in">map</span>(tokenize_function, batched=<span class="hljs-literal">True</span>)
small_train_dataset = tokenized_datasets[<span class="hljs-string">&quot;train&quot;</span>].shuffle(seed=<span class="hljs-number">42</span>).select(<span class="hljs-built_in">range</span>(<span class="hljs-number">200</span>))
small_eval_dataset = tokenized_datasets[<span class="hljs-string">&quot;test&quot;</span>].shuffle(seed=<span class="hljs-number">42</span>).select(<span class="hljs-built_in">range</span>(<span class="hljs-number">200</span>))
<span class="hljs-comment"># Setup evaluation </span>
metric = evaluate.load(<span class="hljs-string">&quot;accuracy&quot;</span>)
<span class="hljs-keyword">def</span> <span class="hljs-title function_">compute_metrics</span>(<span class="hljs-params">eval_pred</span>):
 logits, labels = eval_pred
 predictions = np.argmax(logits, axis=-<span class="hljs-number">1</span>)
 <span class="hljs-keyword">return</span> metric.compute(predictions=predictions, references=labels)
<span class="hljs-comment"># Load pretrained model and evaluate model after each epoch</span>
model = AutoModelForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, num_labels=<span class="hljs-number">5</span>)
training_args = TrainingArguments(output_dir=<span class="hljs-string">&quot;test_trainer&quot;</span>, eval_strategy=<span class="hljs-string">&quot;epoch&quot;</span>)
trainer = Trainer(
 model=model,
 args=training_args,
 train_dataset=small_train_dataset,
 eval_dataset=small_eval_dataset,
 compute_metrics=compute_metrics,
)
trainer.train()<!-- HTML_TAG_END --></pre></div> <h2 class="relative group"><a id="seq2seqtrainer" 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="#seq2seqtrainer"><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>Seq2SeqTrainer</span></h2> <p data-svelte-h="svelte-1ag38mq">We can use the <a href="https://huggingface.co/docs/transformers/main/en/main_classes/trainer#transformers.Seq2SeqTrainer" rel="nofollow">Seq2SeqTrainer</a> for sequence-to-sequence tasks such as translation or summarization. For such generative tasks usually metrics such as ROUGE or BLEU are evaluated. However, these metrics require that we generate some text with the model rather than a single forward pass as with e.g. classification. The <code>Seq2SeqTrainer</code> allows for the use of the generate method when setting <code>predict_with_generate=True</code> which will generate text for each sample in the evaluation set. That means we evaluate generated text within the <code>compute_metric</code> function. We just need to decode the predictions and labels first.</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START --><span class="hljs-keyword">import</span> nltk
<span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset
<span class="hljs-keyword">import</span> evaluate
<span class="hljs-keyword">import</span> numpy <span class="hljs-keyword">as</span> np
<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, DataCollatorForSeq2Seq
<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForSeq2SeqLM, Seq2SeqTrainingArguments, Seq2SeqTrainer
<span class="hljs-comment"># Prepare and tokenize dataset</span>
billsum = load_dataset(<span class="hljs-string">&quot;billsum&quot;</span>, split=<span class="hljs-string">&quot;ca_test&quot;</span>).shuffle(seed=<span class="hljs-number">42</span>).select(<span class="hljs-built_in">range</span>(<span class="hljs-number">200</span>))
billsum = billsum.train_test_split(test_size=<span class="hljs-number">0.2</span>)
tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;t5-small&quot;</span>)
prefix = <span class="hljs-string">&quot;summarize: &quot;</span>
<span class="hljs-keyword">def</span> <span class="hljs-title function_">preprocess_function</span>(<span class="hljs-params">examples</span>):
 inputs = [prefix + doc <span class="hljs-keyword">for</span> doc <span class="hljs-keyword">in</span> examples[<span class="hljs-string">&quot;text&quot;</span>]]
 model_inputs = tokenizer(inputs, max_length=<span class="hljs-number">1024</span>, truncation=<span class="hljs-literal">True</span>)
