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0c.042-.331.063-.628.063-.894v-.02c-.001-.77-.169-1.271-.438-1.578-.341-.391-1.046-.69-2.533-.529-1.505.163-2.347.537-2.824 1.025-.462.472-.705 1.179-.705 2.319 0 1.211.175 1.926.558 2.361.365.414 1.084.751 2.657.751 1.21 0 1.902-.394 2.344-.938.475-.584.742-1.44.878-2.497Z"></path><path d="M14.5 14.25a1 1 0 0 1 1 1v2a1 1 0 0 1-2 0v-2a1 1 0 0 1 1-1Zm-5 0a1 1 0 0 1 1 1v2a1 1 0 0 1-2 0v-2a1 1 0 0 1 1-1Z"></path> </svg> <div> <div class="color-fg-default h4">GitHub Copilot</div> Write better code with AI </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{"location":"navbar","action":"security","context":"product","tag":"link","label":"security_link_product_navbar"}" href="https://github.com/features/security"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon 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class=\"heading-element\" dir=\"auto\"\u003eNEMATUS\u003c/h2\u003e\u003ca id=\"user-content-nematus\" class=\"anchor\" aria-label=\"Permalink: NEMATUS\" href=\"#nematus\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThis fork of Nematus contains the code for:\nNeural Lattice Search for Domain Adaptation in Machine Translation\nHuda Khayrallah Gaurav Kumar Kevin Duh Matt Post Philipp Koehn\n{huda, gkumar, kevinduh, post, phi}@cs.jhu.edu\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eUSAGE:\ndecoding can be perfomed with an existing nematus model.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eRescoring of a Moses word graph using a translation model is performed with: nematus/rescore_graph.py\nThe Moses word graph is obtained by adding the flags \"-osg \" to Moses.\nIt then must be converted to fst format using: scripts/gen_lattice.sh\u003c/p\u003e\n\u003chr\u003e\n\u003cp dir=\"auto\"\u003eAttention-based encoder-decoder model for neural machine translation\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThis package is based on the dl4mt-tutorial by Kyunghyun Cho et al. ( \u003ca href=\"https://github.com/nyu-dl/dl4mt-tutorial\"\u003ehttps://github.com/nyu-dl/dl4mt-tutorial\u003c/a\u003e ).\nIt was used to produce top-scoring systems at the WMT 16 shared translation task.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThe changes to Nematus include:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003earbitrary input features (factored neural machine translation) \u003ca href=\"http://www.statmt.org/wmt16/pdf/W16-2209.pdf\" rel=\"nofollow\"\u003ehttp://www.statmt.org/wmt16/pdf/W16-2209.pdf\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eensemble decoding (and new translation API to support it)\u003c/li\u003e\n\u003cli\u003edropout on all layers (Gal, 2015) \u003ca href=\"http://arxiv.org/abs/1512.05287\" rel=\"nofollow\"\u003ehttp://arxiv.org/abs/1512.05287\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eminimum risk training (Shen et al, 2016) \u003ca href=\"http://aclweb.org/anthology/P16-1159\" rel=\"nofollow\"\u003ehttp://aclweb.org/anthology/P16-1159\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003etied embeddings (Press and Wolf, 2016) \u003ca href=\"https://arxiv.org/abs/1608.05859\" rel=\"nofollow\"\u003ehttps://arxiv.org/abs/1608.05859\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003ecommand line interface for training\u003c/li\u003e\n\u003cli\u003eautomatic training set reshuffling between epochs\u003c/li\u003e\n\u003cli\u003en-best output for decoder\u003c/li\u003e\n\u003cli\u003emore output options (attention weights; word-level probabilities) and visualization scripts\u003c/li\u003e\n\u003cli\u003eperformance improvements to decoder\u003c/li\u003e\n\u003cli\u003erescoring support\u003c/li\u003e\n\u003cli\u003eexecute arbitrary validation scripts (for BLEU early stopping)\u003c/li\u003e\n\u003cli\u003evocabulary files and model parameters are stored in JSON format (backward-compatible loading)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eSUPPORT\u003c/h2\u003e\u003ca id=\"user-content-support\" class=\"anchor\" aria-label=\"Permalink: SUPPORT\" href=\"#support\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eFor general support requests, there is a Google Groups mailing list at \u003ca href=\"https://groups.google.com/d/forum/nematus-support\" rel=\"nofollow\"\u003ehttps://groups.google.com/d/forum/nematus-support\u003c/a\u003e . You can also send an e-mail to \u003ca href=\"mailto:nematus-support@googlegroups.com\"\u003enematus-support@googlegroups.com\u003c/a\u003e .\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eINSTALLATION\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: INSTALLATION\" href=\"#installation\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eNematus requires the following packages:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003ePython \u0026gt;= 2.7\u003c/li\u003e\n\u003cli\u003enumpy\u003c/li\u003e\n\u003cli\u003eTheano \u0026gt;= 0.7 (and its dependencies).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003ewe recommend executing the following command in a Python virtual environment:\n\u003ccode\u003epip install numpy numexpr cython tables theano\u003c/code\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003ethe following packages are optional, but \u003cem\u003ehighly\u003c/em\u003e recommended\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eCUDA \u0026gt;= 7 (only GPU training is sufficiently fast)\u003c/li\u003e\n\u003cli\u003ecuDNN \u0026gt;= 4 (speeds up training substantially)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eyou can run Nematus locally. To install it, execute \u003ccode\u003epython setup.py install\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eDOCKER USAGE\u003c/h2\u003e\u003ca id=\"user-content-docker-usage\" class=\"anchor\" aria-label=\"Permalink: DOCKER USAGE\" href=\"#docker-usage\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eYou can also create docker image by running following command, where you change \u003ccode\u003esuffix\u003c/code\u003e to either \u003ccode\u003ecpu\u003c/code\u003e or \u003ccode\u003egpu\u003c/code\u003e:\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003edocker build -t nematus-docker -f Dockerfile.suffix .\u003c/code\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eTo run a CPU docker instance with the current working directory shared with the Docker container, execute:\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003edocker run -v `pwd`:/playground -it nematus-docker\u003c/code\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eFor GPU you need to have nvidia-docker installed and run:\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003envidia-docker run -v `pwd`:/playground -it nematus-docker\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eTRAINING SPEED\u003c/h2\u003e\u003ca id=\"user-content-training-speed\" class=\"anchor\" aria-label=\"Permalink: TRAINING SPEED\" href=\"#training-speed\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTraining speed depends heavily on having appropriate hardware (ideally a recent NVIDIA GPU),\nand having installed the appropriate software packages.