Fasttext Quick Start Guide

Fasttext Quick Start Guide Book in PDF, ePub and Kindle version is available to download in english. Read online anytime anywhere directly from your device. Click on the download button below to get a free pdf file of Fasttext Quick Start Guide book. This book definitely worth reading, it is an incredibly well-written.

fastText Quick Start Guide

Joydeep Bhattacharjee
fastText Quick Start Guide Book PDF

Author : Joydeep Bhattacharjee
Publisher : Packt Publishing Ltd
Page : 183 pages
File Size : 51,7 Mb
Release : 2018-07-26
Category : Computers
ISBN : 9781789136715

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fastText Quick Start Guide by Joydeep Bhattacharjee Pdf

Perform efficient fast text representation and classification with Facebook's fastText library Key Features Introduction to Facebook's fastText library for NLP Perform efficient word representations, sentence classification, vector representation Build better, more scalable solutions for text representation and classification Book Description Facebook's fastText library handles text representation and classification, used for Natural Language Processing (NLP). Most organizations have to deal with enormous amounts of text data on a daily basis, and gaining efficient data insights requires powerful NLP tools such as fastText. This book is your ideal introduction to fastText. You will learn how to create fastText models from the command line, without the need for complicated code. You will explore the algorithms that fastText is built on and how to use them for word representation and text classification. Next, you will use fastText in conjunction with other popular libraries and frameworks such as Keras, TensorFlow, and PyTorch. Finally, you will deploy fastText models to mobile devices. By the end of this book, you will have all the required knowledge to use fastText in your own applications at work or in projects. What you will learn Create models using the default command line options in fastText Understand the algorithms used in fastText to create word vectors Combine command line text transformation capabilities and the fastText library to implement a training, validation, and prediction pipeline Explore word representation and sentence classification using fastText Use Gensim and spaCy to load the vectors, transform, lemmatize, and perform other NLP tasks efficiently Develop a fastText NLP classifier using popular frameworks, such as Keras, Tensorflow, and PyTorch Who this book is for This book is for data analysts, data scientists, and machine learning developers who want to perform efficient word representation and sentence classification using Facebook's fastText library. Basic knowledge of Python programming is required.

Natural Language Processing with Python Quick Start Guide

Nirant Kasliwal
Natural Language Processing with Python Quick Start Guide Book PDF

Author : Nirant Kasliwal
Publisher : Packt Publishing Ltd
Page : 177 pages
File Size : 47,9 Mb
Release : 2018-11-30
Category : Computers
ISBN : 9781788994101

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Natural Language Processing with Python Quick Start Guide by Nirant Kasliwal Pdf

Build and deploy intelligent applications for natural language processing with Python by using industry standard tools and recently popular methods in deep learning Key FeaturesA no-math, code-driven programmer’s guide to text processing and NLPGet state of the art results with modern tooling across linguistics, text vectors and machine learningFundamentals of NLP methods from spaCy, gensim, scikit-learn and PyTorchBook Description NLP in Python is among the most sought after skills among data scientists. With code and relevant case studies, this book will show how you can use industry-grade tools to implement NLP programs capable of learning from relevant data. We will explore many modern methods ranging from spaCy to word vectors that have reinvented NLP. The book takes you from the basics of NLP to building text processing applications. We start with an introduction to the basic vocabulary along with a workflow for building NLP applications. We use industry-grade NLP tools for cleaning and pre-processing text, automatic question and answer generation using linguistics, text embedding, text classifier, and building a chatbot. With each project, you will learn a new concept of NLP. You will learn about entity recognition, part of speech tagging and dependency parsing for Q and A. We use text embedding for both clustering documents and making chatbots, and then build classifiers using scikit-learn. We conclude by deploying these models as REST APIs with Flask. By the end, you will be confident building NLP applications, and know exactly what to look for when approaching new challenges. What you will learnUnderstand classical linguistics in using English grammar for automatically generating questions and answers from a free text corpusWork with text embedding models for dense number representations of words, subwords and characters in the English language for exploring document clusteringDeep Learning in NLP using PyTorch with a code-driven introduction to PyTorchUsing an NLP project management Framework for estimating timelines and organizing your project into stagesHack and build a simple chatbot application in 30 minutesDeploy an NLP or machine learning application using Flask as RESTFUL APIsWho this book is for Programmers who wish to build systems that can interpret language. Exposure to Python programming is required. Familiarity with NLP or machine learning vocabulary will be helpful, but not mandatory.

