{"id":7995,"date":"2021-12-16T17:59:14","date_gmt":"2021-12-16T16:59:14","guid":{"rendered":"https:\/\/www.ibidemgroup.com\/edu\/?p=7995"},"modified":"2024-03-04T12:35:48","modified_gmt":"2024-03-04T11:35:48","slug":"traduccion-machine-translation-datos-modelos","status":"publish","type":"post","link":"https:\/\/www.ibidemgroup.com\/edu\/traduccion-machine-translation-datos-modelos\/","title":{"rendered":"Machine Translation de otros idiomas a Ingl\u00e9s: herramientas, datasets y modelos preentrenados\ufeff"},"content":{"rendered":"\n<p class=\"has-text-align-center wp-block-paragraph\"><span style=\"color:#0693e3\" class=\"color\">Traducci\u00f3n de Ingl\u00e9s a Espa\u00f1ol realizada por Jose, <\/span><a href=\"https:\/\/www.ibidemgroup.com\/traduccion\/ingles.php\"><span style=\"color:#0693e3\" class=\"color\">traductor de Ingl\u00e9s<\/span><\/a><span style=\"color:#0693e3\" class=\"color\"> experto en Machine Translation con m\u00e1s de 20 a\u00f1os de experiencia traduciendo textos de todo tipo para la <a href=\"https:\/\/www.ibidemgroup.com\/\">agencia de traducci\u00f3n<\/a> Ibidem Group.<\/span><\/p>\n\n\n\n<p class=\"has-text-align-center wp-block-paragraph\"><span style=\"color:#0693e3\" class=\"color\"><strong>Texto original escrito por Thamme Gowda y publicado en <\/strong> <br><\/span><a rel=\"noreferrer noopener\" href=\"https:\/\/www.isi.edu\/~tg\/posts\/2021\/04\/mtdata-nlcodec-rtg-many-english\/\" target=\"_blank\"><span style=\"color:#0693e3\" class=\"color\">https:\/\/www.isi.edu\/~tg\/posts\/2021\/04\/mtdata-nlcodec-rtg-many-english\/<\/span><\/a><span style=\"color:#0693e3\" class=\"color\"> <\/span><\/p>\n\n\n\n<p class=\"has-text-align-center wp-block-paragraph\">***<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Enlaces<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Paper: <a href=\"https:\/\/arxiv.org\/abs\/2104.00290\">https:\/\/arxiv.org\/abs\/2104.00290 <\/a><\/li>\n\n\n\n<li><strong>Demostraci\u00f3n: <\/strong><a rel=\"noreferrer noopener\" href=\"http:\/\/rtg.isi.edu\/many-eng\/v1\/\" target=\"_blank\"><strong>\ud83d\udc49\ud83c\udffcTraductor al ingl\u00e9s\ud83d\udc48\ud83c\udffc\ud83d\udd25<\/strong><\/a><\/li>\n<\/ul>\n\n\n\n<figure class=\"wp-block-embed aligncenter is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio\"><div class=\"wp-block-embed__wrapper\">\n<iframe loading=\"lazy\" title=\"Many-to-English machine translation tools, data and pretrained models.\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube.com\/embed\/NSY0-MvO1KE?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe>\n<\/div><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Resumen<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Estas son las 3 herramientas de traducci\u00f3n autom\u00e1tica que vamos a usar:<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">MTData<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Una herramienta para descargar conjuntos de datos de traducci\u00f3n autom\u00e1tica <a href=\"https:\/\/github.com\/thammegowda\/mtdata\/\">https:\/\/github.com\/thammegowda\/mtdata\/<\/a> <\/p>\n\n\n\n<pre class=\"wp-block-preformatted\">  pip install mtdata  <\/pre>\n\n\n\n<h3 class=\"wp-block-heading\">NLCodec<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/isi-nlp.github.io\/nlcodec\/\">https:\/\/isi-nlp.github.io\/nlcodec\/<\/a> <\/p>\n\n\n\n<pre class=\"wp-block-preformatted\"> pip install nlcodec <\/pre>\n\n\n\n<h3 class=\"wp-block-heading\">RTG<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/isi-nlp.github.io\/rtg\/\">https:\/\/isi-nlp.github.io\/rtg\/<\/a><\/p>\n\n\n\n<pre class=\"wp-block-preformatted\">  pip install rtg <\/pre>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><td>Guiones, tablas y gr\u00e1ficos: <a href=\"https:\/\/github.com\/thammegowda\/006-many-to-eng\">https:\/\/github.com\/thammegowda\/006-many-to-eng   <\/a>   <\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n\n\n<p class=\"wp-block-paragraph\">El primer paso es usar estas herramientas para recopilar datasets grandes.