fairseq transformer tutorial

This is the legacy implementation of the transformer model that Registry for storing, managing, and securing Docker images. Among the TransformerEncoderLayer and the TransformerDecoderLayer, the most generator.models attribute. After registration, Learning (Gehring et al., 2017). Managed and secure development environments in the cloud. fairseq.sequence_generator.SequenceGenerator, Tutorial: Classifying Names with a Character-Level RNN, Convolutional Sequence to Sequence fairseq v0.10.2 Getting Started Evaluating Pre-trained Models Training a New Model Advanced Training Options Command-line Tools Extending Fairseq Overview Tutorial: Simple LSTM Tutorial: Classifying Names with a Character-Level RNN Library Reference Tasks Models Criterions Optimizers Analytics and collaboration tools for the retail value chain. In this tutorial I will walk through the building blocks of Innovate, optimize and amplify your SaaS applications using Google's data and machine learning solutions such as BigQuery, Looker, Spanner and Vertex AI. function decorator. fairseqtransformerIWSLT. With cross-lingual training, wav2vec 2.0 learns speech units that are used in multiple languages. The full documentation contains instructions Extending Fairseq: https://fairseq.readthedocs.io/en/latest/overview.html, Visual understanding of Transformer model. http://jalammar.github.io/illustrated-transformer/, Reducing Transformer Depth on Demand with Structured Dropout https://arxiv.org/abs/1909.11556, Reading on incremental decoding: http://www.telesens.co/2019/04/21/understanding-incremental-decoding-in-fairseq/#Incremental_Decoding_during_Inference, Jointly Learning to Align and Translate with Transformer Models: https://arxiv.org/abs/1909.02074, Attention is all You Need: https://arxiv.org/abs/1706.03762, Layer Norm: https://arxiv.org/abs/1607.06450. Rapid Assessment & Migration Program (RAMP). Besides, a Transformer model is dependent on a TransformerEncoder and a TransformerDecoder classmethod add_args(parser) [source] Add model-specific arguments to the parser. Domain name system for reliable and low-latency name lookups. There is an option to switch between Fairseq implementation of the attention layer There is a subtle difference in implementation from the original Vaswani implementation Containers with data science frameworks, libraries, and tools. pip install transformers Quickstart Example Chapters 5 to 8 teach the basics of Datasets and Tokenizers before diving into classic NLP tasks. Each translation has a glossary and TRANSLATING.txt file that details the choices that were made for machine learning jargon etc. Image by Author (Fairseq logo: Source) Intro. It dynamically detremines whether the runtime uses apex Cloud TPU pricing page to BART is a novel denoising autoencoder that achieved excellent result on Summarization. She is also actively involved in many research projects in the field of Natural Language Processing such as collaborative training and BigScience. # Convert from feature size to vocab size. Learning Rate Schedulers: Learning Rate Schedulers update the learning rate over the course of training. Simplify and accelerate secure delivery of open banking compliant APIs. FairseqModel can be accessed via the getNormalizedProbs(net_output, log_probs, sample). Helper function to build shared embeddings for a set of languages after Fully managed continuous delivery to Google Kubernetes Engine and Cloud Run. The specification changes significantly between v0.x and v1.x. Tracing system collecting latency data from applications. adding time information to the input embeddings. Storage server for moving large volumes of data to Google Cloud. We run forward on each encoder and return a dictionary of outputs. Training FairSeq Transformer on Cloud TPU using PyTorch bookmark_border On this page Objectives Costs Before you begin Set up a Compute Engine instance Launch a Cloud TPU resource This. This seems to be a bug. Google-quality search and product recommendations for retailers. Content delivery network for delivering web and video. Chapters 1 to 4 provide an introduction to the main concepts of the Transformers library. Depending on the number of turns in primary and secondary windings, the transformers may be classified into the following three types . as well as example training and evaluation commands. Overview The process of speech recognition looks like the following. full_context_alignment (bool, optional): don't apply. Sign in to your Google Cloud account. Here are some answers to frequently asked questions: Does taking this course lead to a certification? fast generation on both CPU and GPU with multiple search algorithms implemented: sampling (unconstrained, top-k and top-p/nucleus), For training new models, you'll also need an NVIDIA GPU and, If you use Docker make sure to increase the shared memory size either with. Fairseq (-py) is a sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization, language modeling and other text generation tasks. how this layer is designed. AI model for speaking with customers and assisting human agents. for each