BERT: Bidirectional Encoder Representations from TransformersLiangqun Lu
BERT was developed by Google AI Language and came out Oct. 2018. It has achieved the best performance in many NLP tasks. So if you are interested in NLP, studying BERT is a good way to go.
BERT: Bidirectional Encoder Representation from Transformer.
BERT is a Pretrained Model by Google for State of the art NLP tasks.
BERT has the ability to take into account Syntaxtic and Semantic meaning of Text.
BERT - Part 1 Learning Notes of Senthil KumarSenthil Kumar M
In this part 1 presentation, I have attempted to provide a '30,000 feet view' of BERT (Bidirectional Encoder Representations from Transformer) - a state of the art Language Model in NLP with high level technical explanations. I have attempted to collate useful information about BERT from various useful sources.
BERT: Bidirectional Encoder Representations from TransformersLiangqun Lu
BERT was developed by Google AI Language and came out Oct. 2018. It has achieved the best performance in many NLP tasks. So if you are interested in NLP, studying BERT is a good way to go.
BERT: Bidirectional Encoder Representation from Transformer.
BERT is a Pretrained Model by Google for State of the art NLP tasks.
BERT has the ability to take into account Syntaxtic and Semantic meaning of Text.
BERT - Part 1 Learning Notes of Senthil KumarSenthil Kumar M
In this part 1 presentation, I have attempted to provide a '30,000 feet view' of BERT (Bidirectional Encoder Representations from Transformer) - a state of the art Language Model in NLP with high level technical explanations. I have attempted to collate useful information about BERT from various useful sources.
An introduction to the Transformers architecture and BERTSuman Debnath
The transformer is one of the most popular state-of-the-art deep (SOTA) learning architectures that is mostly used for natural language processing (NLP) tasks. Ever since the advent of the transformer, it has replaced RNN and LSTM for various tasks. The transformer also created a major breakthrough in the field of NLP and also paved the way for new revolutionary architectures such as BERT.
BERT: Pre-training of Deep Bidirectional Transformers for Language UnderstandingYoung Seok Kim
Review of paper
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
ArXiv link: https://arxiv.org/abs/1810.04805
YouTube Presentation: https://youtu.be/GK4IO3qOnLc
(Slides are written in English, but the presentation is done in Korean)
A presentation on Bidirectional Encoder Representations from Transformers (BERT) meant to introduce the model's use cases and training mechanism. Best viewed with powerpoint since it contain many slide animations.
最近のNLP×DeepLearningのベースになっている"Transformer"について、研究室の勉強会用に作成した資料です。参考資料の引用など正確を期したつもりですが、誤りがあれば指摘お願い致します。
This is a material for the lab seminar about "Transformer", which is the base of recent NLP x Deep Learning research.
GPT-2: Language Models are Unsupervised Multitask LearnersYoung Seok Kim
Review of paper
Language Models are Unsupervised Multitask Learners
(GPT-2)
by Alec Radford et al.
Paper link: https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf
YouTube presentation: https://youtu.be/f5zULULWUwM
(Slides are written in English, but the presentation is done in Korean)
Beyond the Symbols: A 30-minute Overview of NLPMENGSAYLOEM1
This presentation delves into the world of Natural Language Processing (NLP), exploring its goal to make human language understandable to machines. The complexities of language, such as ambiguity and complex structures, are highlighted as major challenges. The talk underscores the evolution of NLP through deep learning methodologies, leading to a new era defined by large-scale language models. However, obstacles like low-resource languages and ethical issues including bias and hallucination are acknowledged as enduring challenges in the field. Overall, the presentation provides a condensed, yet comprehensive view of NLP's accomplishments and ongoing hurdles.
I summarized the GPT models in this slide and compared the GPT1, GPT2, and GPT3.
GPT means Generative Pre-Training of a language model and was implemented based on the decoder structure of the transformer model.