labels = tokenizer(text_target=examples[<span class="hljs-string">&quot;summary&quot;</span>], max_length=<span class="hljs-number">128</span>, truncation=<span class="hljs-literal">True</span>)
model_inputs[<span class="hljs-string">&quot;labels&quot;</span>] = labels[<span class="hljs-string">&quot;input_ids&quot;</span>]
 <span class="hljs-keyword">return</span> model_inputs
tokenized_billsum = billsum.<span class="hljs-built_in">map</span>(preprocess_function, batched=<span class="hljs-literal">True</span>)
<span class="hljs-comment"># Setup evaluation</span>
nltk.download(<span class="hljs-string">&quot;punkt_tab&quot;</span>, quiet=<span class="hljs-literal">True</span>)
metric = evaluate.load(<span class="hljs-string">&quot;rouge&quot;</span>)
<span class="hljs-keyword">def</span> <span class="hljs-title function_">compute_metrics</span>(<span class="hljs-params">eval_preds</span>):
 preds, labels = eval_preds
<span class="hljs-comment"># decode preds and labels</span>
 labels = np.where(labels != -<span class="hljs-number">100</span>, labels, tokenizer.pad_token_id)
 decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=<span class="hljs-literal">True</span>)
 decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=<span class="hljs-literal">True</span>)
<span class="hljs-comment"># rougeLSum expects newline after each sentence</span>
 decoded_preds = [<span class="hljs-string">&quot;\n&quot;</span>.join(nltk.sent_tokenize(pred.strip())) <span class="hljs-keyword">for</span> pred <span class="hljs-keyword">in</span> decoded_preds]
 decoded_labels = [<span class="hljs-string">&quot;\n&quot;</span>.join(nltk.sent_tokenize(label.strip())) <span class="hljs-keyword">for</span> label <span class="hljs-keyword">in</span> decoded_labels]
result = metric.compute(predictions=decoded_preds, references=decoded_labels, use_stemmer=<span class="hljs-literal">True</span>)
 <span class="hljs-keyword">return</span> result
<span class="hljs-comment"># Load pretrained model and evaluate model after each epoch</span>
model = AutoModelForSeq2SeqLM.from_pretrained(<span class="hljs-string">&quot;t5-small&quot;</span>)
data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=model)
training_args = Seq2SeqTrainingArguments(
 output_dir=<span class="hljs-string">&quot;./results&quot;</span>,
 eval_strategy=<span class="hljs-string">&quot;epoch&quot;</span>,
 learning_rate=<span class="hljs-number">2e-5</span>,
 per_device_train_batch_size=<span class="hljs-number">16</span>,
 per_device_eval_batch_size=<span class="hljs-number">4</span>,
 weight_decay=<span class="hljs-number">0.01</span>,
 save_total_limit=<span class="hljs-number">3</span>,
 num_train_epochs=<span class="hljs-number">2</span>,
 fp16=<span class="hljs-literal">True</span>,
 predict_with_generate=<span class="hljs-literal">True</span>
)
trainer = Seq2SeqTrainer(
 model=model,
 args=training_args,
 train_dataset=tokenized_billsum[<span class="hljs-string">&quot;train&quot;</span>],
 eval_dataset=tokenized_billsum[<span class="hljs-string">&quot;test&quot;</span>],
 tokenizer=tokenizer,
 data_collator=data_collator,
 compute_metrics=compute_metrics
)
trainer.train()<!-- HTML_TAG_END --></pre></div> <p data-svelte-h="svelte-1x0y3m4">You can use any <code>evaluate</code> metric with the <code>Trainer</code> and <code>Seq2SeqTrainer</code> as long as they are compatible with the task and predictions. In case you don’t want to train a model but just evaluate an existing model you can replace <code>trainer.train()</code> with <code>trainer.evaluate()</code> in the above scripts.</p> <a class="!text-gray-400 !no-underline text-sm flex items-center not-prose mt-4" href="https://github.com/huggingface/evaluate/blob/main/docs/source/transformers_integrations.mdx" target="_blank"><svg class="mr-1" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M31,16l-7,7l-1.41-1.41L28.17,16l-5.58-5.59L24,9l7,7z"></path><path d="M1,16l7-7l1.41,1.41L3.83,16l5.58,5.59L8,23l-7-7z"></path><path d="M12.419,25.484L17.639,6.552l1.932,0.518L14.351,26.002z"></path></svg> <span data-svelte-h="svelte-zjs2n5"><span class="underline">Update</span> on GitHub</span></a> <p></p>
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