course/pr_1213/es/_app/immutable/nodes/83.4bc5fd09.js

import{s as Qe,o as Ne}from"../chunks/scheduler.505acc25.js";import{S as He,i as Ye,e as f,s as r,c as d,h as Se,a as b,d as s,b as i,f as Ve,g as m,j as h,k as Be,l as qe,m as n,n as u,o as p,E as xe,t as c,p as M,F as Ae}from"../chunks/index.e22abd30.js";import{C as De,H as K,E as Le}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.a144e953.js";import{Y as Pe}from"../chunks/Youtube.7545e4b1.js";import{C as L}from"../chunks/CodeBlock.f6688f67.js";import{C as Fe}from"../chunks/CourseFloatingBanner.f0a2dc21.js";import{F as Ke}from"../chunks/FrameworkSwitchCourse.c2af54e8.js";function Oe(J){let a,o;return a=new Fe({props:{chapter:7,classNames:"absolute z-10 right-0 top-0",notebooks:[{label:"Google Colab",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/master/course/en/chapter7/section6_tf.ipynb"},{label:"Aws Studio",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/course/en/chapter7/section6_tf.ipynb"}]}}),{c(){d(a.$$.fragment)},l(l){m(a.$$.fragment,l)},m(l,y){u(a,l,y),o=!0},i(l){o||(c(a.$$.fragment,l),o=!0)},o(l){p(a.$$.fragment,l),o=!1},d(l){M(a,l)}}}function et(J){let a,o;return a=new Fe({props:{chapter:7,classNames:"absolute z-10 right-0 top-0",notebooks:[{label:"Google Colab",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/master/course/en/chapter7/section6_pt.ipynb"},{label:"Aws Studio",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/course/en/chapter7/section6_pt.ipynb"}]}}),{c(){d(a.$$.fragment)},l(l){m(a.$$.fragment,l)},m(l,y){u(a,l,y),o=!0},i(l){o||(c(a.$$.fragment,l),o=!0)},o(l){p(a.$$.fragment,l),o=!1},d(l){M(a,l)}}}function tt(J){let a,o;return a=new L({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFGPT2LMHeadModel, AutoConfig

config = AutoConfig.from_pretrained(
    <span class="hljs-string">&quot;gpt2&quot;</span>,
    vocab_size=<span class="hljs-built_in">len</span>(tokenizer),
    n_ctx=context_length,
    bos_token_id=tokenizer.bos_token_id,
    eos_token_id=tokenizer.eos_token_id,
)

model = TFGPT2LMHeadModel(config)`,wrap:!1}}),{c(){d(a.$$.fragment)},l(l){m(a.$$.fragment,l)},m(l,y){u(a,l,y),o=!0},i(l){o||(c(a.$$.fragment,l),o=!0)},o(l){p(a.$$.fragment,l),o=!1},d(l){M(a,l)}}}function lt(J){let a,o;return a=new L({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> GPT2LMHeadModel, AutoConfig

config = AutoConfig.from_pretrained(
    <span class="hljs-string">&quot;gpt2&quot;</span>,
    vocab_size=<span class="hljs-built_in">len</span>(tokenizer),
    n_ctx=context_length,
    bos_token_id=tokenizer.bos_token_id,
    eos_token_id=tokenizer.eos_token_id,
)