<\/p>\n\n\n\n\n\n<h2 class=\"wp-block-heading\">Descargar datasets<\/h2>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Desde http:\/\/rtg.isi.edu\/many-eng\/data-v1.html.<\/li>\n\n\n\n<li>Tambi\u00e9n disponible en OPUS <a href=\"https:\/\/opus.nlpl.eu\/MT560.php\">https:\/\/opus.nlpl.eu\/MT560.php<\/a><\/li>\n<\/ol>\n\n\n\n\n\n<p class=\"wp-block-paragraph\">Y luego entrenar modelos multiling\u00fces masivos para la traducci\u00f3n autom\u00e1tica al ingl\u00e9s.<\/p>\n\n\n\n\n\n<h2 class=\"wp-block-heading\">Descargar modelos<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"http:\/\/rtg.isi.edu\/many-eng\/models\/\">http:\/\/rtg.isi.edu\/many-eng\/models\/<\/a><\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><td>\n  Recommended: <a href=\"http:\/\/rtg.isi.edu\/many-eng\/models\/rtg500eng-tfm9L6L768d-bsz720k-stp200k-ens05.tgz\">rtg500eng-tfm9L6L768d-bsz720k-stp200k-ens05.tgz\n  <\/a>\n  <\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">Proporcionamos una imagen docker con un modelo\npreentrenado que puede utilizarse f\u00e1cilmente como servicio de traducci\u00f3n:<\/p>\n\n\n\n<pre class=\"wp-block-preformatted\">pick the latest image from https:\/\/hub.docker.com\/repository\/docker\/tgowda\/rtg-model<br> IMAGE=tgowda\/rtg-model:500toEng-v1<br><br> To run without using GPU; requires about 5 to 6GB CPU RAM<br> docker run --rm -i -p 6060:6060 $IMAGE<br><br> Recommended: use GPU (e.g. device=0)<br> docker run --gpus '\"device=0\"' --rm -i -p 6060:6060 $IMAGE<\/pre>\n\n\n\n<p class=\"wp-block-paragraph\">Si no queremos usar docker, entonces haremos lo siguiente:<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Paso 1: Configurar un entorno conda e instala la librer\u00eda rtg.<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><td>   Si falta conda en su sistema, <a rel=\"noreferrer noopener\" href=\"https:\/\/docs.conda.io\/en\/latest\/miniconda.html\" target=\"_blank\">instale miniconda <\/a>para empezar.    <\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n\n\n<pre class=\"wp-block-preformatted\">conda create -n rtg python=3.7<br> conda activate rtg<br> pip install rtg==0.5.0  # install rtg and its dependencies<br> conda install -c conda-forge uwsgi  # needed to deploy service<\/pre>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Paso 2: Descargar un modelo y ejecutar<\/strong><\/p>\n\n\n\n<pre class=\"wp-block-preformatted\">Pick the latest version<br> MODEL=rtg500eng-tfm9L6L768d-bsz720k-stp200k-ens05.tgz<br> wget http:\/\/rtg.isi.edu\/many-eng\/models\/$MODEL<br><br> tar xvf $MODEL    # Extract and run<br> uwsgi --http 127.0.0.1:6060 --module rtg.serve.app:app --pyargv \"\/path\/to\/extracted\/dir\"<br> Alternatively, without uWSGI (not recommended)<br> rtg-serve \/path\/to\/extracted\/dir<br> See \"rtg-serve -h\" to learn optional arguments for --pyargv \"\" of uWSGI<\/pre>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Interacci\u00f3n con la API REST<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Interfaz web: se ha creado una sencilla interfaz web en http:\/\/localhost:6060.<\/li>\n\n\n\n<li>La API REST est\u00e1 disponible en http:\/\/localhost:6060\/translate.