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eTo test your setup, we provide some speed benchmarks with `test/test_train.sh',\non an Intel Xeon CPU E5-2620 v3, with a Nvidia GeForce GTX 1080 GPU and CUDA 8.0:\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eCPU, theano 0.8.2:\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eTHEANO_FLAGS=mode=FAST_RUN,floatX=float32,device=cpu ./test_train.sh\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cblockquote\u003e\n\u003cp dir=\"auto\"\u003e2.37 sentences/s\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003c/blockquote\u003e\n\u003cp dir=\"auto\"\u003eGPU, no CuDNN, theano 0.8.2:\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eTHEANO_FLAGS=mode=FAST_RUN,floatX=float32,device=gpu ./test_train.sh\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cblockquote\u003e\n\u003cp dir=\"auto\"\u003e71.62 sentences/s\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003c/blockquote\u003e\n\u003cp dir=\"auto\"\u003eGPU, CuDNN 5.1, theano 0.8.2:\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eTHEANO_FLAGS=mode=FAST_RUN,floatX=float32,device=gpu ./test_train.sh\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cblockquote\u003e\n\u003cp dir=\"auto\"\u003e139.73 sentences/s\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003c/blockquote\u003e\n\u003cp dir=\"auto\"\u003eGPU, CuDNN 5.1, theano 0.9.0dev5.dev-d5520e:\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eTHEANO_FLAGS=mode=FAST_RUN,floatX=float32,device=gpu ./test_train.sh\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cblockquote\u003e\n\u003cp dir=\"auto\"\u003e173.15 sentences/s\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003c/blockquote\u003e\n\u003cp dir=\"auto\"\u003eGPU, CuDNN 5.1, theano 0.9.0dev5.dev-d5520e, new GPU backend:\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eTHEANO_FLAGS=mode=FAST_RUN,floatX=float32,device=cuda ./test_train.sh\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cblockquote\u003e\n\u003cp dir=\"auto\"\u003e209.21 sentences/s\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eUSAGE INSTRUCTIONS\u003c/h2\u003e\u003ca id=\"user-content-usage-instructions\" class=\"anchor\" aria-label=\"Permalink: USAGE INSTRUCTIONS\" href=\"#usage-instructions\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eexecute nematus/nmt.py to train a model.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003edata sets; model loading and saving\u003c/h4\u003e\u003ca id=\"user-content-data-sets-model-loading-and-saving\" class=\"anchor\" aria-label=\"Permalink: data sets; model loading and saving\" href=\"#data-sets-model-loading-and-saving\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eparameter\u003c/th\u003e\n\u003cth\u003edescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--datasets PATH PATH\u003c/td\u003e\n\u003ctd\u003eparallel training corpus (source and target)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--dictionaries PATH [PATH ...]\u003c/td\u003e\n\u003ctd\u003enetwork vocabularies (one per source factor, plus target vocabulary)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--model PATH\u003c/td\u003e\n\u003ctd\u003emodel file name (default: model.npz)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--saveFreq INT\u003c/td\u003e\n\u003ctd\u003esave frequency (default: 30000)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--reload\u003c/td\u003e\n\u003ctd\u003eload existing model (if '--model' points to existing model)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--overwrite\u003c/td\u003e\n\u003ctd\u003ewrite all models to same file\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003enetwork parameters\u003c/h4\u003e\u003ca id=\"user-content-network-parameters\" class=\"anchor\" aria-label=\"Permalink: network parameters\" href=\"#network-parameters\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eparameter\u003c/th\u003e\n\u003cth\u003edescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--dim_word INT\u003c/td\u003e\n\u003ctd\u003eembedding layer size (default: 512)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--dim INT\u003c/td\u003e\n\u003ctd\u003ehidden layer size (default: 1000)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--n_words_src INT\u003c/td\u003e\n\u003ctd\u003esource vocabulary size (default: None)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--n_words INT\u003c/td\u003e\n\u003ctd\u003etarget vocabulary size (default: None)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--factors INT\u003c/td\u003e\n\u003ctd\u003enumber of input factors (default: 1)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--dim_per_factor INT [INT ...]\u003c/td\u003e\n\u003ctd\u003elist of word vector dimensionalities (one per factor): '--dim_per_factor 250 200 50' for total dimensionality of 500 (default: None)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--use_dropout\u003c/td\u003e\n\u003ctd\u003euse dropout layer (default: False)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--dropout_embedding FLOAT\u003c/td\u003e\n\u003ctd\u003edropout for input embeddings (0: no dropout) (default: 0.2)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--dropout_hidden FLOAT\u003c/td\u003e\n\u003ctd\u003edropout for hidden layer (0: no dropout) (default: 0.2)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--dropout_source FLOAT\u003c/td\u003e\n\u003ctd\u003edropout source words (0: no dropout) (default: 0)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--dropout_target FLOAT\u003c/td\u003e\n\u003ctd\u003edropout target words (0: no dropout) (default: 0)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--tie_decoder_embeddings\u003c/td\u003e\n\u003ctd\u003etie the input embeddings of the decoder with the softmax output embeddings\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--tie_encoder_decoder_embeddings\u003c/td\u003e\n\u003ctd\u003etie the input embeddings of the encoder and the decoder (first factor only). Source and target vocabulary size must the same\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003etraining parameters\u003c/h4\u003e\u003ca id=\"user-content-training-parameters\" class=\"anchor\" aria-label=\"Permalink: training parameters\" href=\"#training-parameters\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eparameter\u003c/th\u003e\n\u003cth\u003edescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--maxlen