(24th May, 2021)
BERT: Pre-training of Deep Bidirectional Transformers for Language Understandinggohyunwoong
This presentation is for SotA models in NLP called Transformer & BERT review materials. I reviewed many model in here Word2Vec, ELMo, GPT, ... etc
reference 1 : Kim Dong Ha (https://www.youtube.com/watch?v=xhY7m8QVKjo)
reference 2 : Raimi Karim (https://towardsdatascience.com/attn-illustrated-attention-5ec4ad276ee3)
Neural Language Generation Head to Toe Hady Elsahar
This is a gentle introduction to Natural language Generation (NLG) using deep learning. If you are a computer science practitioner with basic knowledge about Machine learning. This is a gentle intuitive introduction to Language Generation using Neural Networks. It takes you in a journey from the basic intuitions behind modeling language and how to model probabilities of sequences to recurrent neural networks to large Transformers models that you have seen in the news like GPT2/GPT3. The tutorial wraps up with a summary on the ethical implications of training such large language models on uncurated text from the internet.
A talk on Transformers at GDG DevParty
27.06.2020
Link to Google Slides version: https://docs.google.com/presentation/d/1N7ayCRqgsFO7TqSjN4OWW-dMOQPT5DZcHXsZvw8-6FU/edit?usp=sharing
An introduction to the Transformers architecture and BERTSuman Debnath
The transformer is one of the most popular state-of-the-art deep (SOTA) learning architectures that is mostly used for natural language processing (NLP) tasks. Ever since the advent of the transformer, it has replaced RNN and LSTM for various tasks. The transformer also created a major breakthrough in the field of NLP and also paved the way for new revolutionary architectures such as BERT.
BERT: Pre-training of Deep Bidirectional Transformers for Language UnderstandingYoung Seok Kim
Review of paper
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
ArXiv link: https://arxiv.org/abs/1810.04805
YouTube Presentation: https://youtu.be/GK4IO3qOnLc
(Slides are written in English, but the presentation is done in Korean)
A presentation on Bidirectional Encoder Representations from Transformers (BERT) meant to introduce the model's use cases and training mechanism. Best viewed with powerpoint since it contain many slide animations.
最近のNLP×DeepLearningのベースになっている"Transformer"について、研究室の勉強会用に作成した資料です。参考資料の引用など正確を期したつもりですが、誤りがあれば指摘お願い致します。
This is a material for the lab seminar about "Transformer", which is the base of recent NLP x Deep Learning research.
GPT-2: Language Models are Unsupervised Multitask LearnersYoung Seok Kim
Review of paper
Language Models are Unsupervised Multitask Learners
(GPT-2)
by Alec Radford et al.
Paper link: https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf
YouTube presentation: https://youtu.be/f5zULULWUwM
(Slides are written in English, but the presentation is done in Korean)
Beyond the Symbols: A 30-minute Overview of NLPMENGSAYLOEM1
This presentation delves into the world of Natural Language Processing (NLP), exploring its goal to make human language understandable to machines. The complexities of language, such as ambiguity and complex structures, are highlighted as major challenges. The talk underscores the evolution of NLP through deep learning methodologies, leading to a new era defined by large-scale language models. However, obstacles like low-resource languages and ethical issues including bias and hallucination are acknowledged as enduring challenges in the field. Overall, the presentation provides a condensed, yet comprehensive view of NLP's accomplishments and ongoing hurdles.
I summarized the GPT models in this slide and compared the GPT1, GPT2, and GPT3.
GPT means Generative Pre-Training of a language model and was implemented based on the decoder structure of the transformer model.
(24th May, 2021)
BERT: Pre-training of Deep Bidirectional Transformers for Language Understandinggohyunwoong
This presentation is for SotA models in NLP called Transformer & BERT review materials. I reviewed many model in here Word2Vec, ELMo, GPT, ... etc
reference 1 : Kim Dong Ha (https://www.youtube.com/watch?v=xhY7m8QVKjo)
reference 2 : Raimi Karim (https://towardsdatascience.com/attn-illustrated-attention-5ec4ad276ee3)
Neural Language Generation Head to Toe Hady Elsahar
This is a gentle introduction to Natural language Generation (NLG) using deep learning. If you are a computer science practitioner with basic knowledge about Machine learning. This is a gentle intuitive introduction to Language Generation using Neural Networks. It takes you in a journey from the basic intuitions behind modeling language and how to model probabilities of sequences to recurrent neural networks to large Transformers models that you have seen in the news like GPT2/GPT3. The tutorial wraps up with a summary on the ethical implications of training such large language models on uncurated text from the internet.