Practical Machine Learning with Rust

Joydeep Bhattacharjee
Practical Machine Learning with Rust Book PDF

Author : Joydeep Bhattacharjee
Publisher : Apress
Page : 362 pages
File Size : 53,6 Mb
Release : 2019-12-10
Category : Computers
ISBN : 9781484251218

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Practical Machine Learning with Rust by Joydeep Bhattacharjee Pdf

Explore machine learning in Rust and learn about the intricacies of creating machine learning applications. This book begins by covering the important concepts of machine learning such as supervised, unsupervised, and reinforcement learning, and the basics of Rust. Further, you’ll dive into the more specific fields of machine learning, such as computer vision and natural language processing, and look at the Rust libraries that help create applications for those domains. We will also look at how to deploy these applications either on site or over the cloud. After reading Practical Machine Learning with Rust, you will have a solid understanding of creating high computation libraries using Rust. Armed with the knowledge of this amazing language, you will be able to create applications that are more performant, memory safe, and less resource heavy. What You Will Learn Write machine learning algorithms in RustUse Rust libraries for different tasks in machine learningCreate concise Rust packages for your machine learning applicationsImplement NLP and computer vision in RustDeploy your code in the cloud and on bare metal servers Who This Book Is For Machine learning engineers and software engineers interested in building machine learning applications in Rust.

Handbook of Research on Engineering Innovations and Technology Management in Organizations

Gaur, Loveleen,Solanki, Arun,Jain, Vishal,Khazanchi, Deepak
Handbook of Research on Engineering Innovations and Technology Management in Organizations Book PDF

Author : Gaur, Loveleen,Solanki, Arun,Jain, Vishal,Khazanchi, Deepak
Publisher : IGI Global
Page : 459 pages
File Size : 44,5 Mb
Release : 2020-04-17
Category : Technology & Engineering
ISBN : 9781799827733

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Handbook of Research on Engineering Innovations and Technology Management in Organizations by Gaur, Loveleen,Solanki, Arun,Jain, Vishal,Khazanchi, Deepak Pdf

As technology weaves itself more tightly into everyday life, socio-economic development has become intricately tied to these ever-evolving innovations. Technology management is now an integral element of sound business practices, and this revolution has opened up many opportunities for global communication. However, such swift change warrants greater research that can foresee and possibly prevent future complications within and between organizations. The Handbook of Research on Engineering Innovations and Technology Management in Organizations is a collection of innovative research that explores global concerns in the applications of technology to business and the explosive growth that resulted. Highlighting a wide range of topics such as cyber security, legal practice, and artificial intelligence, this book is ideally designed for engineers, manufacturers, technology managers, technology developers, IT specialists, productivity consultants, executives, lawyers, programmers, managers, policymakers, academicians, researchers, and students.

4th International Conference on Artificial Intelligence and Applied Mathematics in Engineering

D. Jude Hemanth,Tuncay Yigit,Utku Kose,Ugur Guvenc
4th International Conference on Artificial Intelligence and Applied Mathematics in Engineering Book PDF

Author : D. Jude Hemanth,Tuncay Yigit,Utku Kose,Ugur Guvenc
Publisher : Springer Nature
Page : 779 pages
File Size : 50,9 Mb
Release : 2023-07-02
Category : Technology & Engineering
ISBN : 9783031319563

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4th International Conference on Artificial Intelligence and Applied Mathematics in Engineering by D. Jude Hemanth,Tuncay Yigit,Utku Kose,Ugur Guvenc Pdf

As general, this book is a collection of the most recent, quality research papers regarding applications of Artificial Intelligence and Applied Mathematics for engineering problems. The papers included in the book were accepted and presented in the 4th International Conference on Artificial Intelligence and Applied Mathematics in Engineering (ICAIAME 2022), which was held in Baku, Azerbaijan (Azerbaijan Technical University) between May 20 and 22, 2022. Objective of the book content is to inform the international audience about the cutting-edge, effective developments and improvements in different engineering fields. As a collection of the ICAIAME 2022 event, the book gives consideration for the results by especially intelligent system formations and the associated applications. The target audience of the book is international researchers, degree students, practitioners from industry, and experts from different engineering disciplines.