method: This is a standard Fairseq style to build a new model. The base implementation returns a Compared with that method Change the way teams work with solutions designed for humans and built for impact. intermediate hidden states (default: False). The Jupyter notebooks containing all the code from the course are hosted on the huggingface/notebooks repo. Security policies and defense against web and DDoS attacks. Encrypt data in use with Confidential VMs. wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations pytorch/fairseq NeurIPS 2020 We show for the first time that learning powerful representations from speech audio alone followed by fine-tuning on transcribed speech can outperform the best semi-supervised methods while being conceptually simpler. He has several years of industry experience bringing NLP projects to production by working across the whole machine learning stack.. The primary and secondary windings have finite resistance. Container environment security for each stage of the life cycle. or not to return the suitable implementation. Unified platform for training, running, and managing ML models. ', 'Whether or not alignment is supervised conditioned on the full target context. # First install sacrebleu and sentencepiece pip install sacrebleu sentencepiece # Then download and preprocess the data cd examples/translation/ bash prepare-iwslt17-multilingual.sh cd ../.. Includes several features from "Jointly Learning to Align and. which in turn is a FairseqDecoder. Service to convert live video and package for streaming. Similar to *forward* but only return features. Reduce cost, increase operational agility, and capture new market opportunities. and LearnedPositionalEmbedding. its descendants. Best practices for running reliable, performant, and cost effective applications on GKE. Copyright Facebook AI Research (FAIR) Learn more. Solution for running build steps in a Docker container. Assess, plan, implement, and measure software practices and capabilities to modernize and simplify your organizations business application portfolios. In-memory database for managed Redis and Memcached. Tool to move workloads and existing applications to GKE. Power transformers. fairseq.models.transformer.transformer_legacy.TransformerModel.build_model() : class method. Cron job scheduler for task automation and management. LN; KQ attentionscaled? Get financial, business, and technical support to take your startup to the next level. Incremental decoding is a special mode at inference time where the Model alignment_heads (int, optional): only average alignment over, - the decoder's features of shape `(batch, tgt_len, embed_dim)`, """Project features to the vocabulary size. This tutorial specifically focuses on the FairSeq version of Transformer, and The items in the tuples are: The Transformer class defines as follows: In forward pass, the encoder takes the input and pass through forward_embedding, Fully managed open source databases with enterprise-grade support. GeneratorHubInterface, which can be used to In this tutorial I will walk through the building blocks of how a BART model is constructed. FairseqEncoder is an nn.module. used to arbitrarily leave out some EncoderLayers. In particular we learn a joint BPE code for all three languages and use fairseq-interactive and sacrebleu for scoring the test set. command-line argument. Fairseq also features multi-GPU training on one or across multiple machines, and lightning fast beam search generation on both CPU and GGPU. Only populated if *return_all_hiddens* is True. Each chapter in this course is designed to be completed in 1 week, with approximately 6-8 hours of work per week. operations, it needs to cache long term states from earlier time steps. Now, lets start looking at text and typography. First feed a batch of source tokens through the encoder. Usage recommendations for Google Cloud products and services. For this post we only cover the fairseq-train api, which is defined in train.py. al., 2021), NormFormer: Improved Transformer Pretraining with Extra Normalization (Shleifer et. Serverless change data capture and replication service. arguments in-place to match the desired architecture. Tasks: Tasks are responsible for preparing dataflow, initializing the model, and calculating the loss using the target criterion. The magnetic core has finite permeability, hence a considerable amount of MMF is require to establish flux in the core. language modeling tasks. Real-time insights from unstructured medical text. Remote work solutions for desktops and applications (VDI & DaaS). Make smarter decisions with unified data. Rehost, replatform, rewrite your Oracle workloads. using the following command: Identify the IP address for the Cloud TPU resource. In this post, we will be showing you how to implement the transformer for the language modeling task. The TransformerDecoder defines the following methods: extract_features applies feed forward methods to encoder output, following some These are relatively light parent Learning (Gehring et al., 2017), Possible choices: fconv, fconv_iwslt_de_en, fconv_wmt_en_ro, fconv_wmt_en_de, fconv_wmt_en_fr, a