INT\u003c/td\u003e\n\u003ctd\u003emaximum sequence length (default: 100)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--optimizer {adam,adadelta,rmsprop,sgd}\u003c/td\u003e\n\u003ctd\u003eoptimizer (default: adam)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--batch_size INT\u003c/td\u003e\n\u003ctd\u003eminibatch size (default: 80)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--max_epochs INT\u003c/td\u003e\n\u003ctd\u003emaximum number of epochs (default: 5000)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--finish_after INT\u003c/td\u003e\n\u003ctd\u003emaximum number of updates (minibatches) (default: 10000000)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--decay_c FLOAT\u003c/td\u003e\n\u003ctd\u003eL2 regularization penalty (default: 0)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--map_decay_c FLOAT\u003c/td\u003e\n\u003ctd\u003eL2 regularization penalty towards original weights (default: 0)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--alpha_c FLOAT\u003c/td\u003e\n\u003ctd\u003ealignment regularization (default: 0)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--clip_c FLOAT\u003c/td\u003e\n\u003ctd\u003egradient clipping threshold (default: 1)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--lrate FLOAT\u003c/td\u003e\n\u003ctd\u003elearning rate (default: 0.0001)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--no_shuffle\u003c/td\u003e\n\u003ctd\u003edisable shuffling of training data (for each epoch)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--no_sort_by_length\u003c/td\u003e\n\u003ctd\u003edo not sort sentences in maxibatch by length\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--maxibatch_size INT\u003c/td\u003e\n\u003ctd\u003esize of maxibatch (number of minibatches that are sorted by length) (default: 20)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--objective {CE,MRT}\u003c/td\u003e\n\u003ctd\u003etraining objective. CE: cross-entropy minimization (default); MRT: Minimum Risk Training (\u003ca href=\"https://www.aclweb.org/anthology/P/P16/P16-1159.pdf\" rel=\"nofollow\"\u003ehttps://www.aclweb.org/anthology/P/P16/P16-1159.pdf\u003c/a\u003e)\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003evalidation parameters\u003c/h4\u003e\u003ca id=\"user-content-validation-parameters\" class=\"anchor\" aria-label=\"Permalink: validation parameters\" href=\"#validation-parameters\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eparameter\u003c/th\u003e\n\u003cth\u003edescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--valid_datasets PATH PATH\u003c/td\u003e\n\u003ctd\u003eparallel validation corpus (source and target)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--valid_batch_size INT\u003c/td\u003e\n\u003ctd\u003evalidation minibatch size (default: 80)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--validFreq INT\u003c/td\u003e\n\u003ctd\u003evalidation frequency (default: 10000)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--patience INT\u003c/td\u003e\n\u003ctd\u003eearly stopping patience (default: 10)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--external_validation_script PATH\u003c/td\u003e\n\u003ctd\u003elocation of validation script (to run your favorite metric for validation) (default: None)\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003edisplay parameters\u003c/h4\u003e\u003ca id=\"user-content-display-parameters\" class=\"anchor\" aria-label=\"Permalink: display parameters\" href=\"#display-parameters\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eparameter\u003c/th\u003e\n\u003cth\u003edescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--dispFreq INT\u003c/td\u003e\n\u003ctd\u003edisplay loss after INT updates (default: 1000)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--sampleFreq INT\u003c/td\u003e\n\u003ctd\u003edisplay some samples after INT updates (default: 10000)\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eminimum risk training parameters\u003c/h4\u003e\u003ca id=\"user-content-minimum-risk-training-parameters\" class=\"anchor\" aria-label=\"Permalink: minimum risk training parameters\" href=\"#minimum-risk-training-parameters\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eparameter\u003c/th\u003e\n\u003cth\u003edescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--mrt_alpha FLOAT\u003c/td\u003e\n\u003ctd\u003eMRT alpha (default: 0.005)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--mrt_samples INT\u003c/td\u003e\n\u003ctd\u003esamples per source sentence (default: 100)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--mrt_samples_meanloss INT\u003c/td\u003e\n\u003ctd\u003edraw n independent samples to calculate mean loss (which is subtracted from loss) (default: 10)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--mrt_loss STR\u003c/td\u003e\n\u003ctd\u003eloss used in MRT (default: SENTENCEBLEU n=4)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--mrt_reference\u003c/td\u003e\n\u003ctd\u003eadd reference to MRT samples.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--mrt_ml_mix\u003c/td\u003e\n\u003ctd\u003emix in ML objective in MRT training with this scaling factor (default: 0)\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cp dir=\"auto\"\u003emore instructions to train a model, including a sample configuration and\npreprocessing scripts, are provided in \u003ca href=\"https://github.com/rsennrich/wmt16-scripts\"\u003ehttps://github.com/rsennrich/wmt16-scripts\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eUSING A TRAINED MODEL\u003c/h3\u003e\u003ca id=\"user-content-using-a-trained-model\" class=\"anchor\" aria-label=\"Permalink: USING A TRAINED MODEL\" href=\"#using-a-trained-model\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e\u003ccode\u003enematus/translate.py\u003c/code\u003e : use an existing model to translate a source text\u003c/h4\u003e\u003ca id=\"user-content-nematustranslatepy--use-an-existing-model-to-translate-a-source-text\" class=\"anchor\" aria-label=\"Permalink: nematus/translate.py : use an existing model to translate a source text\" href=\"#nematustranslatepy--use-an-existing-model-to-translate-a-source-text\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eparameter\u003c/th\u003e\n\u003cth\u003edescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e-k K\u003c/td\u003e\n\u003ctd\u003eBeam size (default: 5))\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e-p P\u003c/td\u003e\n\u003ctd\u003eNumber of processes (default: 5))\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e-n\u003c/td\u003e\n\u003ctd\u003eNormalize scores by sentence length\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e-v\u003c/td\u003e\n\u003ctd\u003everbose mode.