A talk on Transformers at GDG DevParty
27.06.2020
Link to Google Slides version: https://docs.google.com/presentation/d/1N7ayCRqgsFO7TqSjN4OWW-dMOQPT5DZcHXsZvw8-6FU/edit?usp=sharing
The NLP muppets revolution! @ Data Science London 2019
video: https://skillsmatter.com/skillscasts/13940-a-deep-dive-into-contextual-word-embeddings-and-understanding-what-nlp-models-learn
event: https://www.meetup.com/Data-Science-London/events/261483332/
Evolving Reinforcement Learning Algorithms, JD. Co-Reyes et al, 2021Chris Ohk
RL 논문 리뷰 스터디에서 Evolving Reinforcement Learning Algorithms 논문 내용을 정리해 발표했습니다. 이 논문은 Value-based Model-free RL 에이전트의 손실 함수를 표현하는 언어를 설계하고 기존 DQN보다 최적화된 손실 함수를 제안합니다. 많은 분들에게 도움이 되었으면 합니다.
[PACLING2019] Improving Context-aware Neural Machine Translation with Target-...Hayahide Yamagishi
This is the slide used in the oral presentation at PACLING2019.
(For Japanese speakers) 本発表は私の修論発表と同等ですので、日本語がわかる方は以下のスライドの方が読みやすいかもしれません。
https://www.slideshare.net/HayahideYamagishi/ss-181147693/HayahideYamagishi/ss-181147693
Font has been changed the original one (Hiragino Maru Gothic Pro W4) into the other one by the SlideShare.
Presentation about an eclipse framework that allows to generate ecore model instances as input for tests and benchmarks. Held at the 3rd BigMDE workshop at STAF in L'Aquia, Italy in July 2015.
Training separate models from scratch or fine-tuning each individually for different tasks is costly in terms of computational resources, memory usage, and environmental impact. Multi-task learning leverages information across N tasks and datasets to enhance their performance. This approach presents benefits such as a shared model, representation bias, increased data efficiency, and eavesdropping. To mitigate issues such as catastrophic forgetting and interference, various methods have been proposed. This talk will explore the concepts of a general approach to multi-task learning in transformer-based architectures, novel adapter-based and hypernetwork techniques, and solutions to task sampling and balancing problems.
Python programming concepts for the Internet of things applications development. This PPT contains details about classes, list , tuples, dictionaries, packages like HTTPLib,SMTPLib, etc
ODSC East: Effective Transfer Learning for NLPindico data
Presented by indico co-founder Madison May at ODSC East.
Abstract: Transfer learning, the practice of applying knowledge gained on one machine learning task to aid the solution of a second task, has seen historic success in the field of computer vision. The output representations of generic image classification models trained on ImageNet have been leveraged to build models that detect the presence of custom objects in natural images. Image classification tasks that would typically require hundreds of thousands of images can be tackled with mere dozens of training examples per class thanks to the use of these pretrained reprsentations. The field of natural language processing, however, has seen more limited gains from transfer learning, with most approaches limited to the use of pretrained word representations. In this talk, we explore parameter and data efficient mechanisms for transfer learning on text, and show practical improvements on real-world tasks. In addition, we demo the use of Enso, a newly open-sourced library designed to simplify benchmarking of transfer learning methods on a variety of target tasks. Enso provides tools for the fair comparison of varied feature representations and target task models as the amount of training data made available to the target model is incrementally increased.
This presentation material is my review about SOTA model related paper entitled with "Code Translation with Compiler Representations". It is a paper from Meta AI, and was accepted for an ICLR 2023.
Tool Competition
Introduction
NLP-based approaches and tools have been proposed to improve the efficiency of software engineers, processes, and products, by automatically processing natural language artifacts (issues, emails, commits, etc.).
We believe that the availability of accurate tools is becoming increasingly necessary to improve Software Engineering (SE) processes. One important process is issue management and prioritization where developers have to understand, classify, prioritize, assign, etc. incoming issues reported by end-users and developers.