Practical Natural Language Processing

Sowmya Vajjala,Bodhisattwa Majumder,Anuj Gupta,Harshit Surana
Practical Natural Language Processing Book PDF

Author : Sowmya Vajjala,Bodhisattwa Majumder,Anuj Gupta,Harshit Surana
Publisher : O'Reilly Media
Page : 455 pages
File Size : 53,5 Mb
Release : 2020-06-17
Category : Computers
ISBN : 9781492054023

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Practical Natural Language Processing by Sowmya Vajjala,Bodhisattwa Majumder,Anuj Gupta,Harshit Surana Pdf

Many books and courses tackle natural language processing (NLP) problems with toy use cases and well-defined datasets. But if you want to build, iterate, and scale NLP systems in a business setting and tailor them for particular industry verticals, this is your guide. Software engineers and data scientists will learn how to navigate the maze of options available at each step of the journey. Through the course of the book, authors Sowmya Vajjala, Bodhisattwa Majumder, Anuj Gupta, and Harshit Surana will guide you through the process of building real-world NLP solutions embedded in larger product setups. You’ll learn how to adapt your solutions for different industry verticals such as healthcare, social media, and retail. With this book, you’ll: Understand the wide spectrum of problem statements, tasks, and solution approaches within NLP Implement and evaluate different NLP applications using machine learning and deep learning methods Fine-tune your NLP solution based on your business problem and industry vertical Evaluate various algorithms and approaches for NLP product tasks, datasets, and stages Produce software solutions following best practices around release, deployment, and DevOps for NLP systems Understand best practices, opportunities, and the roadmap for NLP from a business and product leader’s perspective

Machine Learning with scikit-learn Quick Start Guide

Kevin Jolly
Machine Learning with scikit learn Quick Start Guide Book PDF

Author : Kevin Jolly
Publisher : Packt Publishing Ltd
Page : 164 pages
File Size : 48,5 Mb
Release : 2018-10-30
Category : Mathematics
ISBN : 9781789347371

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Machine Learning with scikit-learn Quick Start Guide by Kevin Jolly Pdf

Deploy supervised and unsupervised machine learning algorithms using scikit-learn to perform classification, regression, and clustering. Key FeaturesBuild your first machine learning model using scikit-learnTrain supervised and unsupervised models using popular techniques such as classification, regression and clusteringUnderstand how scikit-learn can be applied to different types of machine learning problemsBook Description Scikit-learn is a robust machine learning library for the Python programming language. It provides a set of supervised and unsupervised learning algorithms. This book is the easiest way to learn how to deploy, optimize, and evaluate all of the important machine learning algorithms that scikit-learn provides. This book teaches you how to use scikit-learn for machine learning. You will start by setting up and configuring your machine learning environment with scikit-learn. To put scikit-learn to use, you will learn how to implement various supervised and unsupervised machine learning models. You will learn classification, regression, and clustering techniques to work with different types of datasets and train your models. Finally, you will learn about an effective pipeline to help you build a machine learning project from scratch. By the end of this book, you will be confident in building your own machine learning models for accurate predictions. What you will learnLearn how to work with all scikit-learn's machine learning algorithmsInstall and set up scikit-learn to build your first machine learning modelEmploy Unsupervised Machine Learning Algorithms to cluster unlabelled data into groupsPerform classification and regression machine learningUse an effective pipeline to build a machine learning project from scratchWho this book is for This book is for aspiring machine learning developers who want to get started with scikit-learn. Intermediate knowledge of Python programming and some fundamental knowledge of linear algebra and probability will help.

Text Analytics with Python

Dipanjan Sarkar
Text Analytics with Python Book PDF

Author : Dipanjan Sarkar
Publisher : Apress
Page : 688 pages
File Size : 50,6 Mb
Release : 2019-05-21
Category : Computers
ISBN : 9781484243541

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Text Analytics with Python by Dipanjan Sarkar Pdf