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--models MODELS [MODELS ...], -m MODELS [MODELS ...]\u003c/td\u003e\n\u003ctd\u003emodel to use. Provide multiple models (with same vocabulary) for ensemble decoding\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--input PATH, -i PATH\u003c/td\u003e\n\u003ctd\u003eInput file (default: standard input)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--output PATH, -o PATH\u003c/td\u003e\n\u003ctd\u003eOutput file (default: standard output)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--output_alignment PATH, -a PATH\u003c/td\u003e\n\u003ctd\u003eOutput file for alignment weights (default: standard output)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--json_alignment\u003c/td\u003e\n\u003ctd\u003eOutput alignment in json format\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--n-best\u003c/td\u003e\n\u003ctd\u003eWrite n-best list (of size k)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--suppress-unk\u003c/td\u003e\n\u003ctd\u003eSuppress hypotheses containing UNK.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--print-word-probabilities, -wp\u003c/td\u003e\n\u003ctd\u003ePrint probabilities of each word\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--search_graph, -sg\u003c/td\u003e\n\u003ctd\u003eOutput file for search graph rendered as PNG image\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e\u003ccode\u003enematus/score.py\u003c/code\u003e : use an existing model to score a parallel corpus\u003c/h4\u003e\u003ca id=\"user-content-nematusscorepy--use-an-existing-model-to-score-a-parallel-corpus\" class=\"anchor\" aria-label=\"Permalink: nematus/score.py : use an existing model to score a parallel corpus\" href=\"#nematusscorepy--use-an-existing-model-to-score-a-parallel-corpus\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eparameter\u003c/th\u003e\n\u003cth\u003edescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e-b B\u003c/td\u003e\n\u003ctd\u003eMinibatch size (default: 80))\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e-n\u003c/td\u003e\n\u003ctd\u003eNormalize scores by sentence length\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e-v\u003c/td\u003e\n\u003ctd\u003everbose mode.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--models MODELS [MODELS ...], -m MODELS [MODELS ...]\u003c/td\u003e\n\u003ctd\u003emodel to use. Provide multiple models (with same vocabulary) for ensemble decoding\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--source PATH, -s PATH\u003c/td\u003e\n\u003ctd\u003eSource text file\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--target PATH, -t PATH\u003c/td\u003e\n\u003ctd\u003eTarget text file\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--output PATH, -o PATH\u003c/td\u003e\n\u003ctd\u003eOutput file (default: standard output)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--walign, -w\u003c/td\u003e\n\u003ctd\u003eWhether to store the alignment weights or not. If specified, weights will be saved in .alignment\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e\u003ccode\u003enematus/rescore.py\u003c/code\u003e : use an existing model to rescore an n-best list.\u003c/h4\u003e\u003ca id=\"user-content-nematusrescorepy--use-an-existing-model-to-rescore-an-n-best-list\" class=\"anchor\" aria-label=\"Permalink: nematus/rescore.py : use an existing model to rescore an n-best list.\" href=\"#nematusrescorepy--use-an-existing-model-to-rescore-an-n-best-list\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThe n-best list is assumed to have the same format as Moses:\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"sentence-ID (starting from 0) ||| translation ||| scores\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003esentence-ID (starting from 0) ||| translation ||| scores\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003enew scores will be appended to the end. \u003ccode\u003erescore.py\u003c/code\u003e has the same arguments as \u003ccode\u003escore.py\u003c/code\u003e, with the exception of this additional parameter:\u003c/p\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eparameter\u003c/th\u003e\n\u003cth\u003edescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--input PATH, -i PATH\u003c/td\u003e\n\u003ctd\u003eInput n-best list file (default: standard input)\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cp dir=\"auto\"\u003esample models, and instructions on using them for translation, are provided in the \u003ccode\u003etest\u003c/code\u003e directory, and at \u003ca href=\"http://statmt.org/rsennrich/wmt16_systems/\" rel=\"nofollow\"\u003ehttp://statmt.org/rsennrich/wmt16_systems/\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003ePUBLICATIONS\u003c/h2\u003e\u003ca id=\"user-content-publications\" class=\"anchor\" aria-label=\"Permalink: PUBLICATIONS\" href=\"#publications\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eif you use Nematus, please cite the following paper:\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eRico Sennrich, Orhan Firat, Kyunghyun Cho, Alexandra Birch, Barry Haddow, Julian Hitschler, Marcin Junczys-Dowmunt, Samuel Läubli, Antonio Valerio Miceli Barone, Jozef Mokry and Maria Nadejde (2017): Nematus: a Toolkit for Neural Machine Translation. In Proceedings of the Demonstrations at the 15th Conference of the European Chapter of the Association for Computational Linguistics, Valencia, Spain.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e@inproceedings{nematus,\naddress = \"Valencia, Spain\",\nauthor = \"Sennrich, Rico and Firat, Orhan and Cho, Kyunghyun and Birch, Alexandra and Haddow, Barry and Hitschler, Julian and Junczys-Dowmunt, Marcin and L{\"a}ubli, Samuel and {Miceli Barone}, Antonio Valerio and Mokry, Jozef and Nadejde, Maria\",\nbooktitle = \"{Proceedings of the Demonstrations at the 15th Conference of the European Chapter of the Association for Computational Linguistics}\",\ntitle = \"{Nematus: a Toolkit for Neural Machine Translation}\",\nyear = \"2017\"\n}\u003c/p\u003e\n\u003cp dir=\"auto\"\u003ethe code is based on the following model:\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eDzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio (2015): Neural Machine Translation by Jointly Learning to Align and Translate, Proceedings of the International Conference on Learning Representations (ICLR).\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eplease refer to the Nematus paper for a description of implementation differences\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eACKNOWLEDGMENTS\u003c/h2\u003e\u003ca id=\"user-content-acknowledgments\" class=\"anchor\" aria-label=\"Permalink: ACKNOWLEDGMENTS\" href=\"#acknowledgments\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThis project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreements 645452 (QT21), 644333 (TraMOOC), 644402 (HimL) and 688139 (SUMMA).