model = GPT2LMHeadModel(config)`,wrap:!1}}),{c(){d(a.$$.fragment)},l(l){m(a.$$.fragment,l)},m(l,y){u(a,l,y),o=!0},i(l){o||(c(a.$$.fragment,l),o=!0)},o(l){p(a.$$.fragment,l),o=!1},d(l){M(a,l)}}}function at(J){let a,o,l,y,j,O,$,ee,_,te,w,g,q,k,je="Hasta ahora hemos partido de modelos preentrenados. En esta sección seguiremos el camino opuesto: entrenaremos desde cero un modelo de lenguaje causal para completar código Python.",le,C,ae,I,$e="Nos centraremos en un subconjunto del stack de ciencia de datos en Python para construir una versión reducida de un modelo de autocompletado.",se,Z,ne,R,_e="Partimos de <code>codeparrot</code>, un gran corpus de código Python. Como el conjunto completo es enorme, lo filtramos para quedarnos con archivos que usen librerías como <code>pandas</code>, <code>sklearn</code>, <code>matplotlib</code> y <code>seaborn</code>.",oe,v,re,G,ke="Luego cargamos el subconjunto ya filtrado:",ie,W,pe,E,ce,z,Ce="Tokenizamos el código y lo dividimos en ventanas cortas de contexto:",de,X,me,V,ue,B,Ie="Usaremos una configuración similar a GPT-2 pequeño:",Me,T,U,D,x,ye,A,Ze="Para modelado causal usamos <code>DataCollatorForLanguageModeling</code> con <code>mlm=False</code>:",fe,F,be,Q,Re="En PyTorch puede entrenarse con <code>Trainer</code>; en TensorFlow, con <code>prepare_tf_dataset()</code> y <code>model.fit()</code>.",Je,N,ve="La idea central es simple: el modelo aprende a predecir el siguiente token de código a partir del contexto anterior. Con suficientes datos y cómputo, este enfoque produce modelos capaces de autocompletar código con bastante utilidad práctica.",we,H,ge,P,Te;j=new Ke({props:{fw:J[0]}}),$=new De({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),_=new K({props:{title:"Entrenar un modelo de lenguaje causal desde cero",local:"training-a-causal-language-model-from-scratch",headingTag:"h1"}});const Ge=[et,Oe],Y=[];function We(e,t){return e[0]==="pt"?0:1}w=We(J),g=Y[w]=Ge[w](J),C=new Pe({props:{id:"Vpjb1lu0MDk"}}),Z=new K({props:{title:"Recopilar los datos",local:"gathering-the-data",headingTag:"h2"}}),v=new L({props:{code:"ZGVmJTIwYW55X2tleXdvcmRfaW5fc3RyaW5nKHN0cmluZyUyQyUyMGtleXdvcmRzKSUzQSUwQSUyMCUyMCUyMCUyMGZvciUyMGtleXdvcmQlMjBpbiUyMGtleXdvcmRzJTNBJTBBJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwaWYlMjBrZXl3b3JkJTIwaW4lMjBzdHJpbmclM0ElMEElMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjByZXR1cm4lMjBUcnVlJTBBJTIwJTIwJTIwJTIwcmV0dXJuJTIwRmFsc2U=",highlighted:`<span class="hljs-keyword">def</span> <span class="hljs-title function_">any_keyword_in_string</span>(<span class="hljs-params">string, keywords</span>):
    <span class="hljs-keyword">for</span> keyword <span class="hljs-keyword">in</span> keywords:
        <span class="hljs-keyword">if</span> keyword <span class="hljs-keyword">in</span> string:
            <span class="hljs-keyword">return</span> <span class="hljs-literal">True</span>
    <span class="hljs-keyword">return</span> <span class="hljs-literal">False</span>`,wrap:!1}}),W=new L({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset, DatasetDict

ds_train = load_dataset(<span class="hljs-string">&quot;huggingface-course/codeparrot-ds-train&quot;</span>, split=<span class="hljs-string">&quot;train&quot;</span>)
ds_valid = load_dataset(<span class="hljs-string">&quot;huggingface-course/codeparrot-ds-valid&quot;</span>, split=<span class="hljs-string">&quot;validation&quot;</span>)