<br>      Un ejemplo de interacci\u00f3n con la API REST:<\/li>\n<\/ul>\n\n\n\n<pre class=\"wp-block-preformatted\">API=http:\/\/localhost:6060\/translate<br> curl $API --data \"source=Comment allez-vous?\" \\<br>    --data \"source=Bonne journ\u00e9e\"<br> API also accepts input as JSON data<br> curl -X POST -H \"Content-Type: application\/json\" $API\\<br>   --data '{\"source\":[\"Comment allez-vous?\", \"Bonne journ\u00e9e\"]}'<\/pre>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><td>Para saber m\u00e1s sobre el servicio RTG y c\u00f3mo interactuar con \u00e9l, vaya a <a rel=\"noreferrer noopener\" href=\"https:\/\/isi-nlp.github.io\/rtg\/#_rtg_serve\" target=\"_blank\">RTGDocs <\/a>   <\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n\n\n<h2 class=\"wp-block-heading\">Decodificaci\u00f3n por lotes<\/h2>\n\n\n\n<pre class=\"wp-block-preformatted\"><code>pip install rtg==0.5.0<\/code> should have already installed sacremoses-xt<br> pip install sacremoses-xt==0.0.44<br> sacremoses normalize -q -d -p -c tokenize -a -x -p :web: &lt; input.src &gt; input.src.tok<br><br> CUDA_VISIBLE_DEVICES=0   # set GPU device ID<br> rtg-decode \/path\/to\/model-extract -if input.src.tok -of output.out<br><br> post process; drop s, detokenize<br> cut -f1 output.out | sed 's\/\/\/g' | sacremoses detokenize &gt; output.out.detok<\/pre>\n\n\n\n<p class=\"wp-block-paragraph\">Transferencia de padres a hijos para\nMT de bajos recursos<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">El modelo preentrenado puede adaptarse a un dataset espec\u00edfico, utilizando una configuraci\u00f3n de transferencia padre-hijo.<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><td><a rel=\"noreferrer noopener\" href=\"https:\/\/isi-nlp.github.io\/rtg\/#conf-parent-child\" target=\"_blank\">Consulta la documentaci\u00f3n de RTG para la transferencia de padres a hijos <\/a>   <\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">La tasa de aprendizaje del entrenador del modelo hijo es un par\u00e1metro crucial: una tasa de aprendizaje m\u00e1s alta destruir\u00eda los pesos del modelo padre, y una tasa de aprendizaje m\u00e1s baja significa menos adaptaci\u00f3n al conjunto de datos hijo. Por lo tanto, la tasa de aprendizaje debe ser la adecuada; consulta los archivos conf.yml en <a href=\"https:\/\/github.com\/thammegowda\/006-many-to-eng\/tree\/master\/lowres-xfer\">https:\/\/github.com\/thammegowda\/006-many-to-eng\/tree\/master\/lowres-xfer<\/a><\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Referencias<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/aclanthology.org\/2021.acl-demo.37\/\">https:\/\/aclanthology.org\/2021.acl-demo.37\/<\/a><\/p>\n\n\n\n<pre class=\"wp-block-preformatted\">@inproceedings{gowda-etal-2021-many,<br>     title = \"Many-to-{E}nglish Machine Translation Tools, Data, and Pretrained Models\",<br>     author = \"Gowda, Thamme  and<br>       Zhang, Zhao  and<br>       Mattmann, Chris  and<br>       May, Jonathan\",<br>     booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations\",<br>     month = aug,<br>     year = \"2021\",<br>     address = \"Online\",<br>     publisher = \"Association for Computational Linguistics\",<br>     url = \"https:\/\/aclanthology.org\/2021.acl-demo.37\",<br>     doi = \"10.18653\/v1\/2021.acl-demo.37\",<br>     pages = \"306--316\",<br> }<\/pre>\n\n\n\n<h3 class=\"wp-block-heading\">Agradecimientos<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Gracias a USC CARC, y TACC por facilitar los recursos t\u00e9cnicos. Gracias a J\u00f6rg Tiedemann por alojar los datasets