\u003c/p\u003e\n\u003c/article\u003e","loaded":true,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":[{"level":2,"text":"NEMATUS","anchor":"nematus","htmlText":"NEMATUS"},{"level":2,"text":"SUPPORT","anchor":"support","htmlText":"SUPPORT"},{"level":2,"text":"INSTALLATION","anchor":"installation","htmlText":"INSTALLATION"},{"level":2,"text":"DOCKER USAGE","anchor":"docker-usage","htmlText":"DOCKER USAGE"},{"level":2,"text":"TRAINING SPEED","anchor":"training-speed","htmlText":"TRAINING SPEED"},{"level":2,"text":"USAGE INSTRUCTIONS","anchor":"usage-instructions","htmlText":"USAGE INSTRUCTIONS"},{"level":4,"text":"data sets; model loading and saving","anchor":"data-sets-model-loading-and-saving","htmlText":"data sets; model loading and saving"},{"level":4,"text":"network parameters","anchor":"network-parameters","htmlText":"network parameters"},{"level":4,"text":"training parameters","anchor":"training-parameters","htmlText":"training parameters"},{"level":4,"text":"validation parameters","anchor":"validation-parameters","htmlText":"validation parameters"},{"level":4,"text":"display parameters","anchor":"display-parameters","htmlText":"display parameters"},{"level":4,"text":"minimum risk training parameters","anchor":"minimum-risk-training-parameters","htmlText":"minimum risk training parameters"},{"level":3,"text":"USING A TRAINED MODEL","anchor":"using-a-trained-model","htmlText":"USING A TRAINED MODEL"},{"level":4,"text":"nematus/translate.py : use an existing model to translate a source text","anchor":"nematustranslatepy--use-an-existing-model-to-translate-a-source-text","htmlText":"nematus/translate.py : use an existing model to translate a source text"},{"level":4,"text":"nematus/score.py : use an existing model to score a parallel corpus","anchor":"nematusscorepy--use-an-existing-model-to-score-a-parallel-corpus","htmlText":"nematus/score.py : use an existing model to score a parallel corpus"},{"level":4,"text":"nematus/rescore.py : use an existing model to rescore an n-best list.","anchor":"nematusrescorepy--use-an-existing-model-to-rescore-an-n-best-list","htmlText":"nematus/rescore.py : use an existing model to rescore an n-best list."},{"level":2,"text":"PUBLICATIONS","anchor":"publications","htmlText":"PUBLICATIONS"},{"level":2,"text":"ACKNOWLEDGMENTS","anchor":"acknowledgments","htmlText":"ACKNOWLEDGMENTS"}],"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2Fkhayrallah%2Fnematus-lattice-search"}},{"displayName":"LICENSE","repoName":"nematus-lattice-search","refName":"master","path":"LICENSE","preferredFileType":"license","tabName":"BSD-3-Clause","richText":null,"loaded":false,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":null,"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2Fkhayrallah%2Fnematus-lattice-search"}}],"overviewFilesProcessingTime":0}},"appPayload":{"helpUrl":"https://docs.github.com","findFileWorkerPath":"/assets-cdn/worker/find-file-worker-1583894afd38.js","findInFileWorkerPath":"/assets-cdn/worker/find-in-file-worker-67668e8c2caa.js","githubDevUrl":null,"enabled_features":{"code_nav_ui_events":false,"overview_shared_code_dropdown_button":false,"react_blob_overlay":false,"copilot_conversational_ux_embedding_update":false,"copilot_smell_icebreaker_ux":true,"copilot_workspace":false,"accessible_code_button":true}}}}</script> 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dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">NEMATUS</h2><a id="user-content-nematus" class="anchor" aria-label="Permalink: NEMATUS" href="#nematus"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">This fork of Nematus contains the code for: Neural Lattice Search for Domain Adaptation in Machine Translation Huda Khayrallah Gaurav Kumar Kevin Duh Matt Post Philipp Koehn {huda, gkumar, kevinduh, post, phi}@cs.jhu.edu</p> <p dir="auto">USAGE: decoding can be perfomed with an existing nematus model.</p> <p dir="auto">Rescoring of a Moses word graph using a translation model is performed with: nematus/rescore_graph.py The Moses word graph is obtained by adding the flags "-osg " to Moses. It then must be converted to fst format using: scripts/gen_lattice.sh</p> <hr> <p dir="auto">Attention-based encoder-decoder model for neural machine translation</p> <p dir="auto">This package is based on the dl4mt-tutorial by Kyunghyun Cho et al. ( <a href="https://github.com/nyu-dl/dl4mt-tutorial">https://github.com/nyu-dl/dl4mt-tutorial</a> ). It was used to produce top-scoring systems at the WMT 16 shared translation task.</p> <p dir="auto">The changes to Nematus include:</p> <ul dir="auto"> <li>arbitrary input features (factored neural machine translation) <a href="http://www.statmt.org/wmt16/pdf/W16-2209.pdf" rel="nofollow">http://www.statmt.org/wmt16/pdf/W16-2209.pdf</a></li> <li>ensemble decoding (and new translation API to support it)</li> <li>dropout on all layers (Gal, 2015) <a href="http://arxiv.org/abs/1512.05287" rel="nofollow">http://arxiv.org/abs/1512.05287</a></li> <li>minimum risk training (Shen et al, 2016) <a href="http://aclweb.org/anthology/P16-1159" rel="nofollow">http://aclweb.org/anthology/P16-1159</a></li> <li>tied embeddings (Press and Wolf, 2016) <a href="https://arxiv.org/abs/1608.05859" rel="nofollow">https://arxiv.org/abs/1608.05859</a></li> <li>command line interface for training</li> <li>automatic training set reshuffling between epochs</li> <li>n-best output for decoder</li> <li>more output options (attention weights; word-level probabilities) and visualization scripts</li> <li>performance improvements to decoder</li> <li>rescoring support</li> <li>execute arbitrary validation scripts (for BLEU early stopping)</li> <li>vocabulary files and model parameters are stored in JSON format (backward-compatible loading)</li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">SUPPORT</h2><a id="user-content-support" class="anchor" aria-label="Permalink: SUPPORT" href="#support"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">For general support requests, there is a Google Groups mailing list at <a href="https://groups.google.com/d/forum/nematus-support" rel="nofollow">https://groups.google.com/d/forum/nematus-support</a> . You can also send an e-mail to <a href="mailto:nematus-support@googlegroups.com">nematus-support@googlegroups.com</a> .</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">INSTALLATION</h2><a id="user-content-installation" class="anchor" aria-label="Permalink: INSTALLATION" href="#installation"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Nematus requires the following packages:</p> <ul dir="auto"> <li>Python >= 2.7</li> <li>numpy</li> <li>Theano >= 0.7 (and its dependencies).