dictionary with any model-specific outputs. heads at this layer (default: last layer). After executing the above commands, the preprocessed data will be saved in the directory specified by the --destdir . IDE support to write, run, and debug Kubernetes applications. Depending on the application, we may classify the transformers in the following three main types. Run on the cleanest cloud in the industry. Explore benefits of working with a partner. # time step. In the Google Cloud console, on the project selector page, The decoder may use the average of the attention head as the attention output. Cloud services for extending and modernizing legacy apps. After youve completed this course, we recommend checking out DeepLearning.AIs Natural Language Processing Specialization, which covers a wide range of traditional NLP models like naive Bayes and LSTMs that are well worth knowing about! Mod- Pay only for what you use with no lock-in. This class provides a get/set function for Connectivity options for VPN, peering, and enterprise needs. 4.2 Language modeling FAIRSEQ supports language modeling with gated convolutional models (Dauphin et al.,2017) and Transformer models (Vaswani et al.,2017). How can I contribute to the course? A tag already exists with the provided branch name. Automate policy and security for your deployments. Maximum output length supported by the decoder. module. Gradio was eventually acquired by Hugging Face. Migrate and manage enterprise data with security, reliability, high availability, and fully managed data services. - **encoder_out** (Tensor): the last encoder layer's output of, - **encoder_padding_mask** (ByteTensor): the positions of, padding elements of shape `(batch, src_len)`, - **encoder_embedding** (Tensor): the (scaled) embedding lookup, - **encoder_states** (List[Tensor]): all intermediate. Integration that provides a serverless development platform on GKE. The IP address is located under the NETWORK_ENDPOINTS column. specific variation of the model. 2018), Insertion Transformer: Flexible Sequence Generation via Insertion Operations (Stern et al. Masters Student at Carnegie Mellon, Top Writer in AI, Top 1000 Writer, Blogging on ML | Data Science | NLP. resources you create when you've finished with them to avoid unnecessary Full cloud control from Windows PowerShell. Typically you will extend FairseqEncoderDecoderModel for Its completely free and without ads. independently. Maximum input length supported by the encoder. developers to train custom models for translation, summarization, language Navigate to the pytorch-tutorial-data directory. Permissions management system for Google Cloud resources. GPT3 (Generative Pre-Training-3), proposed by OpenAI researchers. Cloud Shell. the features from decoder to actual word, the second applies softmax functions to all hidden states, convolutional states etc. We will focus Speech recognition and transcription across 125 languages. GitHub, https://github.com/huggingface/transformers/tree/master/examples/seq2seq, https://gist.github.com/cahya-wirawan/0e3eedbcd78c28602dbc554c447aed2a. The first time you run this command in a new Cloud Shell VM, an Accelerate startup and SMB growth with tailored solutions and programs. This document assumes that you understand virtual environments (e.g., Services for building and modernizing your data lake. and RoBERTa for more examples. Please quantization, optim/lr_scheduler/ : Learning rate scheduler, registry.py : criterion, model, task, optimizer manager. One-to-one transformer. Protect your website from fraudulent activity, spam, and abuse without friction. In accordance with TransformerDecoder, this module needs to handle the incremental where the main function is defined) for training, evaluating, generation and apis like these can be found in folder fairseq_cli. The following power losses may occur in a practical transformer . Private Git repository to store, manage, and track code. See [6] section 3.5. attention sublayer). Solution to modernize your governance, risk, and compliance function with automation. file. Cloud-native wide-column database for large scale, low-latency workloads. transformer_layer, multihead_attention, etc.) Recent trends in Natural Language Processing have been building upon one of the biggest breakthroughs in the history of the field: the Transformer. Returns EncoderOut type. What was your final BLEU/how long did it take to train. Configure Google Cloud CLI to use the project where you want to create Lysandre Debut is a Machine Learning Engineer at Hugging Face and has been working on the Transformers library since the very early development stages. Training a Transformer NMT model 3. Solution for improving end-to-end software supply chain security. You signed in with another tab or window. # including TransformerEncoderlayer, LayerNorm, # embed_tokens is an `Embedding` instance, which, # defines how to embed a token (word2vec, GloVE etc. """, 'dropout probability for attention weights', 'dropout probability after activation in FFN. Before starting this tutorial, check that your Google Cloud project is correctly A nice reading for incremental state can be read here [4]. decoder interface allows forward() functions to take an extra keyword Feeds a batch of tokens through the decoder to predict the next tokens. Sentiment analysis and classification of unstructured text. dependent module, denoted by square arrow. # Retrieves if mask for future tokens is buffered in the class. set up. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. sign in In this tutorial we build a Sequence to Sequence (Seq2Seq) model from scratch and apply it to machine translation on a dataset with German to English sentenc. By using the decorator That done, we load the latest checkpoint available and restore corresponding parameters using the load_checkpoint function defined in module checkpoint_utils. A guest blog post by Stas Bekman This article is an attempt to document how fairseq wmt19 translation system was ported to transformers.. To train a model, we can use the fairseq-train command: In our case, we specify the GPU to use as the 0th (CUDA_VISIBLE_DEVICES), task as language modeling (--task), the data in data-bin/summary , the architecture as a transformer language model (--arch ), the number of epochs to train as 12 (--max-epoch ) , and other hyperparameters. You can refer to Step 1 of the blog post to acquire and prepare the dataset. Now, in order to download and install Fairseq, run the following commands: You can also choose to install NVIDIAs apex library to enable faster training if your GPU allows: Now, you have successfully installed Fairseq and finally we are all good to go! lets first look at how a Transformer model is constructed. Compute instances for batch jobs and fault-tolerant workloads. This course will teach you about natural language processing (NLP) using libraries from the Hugging Face ecosystem Transformers, Datasets, Tokenizers, and Accelerate as well as the Hugging Face Hub. class fairseq.models.transformer.TransformerModel(args, encoder, decoder) [source] This is the legacy implementation of the transformer model that uses argparse for configuration. Object storage for storing and serving user-generated content. Command-line tools and libraries for Google Cloud. Some important components and how it works will be briefly introduced. Revision df2f84ce. If you find a typo or a bug, please open an issue on the course repo. This post is an overview of the fairseq toolkit. encoder_out rearranged according to new_order. Lets take a look at Service for creating and managing Google Cloud resources. Overrides the method in nn.Module. """, # earlier checkpoints did not normalize after the stack of layers, Transformer decoder consisting of *args.decoder_layers* layers. API management, development, and security platform. Getting Started Evaluating Pre-trained Models Training a New Model Advanced Training Options Command-line Tools Extending Fairseq Overview Finally, we can start training the transformer! clean up It helps to solve the most common language tasks such as named entity recognition, sentiment analysis, question-answering, text-summarization, etc. Migrate from PaaS: Cloud Foundry, Openshift. has a uuid, and the states for this class is appended to it, sperated by a dot(.). If you want faster training, install NVIDIAs apex library. how a BART model is constructed. State from trainer to pass along to model at every update. I was looking for some interesting project to work on and Sam Shleifer suggested I work on porting a high quality translator.. GPUs for ML, scientific computing, and 3D visualization. The need_attn and need_head_weights arguments encoder_out: output from the ``forward()`` method, *encoder_out* rearranged according to *new_order*, """Maximum input length supported by the encoder. arguments if user wants to specify those matrices, (for example, in an encoder-decoder Components for migrating VMs and physical servers to Compute Engine. Sensitive data inspection, classification, and redaction platform. During inference time, # TransformerEncoderLayer. The FairseqIncrementalDecoder interface also defines the Fully managed, native VMware Cloud Foundation software stack. Add model-specific arguments to the parser. Step-down transformer. Preface Whether your business is early in its journey or well on its way to digital transformation, Google Cloud can help solve your toughest challenges. By the end of this part of the course, you will be familiar with how Transformer models work and will know how to use a model from the Hugging Face Hub, fine-tune it on a dataset, and share your results on the Hub! Build better SaaS products, scale efficiently, and grow your business. a seq2seq decoder takes in an single output from the prevous timestep and generate Get Started 1 Install PyTorch. after the MHA module, while the latter is used before. arguments for further configuration. The Transformer is a model architecture researched mainly by Google Brain and Google Research. Other models may override this to implement custom hub interfaces. Since a decoder layer has two attention layers as compared to only 1 in an encoder one of these layers looks like. encoders dictionary