This year, we are pleased to announce the first edition of the NLBSE’22 tool competition on issue report classification, an important task in issue management and prioritization.
For the competition, we provide a dataset encompassing more than 800k labeled issue reports (as bugs, enhancements, and questions) extracted from real open-source projects. You are invited to leverage this dataset for evaluating your classification approaches and compare the achieved results against a proposed baseline approach (based on FastText).
Competition overview
We created a Colab notebook with detailed information about the competition (provided data, baseline approach, paper submission, paper format, etc.).
If you want to participate, you must:
Train and tune a multi-label multi-class classifier using the provided training set. The classifier should assign one label to an issue.
Evaluate your classifier on the provided test set
Write a paper (4 pages max.) describing:
The architecture and details of the classifier
The procedure used to pre-process the data
The procedure used to tune the classifier on the training set
The results of your classifier on the test set
Additional info.: provide a link to your code/tool with proper documentation on how to run it
Submit the paper by emailing the tool competition organizers (see below)
Submissions will be evaluated and accepted based on correctness and reproducibility, defined by the following criteria:
Clarity and detail of the paper content
Availability of the code/tool, released as open-source
Correct training/tuning/evaluation of your code/tool on the provided data
Clarity of the code documentation
The accepted submissions will be published at the workshop proceedings.
The submissions will be ranked based on the F1 score achieved by the proposed classifiers on the test set, as indicated in the papers.
The submission with the highest F1 score will be the winner of the competition.
How to participate?
Email your paper to Oscar Chaparro (oscarch@wm.edu) and Rafael Kallis (rk@rafaelkallis.com) by the submission deadline.
Financial Question Answering with BERT Language ModelsBithiah Yuan
FinBERT-QA is a Question Answering system for retrieving opinionated financial passages from task 2 of the FiQA dataset. The system uses techniques from both information retrieval, natural language processing, and deep learning.
Naver learning to rank question answer pairs using hrde-ltcNAVER Engineering
The automatic question answering (QA) task has long been considered a primary objective of artificial intelligence.
Among the QA sub-systems, we focused on answer-ranking part. In particular, we investigated a novel neural network architecture with additional data clustering module to improve the performance in ranking answer candidates which are longer than a single sentence. This work can be used not only for the QA ranking task, but also to evaluate the relevance of next utterance with given dialogue generated from the dialogue model.
In this talk, I'll present our research results (NAACL 2018), and also its potential use cases (i.e. fake news detection). Finally, I'll conclude by introducing some issues on previous research, and by introducing recent approach in academic.
DELAB - sequence generation seminar
Title
Open vocabulary problem
Table of contents
1. Open vocabulary problem
1-1. Open vocabulary problem
1-2. Ignore rare words
1-3. Approximative Softmax
1-4. Back-off Models
1-5. Character-level model
2. Solution1: Byte Pair Encoding(BPE)
3. Solution2: WordPieceModel(WPM)
Similar to BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (20)
Prompt Engineering Tutorial: Cách viết prompt hiệu quả với ChatGPTMinh Pham
Bài giảng về cách sử dụng prompt engineering hiệu quả với ChatGPT. Sau khi học xong bài giảng, người dùng hiểu về cấu trúc cơ bản của prompt, biết cách thiết kế prompt một cách hiệu quả, tiết kiệm
AimeLaw at ALQAC 2021: Enriching Neural Network Models with Legal-Domain Know...Minh Pham
Our presentation slide at the 13th IEEE International Conference on Knowledge and Systems Engineering (KSE 2021).
In this paper, we present our participated systems for three Vietnamese legal text processing tasks at Automated Legal Question Answering Competition (ALQAC 2021). In our systems, we leverage the strength of traditional information retrieval methods (BM25), pre-trained masked language models (BERT), and legal domain knowledge. Our proposed methods help to overcome the shortage of training data. Especially, in the legal textual entailment task, we propose a novel data augmentation
method that is based on legal domain knowledge. Evaluation
results show the effectiveness of our proposed methods.
