import{s as tt,o as ot,n as lt}from"../chunks/scheduler.d586627e.js";import{S as nt,i as at,g as r,s as n,r as m,A as rt,h as s,f as o,c as a,j as Ke,u as c,x as p,k as et,y as st,a as l,v as T,d as f,t as d,w as U}from"../chunks/index.8589a59c.js";import{T as pt}from"../chunks/Tip.84e2336e.js";import{C as ee}from"../chunks/CodeBlock.dffc9a5b.js";import{H as J,E as it}from"../chunks/index.b825d7e5.js";function mt(oe){let i,$=`A partir da versão 2.3.0 o script de conversão agora faz parte do transformers CLI (<strong>transformers-cli</strong>) disponível em qualquer instalação
transformers &gt;= 2.3.0.`,h,_,b="A documentação abaixo reflete o formato do comando <strong>transformers-cli convert</strong>.";return{c(){i=r("p"),i.innerHTML=$,h=n(),_=r("p"),_.innerHTML=b},l(u){i=s(u,"P",{"data-svelte-h":!0}),p(i)!=="svelte-opr9ez"&&(i.innerHTML=$),h=a(u),_=s(u,"P",{"data-svelte-h":!0}),p(_)!=="svelte-12vk8vx"&&(_.innerHTML=b)},m(u,y){l(u,i,y),l(u,h,y),l(u,_,y)},p:lt,d(u){u&&(o(i),o(h),o(_))}}}function ct(oe){let i,$,h,_,b,u,y,Xe=`Uma interface de linha de comando é fornecida para converter os checkpoints originais Bert/GPT/GPT-2/Transformer-XL/XLNet/XLM em modelos
que podem ser carregados usando os métodos <code>from_pretrained</code> da biblioteca.`,le,M,ne,w,ae,C,ke=`Você pode converter qualquer checkpoint do BERT em TensorFlow (em particular <a href="https://github.com/google-research/bert#pre-trained-models" rel="nofollow">os modelos pré-treinados lançados pelo Google</a>) em um arquivo PyTorch usando um
<a href="https://github.com/huggingface/transformers/tree/main/src/transformers/models/bert/convert_bert_original_tf_checkpoint_to_pytorch.py" rel="nofollow">convert_bert_original_tf_checkpoint_to_pytorch.py</a> script.`,re,v,Se=`Esta Interface de Linha de Comando (CLI) recebe como entrada um checkpoint do TensorFlow (três arquivos começando com <code>bert_model.ckpt</code>) e o
arquivo de configuração (<code>bert_config.json</code>), e então cria um modelo PyTorch para esta configuração, carrega os pesos
do checkpoint do TensorFlow no modelo PyTorch e salva o modelo resultante em um arquivo PyTorch que pode
ser importado usando <code>from_pretrained()</code> (veja o exemplo em <a href="quicktour">quicktour</a> , <a href="https://github.com/huggingface/transformers/tree/main/examples/pytorch/text-classification/run_glue.py" rel="nofollow">run_glue.py</a> ).`,se,V,Le=`Você só precisa executar este script de conversão <strong>uma vez</strong> para obter um modelo PyTorch. Você pode então desconsiderar o checkpoint em
TensorFlow (os três arquivos começando com <code>bert_model.ckpt</code>), mas certifique-se de manter o arquivo de configuração (\\
<code>bert_config.json</code>) e o arquivo de vocabulário (<code>vocab.txt</code>), pois eles também são necessários para o modelo PyTorch.`,pe,R,Ze="Para executar este script de conversão específico, você precisará ter o TensorFlow e o PyTorch instalados (<code>pip install tensorflow</code>). O resto do repositório requer apenas o PyTorch.",ie,E,Qe="Aqui está um exemplo do processo de conversão para um modelo <code>BERT-Base Uncased</code> pré-treinado:",me,F,ce,g,Ae='Você pode baixar os modelos pré-treinados do Google para a conversão <a href="https://github.com/google-research/bert#pre-trained-models" rel="nofollow">aqui</a>.',Te,I,fe,P,je=`Converta os checkpoints do modelo ALBERT em TensorFlow para PyTorch usando o
<a href="https://github.com/huggingface/transformers/tree/main/src/transformers/models/albert/convert_albert_original_tf_checkpoint_to_pytorch.py" rel="nofollow">convert_albert_original_tf_checkpoint_to_pytorch.py</a> script.`,de,B,He=`A Interface de Linha de Comando (CLI) recebe como entrada um checkpoint do TensorFlow (três arquivos começando com <code>model.ckpt-best</code>) e o
arquivo de configuração (<code>albert_config.json</code>), então cria e salva um modelo PyTorch. Para executar esta conversão, você
precisa ter o TensorFlow e o PyTorch instalados.`,Ue,N,xe="Aqui está um exemplo do processo de conversão para o modelo <code>ALBERT Base</code> pré-treinado:",ue,X,_e,k,Ge='Você pode baixar os modelos pré-treinados do Google para a conversão <a href="https://github.com/google-research/albert#pre-trained-models" rel="nofollow">aqui</a>.',ye,S,he,L,Oe=`Aqui está um exemplo do processo de conversão para um modelo OpenAI GPT pré-treinado, supondo que seu checkpoint NumPy
foi salvo com o mesmo formato do modelo pré-treinado OpenAI (veja <a href="https://github.com/openai/finetune-transformer-lm" rel="nofollow">aqui</a>\\
)`,be,Z,Me,Q,Je,A,We='Aqui está um exemplo do processo de conversão para um modelo OpenAI GPT-2 pré-treinado (consulte <a href="https://github.com/openai/gpt-2" rel="nofollow">aqui</a>)',$e,j,we,H,Ce,x,Ye="Aqui está um exemplo do processo de conversão para um modelo XLNet pré-treinado:",ve,G,Ve,O,Re,W,De="Aqui está um exemplo do processo de conversão para um modelo XLM pré-treinado:",Ee,Y,Fe,D,ge,q,qe="Aqui está um exemplo do processo de conversão para um modelo T5 pré-treinado:",Ie,z,Pe,K,Be,te,Ne;return b=new J({props:{title:"Convertendo checkpoints do TensorFlow para Pytorch",local:"convertendo-checkpoints-do-tensorflow-para-pytorch",headingTag:"h1"}}),M=new pt({props:{$$slots:{default:[mt]},$$scope:{ctx:oe}}}),w=new J({props:{title:"BERT",local:"bert",headingTag:"h2"}}),F=new ee({props:{code:"ZXhwb3J0JTIwQkVSVF9CQVNFX0RJUiUzRCUyRnBhdGglMkZ0byUyRmJlcnQlMkZ1bmNhc2VkX0wtMTJfSC03NjhfQS0xMiUwQSUwQXRyYW5zZm9ybWVycy1jbGklMjBjb252ZXJ0JTIwLS1tb2RlbF90eXBlJTIwYmVydCUyMCU1QyUwQSUyMCUyMC0tdGZfY2hlY2twb2ludCUyMCUyNEJFUlRfQkFTRV9ESVIlMkZiZXJ0X21vZGVsLmNrcHQlMjAlNUMlMEElMjAlMjAtLWNvbmZpZyUyMCUyNEJFUlRfQkFTRV9ESVIlMkZiZXJ0X2NvbmZpZy5qc29uJTIwJTVDJTBBJTIwJTIwLS1weXRvcmNoX2R1bXBfb3V0cHV0JTIwJTI0QkVSVF9CQVNFX0RJUiUyRnB5dG9yY2hfbW9kZWwuYmlu",highlighted:`<span class="hljs-built_in">export</span> BERT_BASE_DIR=/path/to/bert/uncased_L-12_H-768_A-12