</li> </ul> <p dir="auto">we recommend executing the following command in a Python virtual environment: <code>pip install numpy numexpr cython tables theano</code></p> <p dir="auto">the following packages are optional, but <em>highly</em> recommended</p> <ul dir="auto"> <li>CUDA >= 7 (only GPU training is sufficiently fast)</li> <li>cuDNN >= 4 (speeds up training substantially)</li> </ul> <p dir="auto">you can run Nematus locally. To install it, execute <code>python setup.py install</code></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">DOCKER USAGE</h2><a id="user-content-docker-usage" class="anchor" aria-label="Permalink: DOCKER USAGE" href="#docker-usage"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">You can also create docker image by running following command, where you change <code>suffix</code> to either <code>cpu</code> or <code>gpu</code>:</p> <p dir="auto"><code>docker build -t nematus-docker -f Dockerfile.suffix .</code></p> <p dir="auto">To run a CPU docker instance with the current working directory shared with the Docker container, execute:</p> <p dir="auto"><code>docker run -v `pwd`:/playground -it nematus-docker</code></p> <p dir="auto">For GPU you need to have nvidia-docker installed and run:</p> <p dir="auto"><code>nvidia-docker run -v `pwd`:/playground -it nematus-docker</code></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">TRAINING SPEED</h2><a id="user-content-training-speed" class="anchor" aria-label="Permalink: TRAINING SPEED" href="#training-speed"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Training speed depends heavily on having appropriate hardware (ideally a recent NVIDIA GPU), and having installed the appropriate software packages.</p> <p dir="auto">To test your setup, we provide some speed benchmarks with `test/test_train.sh', on an Intel Xeon CPU E5-2620 v3, with a Nvidia GeForce GTX 1080 GPU and CUDA 8.0:</p> <p dir="auto">CPU, theano 0.8.2:</p> <p dir="auto">THEANO_FLAGS=mode=FAST_RUN,floatX=float32,device=cpu ./test_train.sh</p> <blockquote> <blockquote> <p dir="auto">2.37 sentences/s</p> </blockquote> </blockquote> <p dir="auto">GPU, no CuDNN, theano 0.8.2:</p> <p dir="auto">THEANO_FLAGS=mode=FAST_RUN,floatX=float32,device=gpu ./test_train.sh</p> <blockquote> <blockquote> <p dir="auto">71.62 sentences/s</p> </blockquote> </blockquote> <p dir="auto">GPU, CuDNN 5.1, theano 0.8.2:</p> <p dir="auto">THEANO_FLAGS=mode=FAST_RUN,floatX=float32,device=gpu ./test_train.sh</p> <blockquote> <blockquote> <p dir="auto">139.73 sentences/s</p> </blockquote> </blockquote> <p dir="auto">GPU, CuDNN 5.1, theano 0.9.0dev5.dev-d5520e:</p> <p dir="auto">THEANO_FLAGS=mode=FAST_RUN,floatX=float32,device=gpu ./test_train.sh</p> <blockquote> <blockquote> <p dir="auto">173.15 sentences/s</p> </blockquote> </blockquote> <p dir="auto">GPU, CuDNN 5.1, theano 0.9.0dev5.dev-d5520e, new GPU backend:</p> <p dir="auto">THEANO_FLAGS=mode=FAST_RUN,floatX=float32,device=cuda ./test_train.sh</p> <blockquote> <blockquote> <p dir="auto">209.21 sentences/s</p> </blockquote> </blockquote> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">USAGE INSTRUCTIONS</h2><a id="user-content-usage-instructions" class="anchor" aria-label="Permalink: USAGE INSTRUCTIONS" href="#usage-instructions"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">execute nematus/nmt.py to train a model.</p> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">data sets; model loading and saving</h4><a id="user-content-data-sets-model-loading-and-saving" class="anchor" aria-label="Permalink: data sets; model loading and saving" href="#data-sets-model-loading-and-saving"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <markdown-accessiblity-table><table> <thead> <tr> <th>parameter</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>--datasets PATH PATH</td> <td>parallel training corpus (source and target)</td> </tr> <tr> <td>--dictionaries PATH [PATH ...]</td> <td>network vocabularies (one per source factor, plus target vocabulary)</td> </tr> <tr> <td>--model PATH</td> <td>model file name (default: model.npz)</td> </tr> <tr> <td>--saveFreq INT</td> <td>save frequency (default: 30000)</td> </tr> <tr> <td>--reload</td> <td>load existing model (if '--model' points to existing model)</td> </tr> <tr> <td>--overwrite</td> <td>write all models to same file</td> </tr> </tbody> </table></markdown-accessiblity-table> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">network parameters</h4><a id="user-content-network-parameters" class="anchor" aria-label="Permalink: network parameters" href="#network-parameters"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <markdown-accessiblity-table><table> <thead> <tr> <th>parameter</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>--dim_word INT</td> <td>embedding layer size (default: 512)</td> </tr> <tr> <td>--dim INT</td> <td>hidden layer size (default: 1000)</td> </tr> <tr> <td>--n_words_src INT</td> <td>source vocabulary size (default: None)</td> </tr> <tr> <td>--n_words INT</td> <td>target vocabulary size (default: None)</td> </tr> <tr> <td>--factors INT</td> <td>number of input factors (default: 1)</td> </tr> <tr> <td>--dim_per_factor INT [INT ...]</td> <td>list of word vector dimensionalities (one per factor): '--dim_per_factor 250 200 50' for total dimensionality of 500 (default: None)</td> </tr> <tr> <td>--use_dropout</td> <td>use dropout layer (default: False)</td> </tr> <tr> <td>--dropout_embedding FLOAT</td> <td>dropout for input embeddings (0: no dropout) (default: 0.2)</td> </tr> <tr> <td>--dropout_hidden FLOAT</td> <td>dropout for hidden layer (0: no dropout) (default: 0.2)</td> </tr> <tr> <td>--dropout_source FLOAT</td> <td>dropout source words (0: no dropout) (default: 0)</td> </tr> <tr> <td>--dropout_target FLOAT</td> <td>dropout target words (0: no dropout) (default: 0)</td> </tr> <tr> <td>--tie_decoder_embeddings</td> <td>tie the input embeddings of the decoder with the softmax output embeddings</td> </tr> <tr> <td>--tie_encoder_decoder_embeddings</td> <td>tie the input embeddings of the encoder and the decoder (first factor only). Source and target vocabulary size must the same</td> </tr> </tbody> </table></markdown-accessiblity-table> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">training parameters</h4><a id="user-content-training-parameters" class="anchor" aria-label="Permalink: training parameters" href="#training-parameters"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <markdown-accessiblity-table><table> <thead> <tr> <th>parameter</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>--maxlen INT</td> <td>maximum sequence length (default: 100)</td> </tr> <tr> <td>--optimizer {adam,adadelta,rmsprop,sgd}</td> <td>optimizer (default: adam)</td> </tr> <tr> <td>--batch_size INT</td> <td>minibatch size (default: 80)</td> </tr> <tr> <td>--max_epochs INT</td> <td>maximum number of epochs (default: 5000)</td> </tr> <tr> <td>--finish_after INT</td> <td>maximum number of updates (minibatches) (default: 10000000)</td> </tr> <tr> <td>--decay_c FLOAT</td> <td>L2 regularization penalty (default: 0)</td> </tr> <tr> <td>--map_decay_c FLOAT</td> <td>L2 regularization penalty towards original weights (default: 0)</td> </tr> <tr> <td>--alpha_c FLOAT</td> <td>alignment regularization (default: 0)</td> </tr> <tr> <td>--clip_c FLOAT</td> <td>gradient clipping threshold (default: 1)</td> </tr> <tr> <td>--lrate FLOAT</td> <td>learning rate (default: 0.0001)</td> </tr> <tr> <td>--no_shuffle</td> <td>disable shuffling of training data (for each epoch)</td> </tr> <tr> <td>--no_sort_by_length</td> <td>do not sort sentences in maxibatch by length</td> </tr> <tr> <td>--maxibatch_size INT</td> <td>size of maxibatch (number of minibatches that are sorted by length) (default: 20)</td> </tr> <tr> <td>--objective {CE,MRT}</td> <td>training objective. CE: cross-entropy minimization (default); MRT: Minimum Risk Training (<a href="https://www.aclweb.org/anthology/P/P16/P16-1159.pdf" rel="nofollow">https://www.aclweb.org/anthology/P/P16/P16-1159.pdf</a>)</td> </tr> </tbody> </table></markdown-accessiblity-table> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">validation parameters</h4><a id="user-content-validation-parameters" class="anchor" aria-label="Permalink: validation parameters" href="#validation-parameters"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <markdown-accessiblity-table><table> <thead> <tr> <th>parameter</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>--valid_datasets PATH PATH</td> <td>parallel validation corpus (source and target)</td> </tr> <tr> <td>--valid_batch_size INT</td> <td>validation minibatch size (default: 80)</td> </tr> <tr> <td>--validFreq INT</td> <td>validation frequency (default: 10000)</td> </tr> <tr> <td>--patience INT</td> <td>early stopping patience (default: 10)</td> </tr> <tr> <td>--external_validation_script PATH</td> <td>location of validation script (to run your favorite metric for validation) (default: None)</td> </tr> </tbody> </table></markdown-accessiblity-table> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">display parameters</h4><a id="user-content-display-parameters" class="anchor" aria-label="Permalink: display parameters" href="#display-parameters"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <markdown-accessiblity-table><table> <thead> <tr> <th>parameter</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>--dispFreq INT</td> <td>display loss after INT updates (default: 1000)</td> </tr> <tr> <td>--sampleFreq INT</td> <td>display some samples after INT updates (default: 10000)</td> </tr> </tbody> </table></markdown-accessiblity-table> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">minimum risk training parameters</h4><a id="user-content-minimum-risk-training-parameters" class="anchor" aria-label="Permalink: minimum risk training parameters" href="#minimum-risk-training-parameters"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <markdown-accessiblity-table><table> <thead> <tr> <th>parameter</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>--mrt_alpha FLOAT</td> <td>MRT alpha (default: 0.005)</td> </tr> <tr> <td>--mrt_samples INT</td> <td>samples per source sentence (default: 100)</td> </tr> <tr> <td>--mrt_samples_meanloss INT</td> <td>draw n independent samples to calculate mean loss (which is subtracted from loss) (default: 10)</td> </tr> <tr> <td>--mrt_loss STR</td> <td>loss used in MRT (default: SENTENCEBLEU n=4)</td> </tr> <tr> <td>--mrt_reference</td> <td>add reference to MRT samples.</td> </tr> <tr> <td>--mrt_ml_mix</td> <td>mix in ML objective in MRT training with this scaling factor (default: 0)</td> </tr> </tbody> </table></markdown-accessiblity-table> <p dir="auto">more instructions to train a model, including a sample configuration and preprocessing scripts, are provided in <a href="https://github.com/rsennrich/wmt16-scripts">https://github.com/rsennrich/wmt16-scripts</a></p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">USING A TRAINED MODEL</h3><a id="user-content-using-a-trained-model" class="anchor" aria-label="Permalink: USING A TRAINED MODEL" href="#using-a-trained-model"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto"><code>nematus/translate.py</code> : use an existing model to translate a source text</h4><a id="user-content-nematustranslatepy--use-an-existing-model-to-translate-a-source-text" class="anchor" aria-label="Permalink: nematus/translate.py : use an existing model to translate a source text" href="#nematustranslatepy--use-an-existing-model-to-translate-a-source-text"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <markdown-accessiblity-table><table> <thead> <tr> <th>parameter</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>-k K</td> <td>Beam size (default: 5))</td> </tr> <tr> <td>-p P</td> <td>Number of processes (default: 5))</td> </tr> <tr> <td>-n</td> <td>Normalize scores by sentence length</td> </tr> <tr> <td>-v</td> <td>verbose mode.</td> </tr> <tr> <td>--models MODELS [MODELS ...], -m MODELS [MODELS ...]</td> <td>model to use. Provide multiple models (with same vocabulary) for ensemble decoding</td> </tr> <tr> <td>--input PATH, -i PATH</td> <td>Input file (default: standard input)</td> </tr> <tr> <td>--output PATH, -o PATH</td> <td>Output file (default: standard output)</td> </tr> <tr> <td>--output_alignment PATH, -a PATH</td> <td>Output file for alignment weights (default: standard output)</td> </tr> <tr> <td>--json_alignment</td> <td>Output alignment in json format</td> </tr> <tr> <td>--n-best</td> <td>Write n-best list (of size k)</td> </tr> <tr> <td>--suppress-unk</td> <td>Suppress hypotheses containing UNK.</td> </tr> <tr> <td>--print-word-probabilities, -wp</td> <td>Print probabilities of each word</td> </tr> <tr> <td>--search_graph, -sg</td> <td>Output file for search graph rendered as PNG image</td> </tr> </tbody> </table></markdown-accessiblity-table> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto"><code>nematus/score.py</code> : use an existing model to score a parallel corpus</h4><a id="user-content-nematusscorepy--use-an-existing-model-to-score-a-parallel-corpus" class="anchor" aria-label="Permalink: nematus/score.py : use an existing model to score a parallel corpus" href="#nematusscorepy--use-an-existing-model-to-score-a-parallel-corpus"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <markdown-accessiblity-table><table> <thead> <tr> <th>parameter</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>-b B</td> <td>Minibatch size (default: 80))</td> </tr> <tr> <td>-n</td> <td>Normalize scores by sentence length</td> </tr> <tr> <td>-v</td> <td>verbose mode.