Leverage Natural Language Processing (NLP) in Python and learn how to set up your own robust environment for performing text analytics. This second edition has gone through a major revamp and introduces several significant changes and new topics based on the recent trends in NLP. You’ll see how to use the latest state-of-the-art frameworks in NLP, coupled with machine learning and deep learning models for supervised sentiment analysis powered by Python to solve actual case studies. Start by reviewing Python for NLP fundamentals on strings and text data and move on to engineering representation methods for text data, including both traditional statistical models and newer deep learning-based embedding models. Improved techniques and new methods around parsing and processing text are discussed as well. Text summarization and topic models have been overhauled so the book showcases how to build, tune, and interpret topic models in the context of an interest dataset on NIPS conference papers. Additionally, the book covers text similarity techniques with a real-world example of movie recommenders, along with sentiment analysis using supervised and unsupervised techniques. There is also a chapter dedicated to semantic analysis where you’ll see how to build your own named entity recognition (NER) system from scratch. While the overall structure of the book remains the same, the entire code base, modules, and chapters has been updated to the latest Python 3.x release. What You'll Learn • Understand NLP and text syntax, semantics and structure• Discover text cleaning and feature engineering• Review text classification and text clustering • Assess text summarization and topic models• Study deep learning for NLP Who This Book Is For IT professionals, data analysts, developers, linguistic experts, data scientists and engineers and basically anyone with a keen interest in linguistics, analytics and generating insights from textual data.

Transfer Learning for Natural Language Processing

Paul Azunre
Transfer Learning for Natural Language Processing Book PDF

Author : Paul Azunre
Publisher : Simon and Schuster
Page : 262 pages
File Size : 49,5 Mb
Release : 2021-08-31
Category : Computers
ISBN : 9781638350996

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Transfer Learning for Natural Language Processing by Paul Azunre Pdf

Build custom NLP models in record time by adapting pre-trained machine learning models to solve specialized problems. Summary In Transfer Learning for Natural Language Processing you will learn: Fine tuning pretrained models with new domain data Picking the right model to reduce resource usage Transfer learning for neural network architectures Generating text with generative pretrained transformers Cross-lingual transfer learning with BERT Foundations for exploring NLP academic literature Training deep learning NLP models from scratch is costly, time-consuming, and requires massive amounts of data. In Transfer Learning for Natural Language Processing, DARPA researcher Paul Azunre reveals cutting-edge transfer learning techniques that apply customizable pretrained models to your own NLP architectures. You’ll learn how to use transfer learning to deliver state-of-the-art results for language comprehension, even when working with limited label data. Best of all, you’ll save on training time and computational costs. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Build custom NLP models in record time, even with limited datasets! Transfer learning is a machine learning technique for adapting pretrained machine learning models to solve specialized problems. This powerful approach has revolutionized natural language processing, driving improvements in machine translation, business analytics, and natural language generation. About the book Transfer Learning for Natural Language Processing teaches you to create powerful NLP solutions quickly by building on existing pretrained models. This instantly useful book provides crystal-clear explanations of the concepts you need to grok transfer learning along with hands-on examples so you can practice your new skills immediately. As you go, you’ll apply state-of-the-art transfer learning methods to create a spam email classifier, a fact checker, and more real-world applications. What's inside Fine tuning pretrained models with new domain data Picking the right model to reduce resource use Transfer learning for neural network architectures Generating text with pretrained transformers About the reader For machine learning engineers and data scientists with some experience in NLP. About the author Paul Azunre holds a PhD in Computer Science from MIT and has served as a Principal Investigator on several DARPA research programs. Table of Contents PART 1 INTRODUCTION AND OVERVIEW 1 What is transfer learning? 2 Getting started with baselines: Data preprocessing 3 Getting started with baselines: Benchmarking and optimization PART 2 SHALLOW TRANSFER LEARNING AND DEEP TRANSFER LEARNING WITH RECURRENT NEURAL NETWORKS (RNNS) 4 Shallow transfer learning for NLP 5 Preprocessing data for recurrent neural network deep transfer learning experiments 6 Deep transfer learning for NLP with recurrent neural networks PART 3 DEEP TRANSFER LEARNING WITH TRANSFORMERS AND ADAPTATION STRATEGIES 7 Deep transfer learning for NLP with the transformer and GPT 8 Deep transfer learning for NLP with BERT and multilingual BERT 9 ULMFiT and knowledge distillation adaptation strategies 10 ALBERT, adapters, and multitask adaptation strategies 11 Conclusions