transformers-cli convert --model_type bert \\
  --tf_checkpoint <span class="hljs-variable">$BERT_BASE_DIR</span>/bert_model.ckpt \\
  --config <span class="hljs-variable">$BERT_BASE_DIR</span>/bert_config.json \\
  --pytorch_dump_output <span class="hljs-variable">$BERT_BASE_DIR</span>/pytorch_model.bin`,wrap:!1}}),I=new J({props:{title:"ALBERT",local:"albert",headingTag:"h2"}}),X=new ee({props:{code:"ZXhwb3J0JTIwQUxCRVJUX0JBU0VfRElSJTNEJTJGcGF0aCUyRnRvJTJGYWxiZXJ0JTJGYWxiZXJ0X2Jhc2UlMEElMEF0cmFuc2Zvcm1lcnMtY2xpJTIwY29udmVydCUyMC0tbW9kZWxfdHlwZSUyMGFsYmVydCUyMCU1QyUwQSUyMCUyMC0tdGZfY2hlY2twb2ludCUyMCUyNEFMQkVSVF9CQVNFX0RJUiUyRm1vZGVsLmNrcHQtYmVzdCUyMCU1QyUwQSUyMCUyMC0tY29uZmlnJTIwJTI0QUxCRVJUX0JBU0VfRElSJTJGYWxiZXJ0X2NvbmZpZy5qc29uJTIwJTVDJTBBJTIwJTIwLS1weXRvcmNoX2R1bXBfb3V0cHV0JTIwJTI0QUxCRVJUX0JBU0VfRElSJTJGcHl0b3JjaF9tb2RlbC5iaW4=",highlighted:`<span class="hljs-built_in">export</span> ALBERT_BASE_DIR=/path/to/albert/albert_base