</td> </tr> <tr> <td>--models MODELS [MODELS ...], -m MODELS [MODELS ...]</td> <td>model to use. Provide multiple models (with same vocabulary) for ensemble decoding</td> </tr> <tr> <td>--source PATH, -s PATH</td> <td>Source text file</td> </tr> <tr> <td>--target PATH, -t PATH</td> <td>Target text file</td> </tr> <tr> <td>--output PATH, -o PATH</td> <td>Output file (default: standard output)</td> </tr> <tr> <td>--walign, -w</td> <td>Whether to store the alignment weights or not. If specified, weights will be saved in .alignment</td> </tr> </tbody> </table></markdown-accessiblity-table> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto"><code>nematus/rescore.py</code> : use an existing model to rescore an n-best list.</h4><a id="user-content-nematusrescorepy--use-an-existing-model-to-rescore-an-n-best-list" class="anchor" aria-label="Permalink: nematus/rescore.py : use an existing model to rescore an n-best list." href="#nematusrescorepy--use-an-existing-model-to-rescore-an-n-best-list"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">The n-best list is assumed to have the same format as Moses:</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="sentence-ID (starting from 0) ||| translation ||| scores"><pre class="notranslate"><code>sentence-ID (starting from 0) ||| translation ||| scores </code></pre></div> <p dir="auto">new scores will be appended to the end. <code>rescore.py</code> has the same arguments as <code>score.py</code>, with the exception of this additional parameter:</p> <markdown-accessiblity-table><table> <thead> <tr> <th>parameter</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>--input PATH, -i PATH</td> <td>Input n-best list file (default: standard input)</td> </tr> </tbody> </table></markdown-accessiblity-table> <p dir="auto">sample models, and instructions on using them for translation, are provided in the <code>test</code> directory, and at <a href="http://statmt.org/rsennrich/wmt16_systems/" rel="nofollow">http://statmt.org/rsennrich/wmt16_systems/</a></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">PUBLICATIONS</h2><a id="user-content-publications" class="anchor" aria-label="Permalink: PUBLICATIONS" href="#publications"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">if you use Nematus, please cite the following paper:</p> <p dir="auto">Rico Sennrich, Orhan Firat, Kyunghyun Cho, Alexandra Birch, Barry Haddow, Julian Hitschler, Marcin Junczys-Dowmunt, Samuel Läubli, Antonio Valerio Miceli Barone, Jozef Mokry and Maria Nadejde (2017): Nematus: a Toolkit for Neural Machine Translation. In Proceedings of the Demonstrations at the 15th Conference of the European Chapter of the Association for Computational Linguistics, Valencia, Spain.</p> <p dir="auto">@inproceedings{nematus, address = "Valencia, Spain", author = "Sennrich, Rico and Firat, Orhan and Cho, Kyunghyun and Birch, Alexandra and Haddow, Barry and Hitschler, Julian and Junczys-Dowmunt, Marcin and L{"a}ubli, Samuel and {Miceli Barone}, Antonio Valerio and Mokry, Jozef and Nadejde, Maria", booktitle = "{Proceedings of the Demonstrations at the 15th Conference of the European Chapter of the Association for Computational Linguistics}", title = "{Nematus: a Toolkit for Neural Machine Translation}", year = "2017" }</p> <p dir="auto">the code is based on the following model:</p> <p dir="auto">Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio (2015): Neural Machine Translation by Jointly Learning to Align and Translate, Proceedings of the International Conference on Learning Representations (ICLR).</p> <p dir="auto">please refer to the Nematus paper for a description of implementation differences</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">ACKNOWLEDGMENTS</h2><a id="user-content-acknowledgments" class="anchor" aria-label="Permalink: ACKNOWLEDGMENTS" href="#acknowledgments"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreements 645452 (QT21), 644333 (TraMOOC), 644402 (HimL) and 688139 (SUMMA).</p> </article></div></div></div></div></div> <!-- --> <!-- --> <script type="application/json" id="__PRIMER_DATA_:R0:__">{"resolvedServerColorMode":"day"}</script></div> </react-partial> <input type="hidden" data-csrf="true" value="aqfH3xy3Az3tqlub4Ho6dYHVA1wWDlK28+ffg8S6u3RvGoa3UNJT+xHsV7Er74nBld0mg05plK389yzGGm/F1A==" /> </div> <div data-view-component="true" class="Layout-sidebar"> <div class="BorderGrid about-margin" data-pjax> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <div class="hide-sm hide-md"> <h2 class="mb-3 h4">About</h2> <p class="f4 my-3"> Open-Source Neural Machine Translation in Theano </p> <h3 class="sr-only">Resources</h3> <div class="mt-2"> <a class="Link--muted" data-analytics-event="{"category":"Repository 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<li class="d-inline"> <span class="d-inline-flex flex-items-center flex-nowrap text-small mr-3"> <svg style="color:#3572A5;" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-dot-fill mr-2"> <path d="M8 4a4 4 0 1 1 0 8 4 4 0 0 1 0-8Z"></path> </svg> <span class="color-fg-default text-bold mr-1">Python</span> <span>74.0%</span> </span> </li> <li class="d-inline"> <span class="d-inline-flex flex-items-center flex-nowrap text-small mr-3"> <svg style="color:#0298c3;" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-dot-fill mr-2"> <path d="M8 4a4 4 0 1 1 0 8 4 4 0 0 1 0-8Z"></path> </svg> <span class="color-fg-default text-bold mr-1">Perl</span> <span>7.3%</span> </span> </li> <li class="d-inline"> <span class="d-inline-flex flex-items-center flex-nowrap text-small mr-3"> <svg style="color:#c065db;" aria-hidden="true" height="16" 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0-8Z"></path> </svg> <span class="color-fg-default text-bold mr-1">PHP</span> <span>3.5%</span> </span> </li> <li class="d-inline"> <span class="d-inline-flex flex-items-center flex-nowrap text-small mr-3"> <svg style="color:#89e051;" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-dot-fill mr-2"> <path d="M8 4a4 4 0 1 1 0 8 4 4 0 0 1 0-8Z"></path> </svg> <span class="color-fg-default text-bold mr-1">Shell</span> <span>2.2%</span> </span> </li> <li class="d-inline"> <span class="d-inline-flex flex-items-center flex-nowrap text-small mr-3"> <svg style="color:#ededed;" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-dot-fill mr-2"> <path d="M8 4a4 4 0 1 1 0 8 4 4 0 0 1 0-8Z"></path> </svg> <span class="color-fg-default text-bold mr-1">Other</span> <span>1.8%</span> </span> </li> </ul> </div> </div> </div> </div> </div></div> </div> 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