group the similar words array(['Ruby on Rails', 'Ruby', 'AWS DynamoDB', 'Python', 'MySQL',
         'Swift', 'Android', 'iOS', 'JavaScript', 'React Native', 'ReactJS',
         'TypeScript', 'Vue.js', 'Webpack', 'Amazon Web Services(AWS)',
         'Kubernetes', 'PHP', 'CI/CD', 'Java', 'C#', 'C', 'Node.js',
         'REST API', 'Go', 'Redux-Saga', 'Redux.js', 'Babel', 'GraphQL',
         'Tensorflow', 'PyTorch', 'Jenkins', 'Spring', 'Django', 'Git',
         'AWS EC2', 'CSS', 'HTML', 'MongoDB', 'Docker', 'Scala', 'SQL',
         'Embedded System', 'NLP', 'Apache', 'Kotlin', 'Angular', 'jQuery',
         'C++', 'RxJS', 'AngularJS', 'Redis', 'Next.js', 'NoSQL',
         'GCP(Google Cloud Platform)', 'Elasticsearch', 'OpenStack',
         'JPA(Java Persistent API)', 'TCP/IP', 'Objective-C', 'Realm',
         'Firebase', 'Ajax', 'Linux', 'PostgreSQL', 'ES6', 'AWS Lambda',
         'HTML5', 'AWS S3', 'GitHub', 'RxSwift', 'Terraform', 'AWS EKS',
         'AWS RDS', 'Microsoft Azure', 'Sass(SCSS)', 'CodeIgniter', 'Flask',
         'Nuxt.js', 'Ansible', 'Spring Boot', 'Linux kernel',
         'Apache Kafka', 'Deep Learning', 'Nginx', 'ActionScript', 'OOP',
         'Shell', 'gulp', 'Celery', 'SQLAlchemy', 'ExpressJS', 'RxJava',
         'Apache Spark', 'WebGL', 'OpenGL', 'Machine Learning',
         'MSSQL(Microsoft SQL Server)', 'Database', 'styled-components',
         'MVC', 'Retrofit', 'Machine Vision', 'Oracle', 'web3.js', 'R',
         'AWS ElasticBeanstalk', 'Elastic Stack', 'Laravel', 'ASP.NET',
         'Aurora DB', 'Redux-Observable', '.NET', 'AWS Backup',
         'AWS CloudWatch', 'Kibana', 'Fluentd', 'Logstash', 'JSP',
         'Bootstrap', 'Datadog', 'Rust', 'Azure', 'Apache Hadoop',
         'AWS X-Ray', 'Memcached', 'Jest', 'Mocha',
         'DRF(Django REST framework)', 'Spring Cloud', 'Data Analysys',
         'Big Data', 'GitLab', 'Gradle', 'SQLite', 'Microsoft IIS', 'Unity',
         'Electron', 'MariaDB', 'mSQL', 'gensim', 'Scikit-Learn',
         'AWS Simple Queue Service(AWS SQS)', 'gRPC',
         'Naver Cloud Platform', 'Ubuntu', 'Microservice Architecture',
         'Apache ActiveMQ', 'Oracle Database', 'Apache Subversion(SVN)',
         'Apache Tomcat', 'Red Hat Ceph Storage', 'Puppeteer', 'OpenLayers',
         'Vuex', 'Less.js', 'JIRA', 'Keras', 'NCP(Naver Cloud Platform)',
         'NestJS', 'PKI(Public key infrastructure)', 'AWS ECS', 'Hibernate',
         'UML', 'BitBucket', 'Arduino', 'Raspberry Pi', 'RabbitMQ',
         'Capistrano', 'Bamboo', 'MVP', 'OkHttp', 'Cocos2d', 'Ethereum',
         'Blockchain', 'DSP(Digital Signal Processing)', 'D3.js', 'Cocoa',
         'Axios', 'Ionic', 'WPF', 'AWS IAM', 'Shell Script',
         'Responsive Web', 'Canvas', 'ThreeJS', 'Apache ZooKeeper',
         'Pandas', 'Spring Batch', 'JUnit', 'Spring Data JPA', 'ASP',
         'Grunt', 'WordPress', 'MyBatis', 'AWS ElastiCache',
         'Apache HTTP Server', 'AWS Security Hub', 'Google API', 'Qt',
         'CAD', 'GatsbyJS', 'PostCSS', 'Socket.IO', 'Backbone.js',
         'Azure Linux Virtual Machines', 'Heroku', 'CUDA', 'IOCP', 'Unix',
         'CocoaPods', 'MVVM(Model-View-ViewModel)',
         'Google Firebase Crashlytics', 'Google Cloud Platform',
         'Windows kernel', 'OpenCV', 'Unreal Engine', 'Google Cloud SDK',
         'RxAndroid', 'Windows Embedded', 'Entity Framework', 'Packer',
         'Nexus', 'Consul', 'Selenium', 'Jekyll', 'XML',
         'Dependency Lookup', 'RxKotlin', 'Expo', 'Sketch', 'InVision',
         'Azure Text Analytics', 'Google Dialogflow',
         'Google Cloud Natural Language'], dtype=object)


let say I have this array list 
if I want to group them by similar things 
is there any pretrained language model to do this job easier? 



for example  
pytorch and tensorflow should be in one group because 
most of the deep learning people are using pytorch or tensorflow
There will not be any pre-trained models to cluster these words.

In fact, in order to build your own clustering model you will need more metadata about each observation/word in your array.

At the moment any model would only be able to "see" the name of the package/software in your array. So the best you could hope for is a model that clusters these words based on their spellings.

Now let's pretend you find a brief description of each software, now you could do a bit more. With this longer text you could use supervised or unsupervised methods to cluster these softwares into groups (see topic models, k-means etc) based upon similar words in the descriptions.

Long story short, there is not a pre-built model to do this, and to build one yourself you're going to need more information about each observation.