transformers-cli convert --model_type albert \\
  --tf_checkpoint <span class="hljs-variable">$ALBERT_BASE_DIR</span>/model.ckpt-best \\
  --config <span class="hljs-variable">$ALBERT_BASE_DIR</span>/albert_config.json \\
  --pytorch_dump_output <span class="hljs-variable">$ALBERT_BASE_DIR</span>/pytorch_model.bin`,wrap:!1}}),S=new J({props:{title:"OpenAI GPT",local:"openai-gpt",headingTag:"h2"}}),Z=new ee({props:{code:"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",highlighted:`<span class="hljs-built_in">export</span> OPENAI_GPT_CHECKPOINT_FOLDER_PATH=/path/to/openai/pretrained/numpy/weights

transformers-cli convert --model_type gpt \\
  --tf_checkpoint <span class="hljs-variable">$OPENAI_GPT_CHECKPOINT_FOLDER_PATH</span> \\
  --pytorch_dump_output <span class="hljs-variable">$PYTORCH_DUMP_OUTPUT</span> \\
  [--config OPENAI_GPT_CONFIG] \\
  [--finetuning_task_name OPENAI_GPT_FINETUNED_TASK] \\`,wrap:!1}}),Q=new J({props:{title:"OpenAI GPT-2",local:"openai-gpt-2",headingTag:"h2"}}),j=new ee({props:{code:"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",highlighted:`<span class="hljs-built_in">export</span> OPENAI_GPT2_CHECKPOINT_PATH=/path/to/openai-community/gpt2/pretrained/weights

transformers-cli convert --model_type gpt2 \\
  --tf_checkpoint <span class="hljs-variable">$OPENAI_GPT2_CHECKPOINT_PATH</span> \\
  --pytorch_dump_output <span class="hljs-variable">$PYTORCH_DUMP_OUTPUT</span> \\
  [--config OPENAI_GPT2_CONFIG] \\
  [--finetuning_task_name OPENAI_GPT2_FINETUNED_TASK]`,wrap:!1}}),H=new J({props:{title:"XLNet",local:"xlnet",headingTag:"h2"}}),G=new ee({props:{code:"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",highlighted:`<span class="hljs-built_in">export</span> TRANSFO_XL_CHECKPOINT_PATH=/path/to/xlnet/checkpoint
<span class="hljs-built_in">export</span> TRANSFO_XL_CONFIG_PATH=/path/to/xlnet/config