raw_datasets = DatasetDict({<span class="hljs-string">&quot;train&quot;</span>: ds_train, <span class="hljs-string">&quot;valid&quot;</span>: ds_valid})`,wrap:!1}}),E=new K({props:{title:"Preparar el conjunto de datos",local:"preparing-the-dataset",headingTag:"h2"}}),X=new L({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Ub2tlbml6ZXIlMEElMEFjb250ZXh0X2xlbmd0aCUyMCUzRCUyMDEyOCUwQXRva2VuaXplciUyMCUzRCUyMEF1dG9Ub2tlbml6ZXIuZnJvbV9wcmV0cmFpbmVkKCUyMmh1Z2dpbmdmYWNlLWNvdXJzZSUyRmNvZGUtc2VhcmNoLW5ldC10b2tlbml6ZXIlMjIpJTBBJTBBJTBBZGVmJTIwdG9rZW5pemUoZWxlbWVudCklM0ElMEElMjAlMjAlMjAlMjBvdXRwdXRzJTIwJTNEJTIwdG9rZW5pemVyKCUwQSUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMGVsZW1lbnQlNUIlMjJjb250ZW50JTIyJTVEJTJDJTBBJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwdHJ1bmNhdGlvbiUzRFRydWUlMkMlMEElMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjBtYXhfbGVuZ3RoJTNEY29udGV4dF9sZW5ndGglMkMlMEElMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjByZXR1cm5fb3ZlcmZsb3dpbmdfdG9rZW5zJTNEVHJ1ZSUyQyUwQSUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMHJldHVybl9sZW5ndGglM0RUcnVlJTJDJTBBJTIwJTIwJTIwJTIwKSUwQSUyMCUyMCUyMCUyMGlucHV0X2JhdGNoJTIwJTNEJTIwJTVCJTVEJTBBJTIwJTIwJTIwJTIwZm9yJTIwbGVuZ3RoJTJDJTIwaW5wdXRfaWRzJTIwaW4lMjB6aXAob3V0cHV0cyU1QiUyMmxlbmd0aCUyMiU1RCUyQyUyMG91dHB1dHMlNUIlMjJpbnB1dF9pZHMlMjIlNUQpJTNBJTBBJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwaWYlMjBsZW5ndGglMjAlM0QlM0QlMjBjb250ZXh0X2xlbmd0aCUzQSUwQSUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMGlucHV0X2JhdGNoLmFwcGVuZChpbnB1dF9pZHMpJTBBJTIwJTIwJTIwJTIwcmV0dXJuJTIwJTdCJTIyaW5wdXRfaWRzJTIyJTNBJTIwaW5wdXRfYmF0Y2glN0QlMEElMEElMEF0b2tlbml6ZWRfZGF0YXNldHMlMjAlM0QlMjByYXdfZGF0YXNldHMubWFwKCUwQSUyMCUyMCUyMCUyMHRva2VuaXplJTJDJTIwYmF0Y2hlZCUzRFRydWUlMkMlMjByZW1vdmVfY29sdW1ucyUzRHJhd19kYXRhc2V0cyU1QiUyMnRyYWluJTIyJTVELmNvbHVtbl9uYW1lcyUwQSk=",highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer

context_length = <span class="hljs-number">128</span>
tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;huggingface-course/code-search-net-tokenizer&quot;</span>)


<span class="hljs-keyword">def</span> <span class="hljs-title function_">tokenize</span>(<span class="hljs-params">element</span>):
    outputs = tokenizer(
        element[<span class="hljs-string">&quot;content&quot;</span>],
        truncation=<span class="hljs-literal">True</span>,
        max_length=context_length,
        return_overflowing_tokens=<span class="hljs-literal">True</span>,
        return_length=<span class="hljs-literal">True</span>,
    )
    input_batch = []
    <span class="hljs-keyword">for</span> length, input_ids <span class="hljs-keyword">in</span> <span class="hljs-built_in">zip</span>(outputs[<span class="hljs-string">&quot;length&quot;</span>], outputs[<span class="hljs-string">&quot;input_ids&quot;</span>]):
        <span class="hljs-keyword">if</span> length == context_length:
            input_batch.append(input_ids)
    <span class="hljs-keyword">return</span> {<span class="hljs-string">&quot;input_ids&quot;</span>: input_batch}


tokenized_datasets = raw_datasets.<span class="hljs-built_in">map</span>(
    tokenize, batched=<span class="hljs-literal">True</span>, remove_columns=raw_datasets[<span class="hljs-string">&quot;train&quot;</span>].column_names
)`,wrap:!1}}),V=new K({props:{title:"Inicializar un modelo nuevo",local:"initializing-a-new-model",headingTag:"h2"}});const Ee=[lt,tt],S=[];function ze(e,t){return e[0]==="pt"?0:1}return T=ze(J),U=S[T]=Ee[T](J),x=new K({props:{title:"Data collator y entrenamiento",local:"training",headingTag:"h2"}}),F=new L({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMERhdGFDb2xsYXRvckZvckxhbmd1YWdlTW9kZWxpbmclMEElMEF0b2tlbml6ZXIucGFkX3Rva2VuJTIwJTNEJTIwdG9rZW5pemVyLmVvc190b2tlbiUwQWRhdGFfY29sbGF0b3IlMjAlM0QlMjBEYXRhQ29sbGF0b3JGb3JMYW5ndWFnZU1vZGVsaW5nKHRva2VuaXplciUyQyUyMG1sbSUzREZhbHNlKQ==",highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DataCollatorForLanguageModeling

tokenizer.pad_token = tokenizer.eos_token
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