Mastering Transformers

Savaş Yıldırım,Meysam Asgari- Chenaghlu
Mastering Transformers Book PDF

Author : Savaş Yıldırım,Meysam Asgari- Chenaghlu
Publisher : Packt Publishing Ltd
Page : 374 pages
File Size : 48,7 Mb
Release : 2021-09-15
Category : Computers
ISBN : 9781801078894

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Mastering Transformers by Savaş Yıldırım,Meysam Asgari- Chenaghlu Pdf

Take a problem-solving approach to learning all about transformers and get up and running in no time by implementing methodologies that will build the future of NLP Key Features Explore quick prototyping with up-to-date Python libraries to create effective solutions to industrial problems Solve advanced NLP problems such as named-entity recognition, information extraction, language generation, and conversational AI Monitor your model's performance with the help of BertViz, exBERT, and TensorBoard Book DescriptionTransformer-based language models have dominated natural language processing (NLP) studies and have now become a new paradigm. With this book, you'll learn how to build various transformer-based NLP applications using the Python Transformers library. The book gives you an introduction to Transformers by showing you how to write your first hello-world program. You'll then learn how a tokenizer works and how to train your own tokenizer. As you advance, you'll explore the architecture of autoencoding models, such as BERT, and autoregressive models, such as GPT. You'll see how to train and fine-tune models for a variety of natural language understanding (NLU) and natural language generation (NLG) problems, including text classification, token classification, and text representation. This book also helps you to learn efficient models for challenging problems, such as long-context NLP tasks with limited computational capacity. You'll also work with multilingual and cross-lingual problems, optimize models by monitoring their performance, and discover how to deconstruct these models for interpretability and explainability. Finally, you'll be able to deploy your transformer models in a production environment. By the end of this NLP book, you'll have learned how to use Transformers to solve advanced NLP problems using advanced models.What you will learn Explore state-of-the-art NLP solutions with the Transformers library Train a language model in any language with any transformer architecture Fine-tune a pre-trained language model to perform several downstream tasks Select the right framework for the training, evaluation, and production of an end-to-end solution Get hands-on experience in using TensorBoard and Weights & Biases Visualize the internal representation of transformer models for interpretability Who this book is for This book is for deep learning researchers, hands-on NLP practitioners, as well as ML/NLP educators and students who want to start their journey with Transformers. Beginner-level machine learning knowledge and a good command of Python will help you get the best out of this book.

Deep Learning for Natural Language Processing

Palash Goyal,Sumit Pandey,Karan Jain
Deep Learning for Natural Language Processing Book PDF

Author : Palash Goyal,Sumit Pandey,Karan Jain
Publisher : Apress
Page : 290 pages
File Size : 52,7 Mb
Release : 2018-06-26
Category : Computers
ISBN : 9781484236857

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Deep Learning for Natural Language Processing by Palash Goyal,Sumit Pandey,Karan Jain Pdf

Discover the concepts of deep learning used for natural language processing (NLP), with full-fledged examples of neural network models such as recurrent neural networks, long short-term memory networks, and sequence-2-sequence models. You’ll start by covering the mathematical prerequisites and the fundamentals of deep learning and NLP with practical examples. The first three chapters of the book cover the basics of NLP, starting with word-vector representation before moving onto advanced algorithms. The final chapters focus entirely on implementation, and deal with sophisticated architectures such as RNN, LSTM, and Seq2seq, using Python tools: TensorFlow, and Keras. Deep Learning for Natural Language Processing follows a progressive approach and combines all the knowledge you have gained to build a question-answer chatbot system. This book is a good starting point for people who want to get started in deep learning for NLP. All the code presented in the book will be available in the form of IPython notebooks and scripts, which allow you to try out the examples and extend them in interesting ways. What You Will Learn Gain the fundamentals of deep learning and its mathematical prerequisites Discover deep learning frameworks in Python Develop a chatbot Implement a research paper on sentiment classification Who This Book Is For Software developers who are curious to try out deep learning with NLP.

Teeline Fast

Ann Dix
Teeline Fast Book PDF

Author : Ann Dix
Publisher : Heinemann
Page : 84 pages
File Size : 41,9 Mb
Release : 1990
Category : Shorthand
ISBN : 0435453521

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Teeline Fast by Ann Dix Pdf

This course book is designed for short courses of all kinds either in the classroom or for self-study. It contains sufficient theory to lay the foundation for higher speeds for students who chose to progress.