transformers-cli convert --model_type xlnet \\
  --tf_checkpoint <span class="hljs-variable">$TRANSFO_XL_CHECKPOINT_PATH</span> \\
  --config <span class="hljs-variable">$TRANSFO_XL_CONFIG_PATH</span> \\
  --pytorch_dump_output <span class="hljs-variable">$PYTORCH_DUMP_OUTPUT</span> \\
  [--finetuning_task_name XLNET_FINETUNED_TASK] \\`,wrap:!1}}),O=new J({props:{title:"XLM",local:"xlm",headingTag:"h2"}}),Y=new ee({props:{code:"ZXhwb3J0JTIwWExNX0NIRUNLUE9JTlRfUEFUSCUzRCUyRnBhdGglMkZ0byUyRnhsbSUyRmNoZWNrcG9pbnQlMEElMEF0cmFuc2Zvcm1lcnMtY2xpJTIwY29udmVydCUyMC0tbW9kZWxfdHlwZSUyMHhsbSUyMCU1QyUwQSUyMCUyMC0tdGZfY2hlY2twb2ludCUyMCUyNFhMTV9DSEVDS1BPSU5UX1BBVEglMjAlNUMlMEElMjAlMjAtLXB5dG9yY2hfZHVtcF9vdXRwdXQlMjAlMjRQWVRPUkNIX0RVTVBfT1VUUFVUJTBBJTIwJTVCLS1jb25maWclMjBYTUxfQ09ORklHJTVEJTIwJTVDJTBBJTIwJTVCLS1maW5ldHVuaW5nX3Rhc2tfbmFtZSUyMFhNTF9GSU5FVFVORURfVEFTSyU1RA==",highlighted:`<span class="hljs-built_in">export</span> XLM_CHECKPOINT_PATH=/path/to/xlm/checkpoint

transformers-cli convert --model_type xlm \\
  --tf_checkpoint <span class="hljs-variable">$XLM_CHECKPOINT_PATH</span> \\
  --pytorch_dump_output <span class="hljs-variable">$PYTORCH_DUMP_OUTPUT</span>
 [--config XML_CONFIG] \\
 [--finetuning_task_name XML_FINETUNED_TASK]`,wrap:!1}}),D=new J({props:{title:"T5",local:"t5",headingTag:"h2"}}),z=new ee({props:{code:"ZXhwb3J0JTIwVDUlM0QlMkZwYXRoJTJGdG8lMkZ0NSUyRnVuY2FzZWRfTC0xMl9ILTc2OF9BLTEyJTBBJTBBdHJhbnNmb3JtZXJzLWNsaSUyMGNvbnZlcnQlMjAtLW1vZGVsX3R5cGUlMjB0NSUyMCU1QyUwQSUyMCUyMC0tdGZfY2hlY2twb2ludCUyMCUyNFQ1JTJGdDVfbW9kZWwuY2twdCUyMCU1QyUwQSUyMCUyMC0tY29uZmlnJTIwJTI0VDUlMkZ0NV9jb25maWcuanNvbiUyMCU1QyUwQSUyMCUyMC0tcHl0b3JjaF9kdW1wX291dHB1dCUyMCUyNFQ1JTJGcHl0b3JjaF9tb2RlbC5iaW4=",highlighted:`<span class="hljs-built_in">export</span> T5=/path/to/t5/uncased_L-12_H-768_A-12

transformers-cli convert --model_type t5 \\
  --tf_checkpoint <span class="hljs-variable">$T5</span>/t5_model.ckpt \\
  --config <span class="hljs-variable">$T5</span>/t5_config.json \\
  --pytorch_dump_output <span class="hljs-variable">$T5</span>/pytorch_model.bin`,wrap:!1}}),K=new 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