Artificial Intelligence (AI) is the new center of attraction in technology. The field is gaining ground, followed by tremendous research. To deploy AI more conveniently, the introduction of new modes, methods, and libraries is becoming standard. Machine Learning (ML) is one of the trending topics in AI. Although we have hundreds of famous libraries and frameworks for AI and ML, there is always a chance of improvement. Apart from other popular frameworks such as TensorFlow, Theano, and PyTorch, Fairseq has also emerged as an optimal machine learning solution. It is gaining popularity and is used by over 1.7k developers worldwide.
What is Fairseq?
Fairseq PyTorch is an opensource machine learning library based on a sequence modeling toolkit. It allows the researchers to train custom models for fairseq summarization transformer, language, translation, and other generation tasks. It supports distributed training across multiple GPUs and machines. GitHub hosts its repository.
Fairseq Features in 2020
Fairseq provides researchers with smooth implementation of sequence to sequence models. It supports various models. Some of them include:
Convolutional Neural Networks (CNN)
Convolutional Neural Networks are a form of deep neural networks commonly used for visual imagery. They are useful in areas such as object detection, image recognition and other computer vision stuff. Fairseq is handy with the following:
1) Fairseq Language Modelling with Gated CNN
2) Classical Structured Prediction Losses
3) Hierarchical Neural Story Generation
4) Unsupervised Learning for Speech Recognition using predictive analytics solution
LightConv and DynamicConv Models
This model contains some pre-trained dataset and as well as the instructions on training the new model. It includes models without graphics library hence making it faster. You can quickly get the fairseq-preprocess datasets for languages such as English, Chinese, German, and French with fairseq-train paper.
Long Short-Term Memory (LSTM) Networks
LSTM is an artificial recurrent neural network (RNN) that are well-suited for classification and making predictions on time series data. It is convenient to use for unsegmented handwriting recognition, speech recognition, and anomaly detection in network traffic. Fairseq provides a practical approach to solve Attention-based Neural Machine Translation.
Transformer (self-attention) Networks
In place of CNN and RNN, many researchers prefer to use transformer networks. They implement encoder and decoder as self–attention networks to draw global dependencies between input and output. It works well in:
1) Scaling Neural Machine Fairseq Translation
2) Understanding Back-Translation
3) Mixture Models for Diverse Machine Translation
4) Input Representations for Neural Language Modeling
Non-autoregressive Transformers or NAT removes the dependencies from the inputs of the decoder on the previous target token with fairseq bart. It helps to achieve:
1) Non-autoregressive Neural Machine Translation
2) Neural Sequence Modeling Iterative Refinement
3) Flexible Sequence Generation by Fairseq Insertion Transformer Model
4) Mask-Predict: Conditional Masked Language Models Parallel Decoding.
Apart from all these supported models and techniques by Fairseq, it also has other advantages. You can do multi-GPU training either on one machine or multiple machines. One can quickly implement them on both CPU and GPU with search algorithms. With its mixed-precision training, you can train models while consuming lesser GPU memory. It is extensible and makes registering of new models, tasks, and optimizers convenient.
The GitHub repository of Fairseq is at this link. It has 1128 commits with eight branches and 11 releases. Over six thousand people have starred it while 1.7k forked it. It has about 132 contributors with an active community backing it up.
How to Use FairSeq – Installation Requirements and Prerequisite
1) As Fairseq is an ML library in python, so you need python with version 3.6 or onwards.
2) PyTorch is also necessary before proceeding with Fairseq. You will require version 1.2.0 or onwards.
4) Install NVIDIA’s apex library for faster training with the following two commands.
5) After fulfilling all the requirements, install Fairseq. You can either clone it by ‘git clone https://github.com/pytorch/fairseq’ or use the command ‘pip install fairseq.’
After successfully installing the fairseq, you can view its documentation here to get started. You even get pre-trained models and datasets with which you can get familiarization with the new library. Each pre-trained model has its READMEs as well for your convenience.
How to Install Fairseq – Interactive Installation Guide
There are a few simple steps to get started with fairseq. Follow the sequence:
1) First, you need python installed on your machine. Make sure its version is either 3.6 or higher. You can get python for your computer here.
2) After getting python, you need PyTorch. The underlying technology behind fairseq is PyTorch. You need version 1.2.0 or higher. To get PyTorch, you can clone it by the command ‘git clone https://github.com/pytorch/pytorch.git.’ You can install it from Anaconda or Chocolatey based installed. Here is the documentation.
3) Get fairseq by typing the following commands on the terminal.
git clone https://github.com/pytorch/fairseq.git
pip install -r requirements.txt
python setup.py build develop
Download pre-trained models and get acquainted with the syntax.
Start working on new projects and models.
Fairseq Machine Translation Youtube
This video takes you through the fairseq documentation tutorial and demo. If you are a newbie with fairseq, this might help you out.
1) Why is the dictionary required in fairseq?
Dictionaries are the base of machine learning. One important aspect is that you train data using a separate function and then return the results. These results can be effectively stored in dictionaries and can be retrieved efficiently. You can save multiple values in a single dictionary with unique key-value pairs.
2) How to get a specific module out of fairseq?
There are several modules defined in fairseq. All of them have the same naming convention that starts with ‘fairseq.modules.’ To get a specific module, you need to retrieve its name and place it at the end of fairseq.modules. For example,
- fairseq.modules.AdaptiveInput (AdaptiveInput is the module name)
- fairseq.modules.AdaptiveSoftmax (AdaptiveSoftmax is the module name)
fairseq.modules.BeamableMM (BeamableMM is the module name)
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