en OPUS. <\/p>\n\n\n<div class=\"kk-star-ratings kksr-auto kksr-align-right kksr-valign-bottom\"\n    data-payload='{&quot;align&quot;:&quot;right&quot;,&quot;id&quot;:&quot;7995&quot;,&quot;slug&quot;:&quot;default&quot;,&quot;valign&quot;:&quot;bottom&quot;,&quot;ignore&quot;:&quot;&quot;,&quot;reference&quot;:&quot;auto&quot;,&quot;class&quot;:&quot;&quot;,&quot;count&quot;:&quot;0&quot;,&quot;legendonly&quot;:&quot;&quot;,&quot;readonly&quot;:&quot;&quot;,&quot;score&quot;:&quot;0&quot;,&quot;starsonly&quot;:&quot;&quot;,&quot;best&quot;:&quot;5&quot;,&quot;gap&quot;:&quot;5&quot;,&quot;greet&quot;:&quot;Rate this post&quot;,&quot;legend&quot;:&quot;0\\\/5 - (0 votos)&quot;,&quot;size&quot;:&quot;24&quot;,&quot;title&quot;:&quot;Machine Translation de otros idiomas a Ingl\u00e9s: herramientas, datasets y modelos preentrenados\ufeff&quot;,&quot;width&quot;:&quot;0&quot;,&quot;_legend&quot;:&quot;{score}\\\/{best} - ({count} {votes})&quot;,&quot;font_factor&quot;:&quot;1.25&quot;}'>\n            \n<div class=\"kksr-stars\">\n    \n<div class=\"kksr-stars-inactive\">\n            <div class=\"kksr-star\" data-star=\"1\" style=\"padding-right: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" data-star=\"2\" style=\"padding-right: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" data-star=\"3\" style=\"padding-right: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" data-star=\"4\" style=\"padding-right: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" data-star=\"5\" style=\"padding-right: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n    <\/div>\n    \n<div class=\"kksr-stars-active\" style=\"width: 0px;\">\n            <div class=\"kksr-star\" style=\"padding-right: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" style=\"padding-right: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" style=\"padding-right: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" style=\"padding-right: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" style=\"padding-right: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n    <\/div>\n<\/div>\n                \n\n<div class=\"kksr-legend\" style=\"font-size: 19.2px;\">\n            <span class=\"kksr-muted\">Rate this post<\/span>\n    <\/div>\n    <\/div>\n","protected":false},"excerpt":{"rendered":"Interesant\u00edsimo art\u00edculo de Thamme Gowda, Zhao Zhang, Chris A Mattmann, y Jonathan May presentando herramientas \u00fatiles para la investigaci\u00f3n con Traducci\u00f3n Autom\u00e1tica: MTData, NLCodec y RTG. Modelo de traducci\u00f3n autom\u00e1tica neural multiling\u00fce capaz de traducir desde 500 idiomas de origen (Espa\u00f1ol, Franc\u00e9s, Alem\u00e1n, etc) al Ingl\u00e9s. ","protected":false},"author":8,"featured_media":8644,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[619],"tags":[279,466],"class_list":["post-7995","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-traduccion-automatica","tag-machine-translation","tag-traduccino-automatica"],"acf":[],"aioseo_notices":[],"aioseo_head":"\n\t\t<!-- All in One SEO 5.0.3 - aioseo.com -->\n\t<meta name=\"robots\" content=\"max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n\t<meta name=\"author\" content=\"Mauri Fernandez\"\/>\n\t<link rel=\"canonical\" href=\"https:\/\/www.ibidemgroup.com\/edu\/traduccion-machine-translation-datos-modelos\/\" \/>\n\t\t<meta name=\"generator\" content=\"All in One SEO (AIOSEO) 5.0.3\" \/>\n\t\t<meta property=\"og:locale\" content=\"es_ES\" \/>\n\t\t<meta property=\"og:site_name\" content=\"IBIDEM EDU |\" 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