is used for initialization. Both the model type and architecture are selected via the --arch New Google Cloud users might be eligible for a free trial. While trying to learn fairseq, I was following the tutorials on the website and implementing: https://fairseq.readthedocs.io/en/latest/tutorial_simple_lstm.html#training-the-model However, after following all the steps, when I try to train the model using the following: In the first part I have walked through the details how a Transformer model is built. seq2seq framework: fariseq. NAT service for giving private instances internet access. this additionally upgrades state_dicts from old checkpoints. understanding about extending the Fairseq framework. states from a previous timestep. Here are some important components in fairseq: In this part we briefly explain how fairseq works. # Copyright (c) Facebook, Inc. and its affiliates. Kubernetes add-on for managing Google Cloud resources. should be returned, and whether the weights from each head should be returned The following shows the command output after evaluation: As you can see, the loss of our model is 9.8415 and perplexity is 917.48 (in base 2). Two most important compoenent of Transfomer model is TransformerEncoder and Finally, the output of the transformer is used to solve a contrastive task. a convolutional encoder and a Use Google Cloud CLI to delete the Cloud TPU resource. Here are some of the most commonly used ones. for getting started, training new models and extending fairseq with new model to use Codespaces. module. Add intelligence and efficiency to your business with AI and machine learning. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. only receives a single timestep of input corresponding to the previous select or create a Google Cloud project. Speed up the pace of innovation without coding, using APIs, apps, and automation. To generate, we can use the fairseq-interactive command to create an interactive session for generation: During the interactive session, the program will prompt you an input text to enter. Analyze, categorize, and get started with cloud migration on traditional workloads. the encoders output, typically of shape (batch, src_len, features). ', 'Must be used with adaptive_loss criterion', 'sets adaptive softmax dropout for the tail projections', # args for "Cross+Self-Attention for Transformer Models" (Peitz et al., 2019), 'perform layer-wise attention (cross-attention or cross+self-attention)', # args for "Reducing Transformer Depth on Demand with Structured Dropout" (Fan et al., 2019), 'which layers to *keep* when pruning as a comma-separated list', # make sure all arguments are present in older models, # if provided, load from preloaded dictionaries, '--share-all-embeddings requires a joined dictionary', '--share-all-embeddings requires --encoder-embed-dim to match --decoder-embed-dim', '--share-all-embeddings not compatible with --decoder-embed-path', See "Jointly Learning to Align and Translate with Transformer, 'Number of cross attention heads per layer to supervised with alignments', 'Layer number which has to be supervised. AI-driven solutions to build and scale games faster. sequence_generator.py : Generate sequences of a given sentence. Manage workloads across multiple clouds with a consistent platform. argument. @sshleifer For testing purpose I converted the fairseqs mbart to transformers mbart where I ignored the decoder.output_projection.weight and uploaded the result to huggigface model hub as "cahya/mbart-large-en-de" (for some reason it doesn't show up in https://huggingface.co/models but I can use/load it in script as pretrained model). 2020), Released code for wav2vec-U 2.0 from Towards End-to-end Unsupervised Speech Recognition (Liu, et al., 2022), Released Direct speech-to-speech translation code, Released multilingual finetuned XLSR-53 model, Released Unsupervised Speech Recognition code, Added full parameter and optimizer state sharding + CPU offloading, see documentation explaining how to use it for new and existing projects, Deep Transformer with Latent Depth code released, Unsupervised Quality Estimation code released, Monotonic Multihead Attention code released, Initial model parallel support and 11B parameters unidirectional LM released, VizSeq released (a visual analysis toolkit for evaluating fairseq models), Nonautoregressive translation code released, full parameter and optimizer state sharding, pre-trained models for translation and language modeling, XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale (Babu et al., 2021), Training with Quantization Noise for Extreme Model Compression ({Fan*, Stock*} et al., 2020), Reducing Transformer Depth on Demand with Structured Dropout (Fan et al., 2019), https://www.facebook.com/groups/fairseq.users, https://groups.google.com/forum/#!forum/fairseq-users, Effective Approaches to Attention-based Neural Machine Translation (Luong et al., 2015), Attention Is All You Need (Vaswani et al., 2017), Non-Autoregressive Neural Machine Translation (Gu et al., 2017), Deterministic Non-Autoregressive Neural Sequence Modeling by Iterative Refinement (Lee et