The REGTECH Book

Janos Barberis,Douglas W. Arner,Ross P. Buckley
The REGTECH Book Book PDF

Author : Janos Barberis,Douglas W. Arner,Ross P. Buckley
Publisher : John Wiley & Sons
Page : 1004 pages
File Size : 47,8 Mb
Release : 2019-08-06
Category : Business & Economics
ISBN : 9781119362173

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The REGTECH Book by Janos Barberis,Douglas W. Arner,Ross P. Buckley Pdf

The Regulatory Technology Handbook The transformational potential of RegTech has been confirmed in recent years with US$1.2 billion invested in start-ups (2017) and an expected additional spending of US$100 billion by 2020. Regulatory technology will not only provide efficiency gains for compliance and reporting functions, it will radically change market structure and supervision. This book, the first of its kind, is providing a comprehensive and invaluable source of information aimed at corporates, regulators, compliance professionals, start-ups and policy makers. The REGTECH Book brings into a single volume the curated industry expertise delivered by subject matter experts. It serves as a single reference point to understand the RegTech eco-system and its impact on the industry. Readers will learn foundational notions such as: • The economic impact of digitization and datafication of regulation • How new technologies (Artificial Intelligence, Blockchain) are applied to compliance • Business use cases of RegTech for cost-reduction and new product origination • The future regulatory landscape affecting financial institutions, technology companies and other industries Edited by world-class academics and written by compliance professionals, regulators, entrepreneurs and business leaders, the RegTech Book represents an invaluable resource that paves the way for 21st century regulatory innovation.

Deep Learning in Natural Language Processing

Li Deng,Yang Liu
Deep Learning in Natural Language Processing Book PDF

Author : Li Deng,Yang Liu
Publisher : Springer
Page : 329 pages
File Size : 53,7 Mb
Release : 2018-05-23
Category : Computers
ISBN : 9789811052095

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Deep Learning in Natural Language Processing by Li Deng,Yang Liu Pdf

In recent years, deep learning has fundamentally changed the landscapes of a number of areas in artificial intelligence, including speech, vision, natural language, robotics, and game playing. In particular, the striking success of deep learning in a wide variety of natural language processing (NLP) applications has served as a benchmark for the advances in one of the most important tasks in artificial intelligence. This book reviews the state of the art of deep learning research and its successful applications to major NLP tasks, including speech recognition and understanding, dialogue systems, lexical analysis, parsing, knowledge graphs, machine translation, question answering, sentiment analysis, social computing, and natural language generation from images. Outlining and analyzing various research frontiers of NLP in the deep learning era, it features self-contained, comprehensive chapters written by leading researchers in the field. A glossary of technical terms and commonly used acronyms in the intersection of deep learning and NLP is also provided. The book appeals to advanced undergraduate and graduate students, post-doctoral researchers, lecturers and industrial researchers, as well as anyone interested in deep learning and natural language processing.

Natural Language Processing with Spark NLP

Alex Thomas
Natural Language Processing with Spark NLP Book PDF

Author : Alex Thomas
Publisher : "O'Reilly Media, Inc."
Page : 411 pages
File Size : 50,8 Mb
Release : 2020-06-25
Category : Computers
ISBN : 9781492047711

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Natural Language Processing with Spark NLP by Alex Thomas Pdf

If you want to build an enterprise-quality application that uses natural language text but aren’t sure where to begin or what tools to use, this practical guide will help get you started. Alex Thomas, principal data scientist at Wisecube, shows software engineers and data scientists how to build scalable natural language processing (NLP) applications using deep learning and the Apache Spark NLP library. Through concrete examples, practical and theoretical explanations, and hands-on exercises for using NLP on the Spark processing framework, this book teaches you everything from basic linguistics and writing systems to sentiment analysis and search engines. You’ll also explore special concerns for developing text-based applications, such as performance. In four sections, you’ll learn NLP basics and building blocks before diving into application and system building: Basics: Understand the fundamentals of natural language processing, NLP on Apache Stark, and deep learning Building blocks: Learn techniques for building NLP applications—including tokenization, sentence segmentation, and named-entity recognition—and discover how and why they work Applications: Explore the design, development, and experimentation process for building your own NLP applications Building NLP systems: Consider options for productionizing and deploying NLP models, including which human languages to support