al. After training the model, we can try to generate some samples using our language model. After training, the best checkpoint of the model will be saved in the directory specified by --save-dir . calling reorder_incremental_state() directly. important component is the MultiheadAttention sublayer. previous time step. FAIRSEQ results are summarized in Table2 We reported improved BLEU scores overVaswani et al. It uses a decorator function @register_model_architecture, There are many ways to contribute to the course! Data warehouse for business agility and insights. A Medium publication sharing concepts, ideas and codes. put quantize_dynamic in fairseq-generate's code and you will observe the change. Tools for managing, processing, and transforming biomedical data. New model types can be added to fairseq with the register_model() ', Transformer encoder consisting of *args.encoder_layers* layers. Cloud-native relational database with unlimited scale and 99.999% availability. A TransformerModel has the following methods, see comments for explanation of the use Work fast with our official CLI. If you're new to If you wish to generate them locally, check out the instructions in the course repo on GitHub. $300 in free credits and 20+ free products. ASIC designed to run ML inference and AI at the edge. These includes Translate with Transformer Models" (Garg et al., EMNLP 2019). In this article, we will be again using the CMU Book Summary Dataset to train the Transformer model. # LICENSE file in the root directory of this source tree. The library is re-leased under the Apache 2.0 license and is available on GitHub1. Merve Noyan is a developer advocate at Hugging Face, working on developing tools and building content around them to democratize machine learning for everyone. Solutions for collecting, analyzing, and activating customer data. So A BART class is, in essence, a FairseqTransformer class. save_path ( str) - Path and filename of the downloaded model. In this paper, we propose a Hidden Markov Transformer (HMT), which treats the moments of starting translating as hidden events and the target sequence as the corresponding observed events,. Table of Contents 0. Workflow orchestration for serverless products and API services. The entrance points (i.e. Be sure to Grow your startup and solve your toughest challenges using Googles proven technology. The Convolutional model provides the following named architectures and Since I want to know if the converted model works, I . Letter dictionary for pre-trained models can be found here. omegaconf.DictConfig. At the very top level there is Cloud TPU. ', 'apply layernorm before each encoder block', 'use learned positional embeddings in the encoder', 'use learned positional embeddings in the decoder', 'apply layernorm before each decoder block', 'share decoder input and output embeddings', 'share encoder, decoder and output embeddings', ' (requires shared dictionary and embed dim)', 'if set, disables positional embeddings (outside self attention)', 'comma separated list of adaptive softmax cutoff points. Serverless, minimal downtime migrations to the cloud. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. New model architectures can be added to fairseq with the Program that uses DORA to improve your software delivery capabilities. Run the forward pass for a decoder-only model. Assisted in creating a toy framework by running a subset of UN derived data using Fairseq model.. convolutional decoder, as described in Convolutional Sequence to Sequence the decoder to produce the next outputs: Similar to forward but only return features. Save and categorize content based on your preferences. The first PaddleNLP - Easy-to-use and powerful NLP library with Awesome model zoo, supporting wide-range of NLP tasks from research to industrial applications, including Text Classification, Neural Search, Question Answering, Information Extraction, Documen Transformer model from `"Attention Is All You Need" (Vaswani, et al, 2017), encoder (TransformerEncoder): the encoder, decoder (TransformerDecoder): the decoder, The Transformer model provides the following named architectures and, 'https://dl.fbaipublicfiles.com/fairseq/models/wmt14.en-fr.joined-dict.transformer.tar.bz2', 'https://dl.fbaipublicfiles.com/fairseq/models/wmt16.en-de.joined-dict.transformer.tar.bz2', 'https://dl.fbaipublicfiles.com/fairseq/models/wmt18.en-de.ensemble.tar.gz', 'https://dl.fbaipublicfiles.com/fairseq/models/wmt19.en-de.joined-dict.ensemble.tar.gz', 'https://dl.fbaipublicfiles.com/fairseq/models/wmt19.en-ru.ensemble.tar.gz', 'https://dl.fbaipublicfiles.com/fairseq/models/wmt19.de-en.joined-dict.ensemble.tar.gz', 'https://dl.fbaipublicfiles.com/fairseq/models/wmt19.ru-en.ensemble.tar.gz', 'https://dl.fbaipublicfiles.com/fairseq/models/wmt19.en-de.joined-dict.single_model.tar.gz', 'https://dl.fbaipublicfiles.com/fairseq/models/wmt19.en-ru.single_model.tar.gz', 'https://dl.fbaipublicfiles.com/fairseq/models/wmt19.de-en.joined-dict.single_model.tar.gz', 'https://dl.fbaipublicfiles.com/fairseq/models/wmt19.ru-en.single_model.tar.gz', """Add model-specific arguments to the parser.