Research methods for engineering students (v.2020)Minh Pham
Beginning students who start doing research may face to many difficulties from choosing a good research topic to start, how to develop new ideas to how to implement models to test their ideas and write papers. Research skill is a craft skill. You only learn it by doing. However, it is good to learn know-how in doing research. In this lecture, I share information of how-to-do research for engineering students with the hope that it will help students to save time at the beginning state of doing research.
Tài liệu giới thiệu kiến thức cơ bản về AIML và cách sử dụng khi phát triển chatbot. Để áp dụng được tốt hơn, độc giả cần tìm hiểu các tài liệu chi tiết hơn.
Mạng neural nhân tạo và ứng dụng trong xử lý ngôn ngữ tự nhiênMinh Pham
Slide bài thuyết trình tại sự kiện của của công ty rubikAI. Nội dung của bài trình bày là kiến thức cơ bản về mạng neural và ứng dụng trong xử lý ngôn ngữ tự nhiên.
Slide của bài trình bày tại al+ AI Seminar số 4 về báo bài báo được giải thưởng best paper award tại hội nghị NAACL 2018
Peters et al., 2018. Deep Contextualized Word Representations. In NAACL.
Bài báo gốc: http://aclweb.org/anthology/N18-1202
Mô hình ELMo là mô hình biểu diễn từ phụ thuộc ngữ cảnh học từ mô hình ngôn ngữ hai chiều. ELMo được áp dụng cho nhiều bài toán khác nhau và đạt kết quả tốt nhất trên nhiều tập dữ liệu.
Research Methods in Natural Language Processing (2018 version)Minh Pham
Updated version of my lecture slide about "Research Methods in Natural Language Processing" for the course RAW-501 in Master program of FPT University.
A Feature-Based Model for Nested Named-Entity Recognition at VLSP-2018 NER Ev...Minh Pham
The presentation of a feature-based model for nested named-entity recognition at VLSP 2018. Our system obtained the first rank among participant systems. There is still a gap between the accuracy on the development set and the test set.
Về kỹ thuật Attention trong mô hình sequence-to-sequence tại hội nghị ACL 2017Minh Pham
Trình bày về kỹ thuật attention trong mô hình sequence-to-sequence và ứng dụng trong các nghiên cứu NLP tại ACL 2017. Ngoài ra chúng tôi cũng tóm tắt một số các nghiên cứu thú vị khác tại hội nghị.
Các bài toán xử lý ngôn ngữ tự nhiên trong phát triển hệ thống chatbotMinh Pham
Trình bày về những bài toán xử lý ngôn ngữ tự nhiên trong phát triển hệ thống chatbot theo mô hình truy xuất thông tin. Ngoài ra mô hình sinh hội thoại sử dụng mạng Neural cũng được đề cập (neural chatbot)
AI Pilot Review: The World’s First Virtual Assistant Marketing SuiteGoogle
AI Pilot Review: The World’s First Virtual Assistant Marketing Suite
👉👉 Click Here To Get More Info 👇👇
https://sumonreview.com/ai-pilot-review/
AI Pilot Review: Key Features
✅Deploy AI expert bots in Any Niche With Just A Click
✅With one keyword, generate complete funnels, websites, landing pages, and more.
✅More than 85 AI features are included in the AI pilot.
✅No setup or configuration; use your voice (like Siri) to do whatever you want.
✅You Can Use AI Pilot To Create your version of AI Pilot And Charge People For It…
✅ZERO Manual Work With AI Pilot. Never write, Design, Or Code Again.
✅ZERO Limits On Features Or Usages
✅Use Our AI-powered Traffic To Get Hundreds Of Customers
✅No Complicated Setup: Get Up And Running In 2 Minutes
✅99.99% Up-Time Guaranteed
✅30 Days Money-Back Guarantee
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See My Other Reviews Article:
(1) TubeTrivia AI Review: https://sumonreview.com/tubetrivia-ai-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
Graspan: A Big Data System for Big Code AnalysisAftab Hussain
We built a disk-based parallel graph system, Graspan, that uses a novel edge-pair centric computation model to compute dynamic transitive closures on very large program graphs.
We implement context-sensitive pointer/alias and dataflow analyses on Graspan. An evaluation of these analyses on large codebases such as Linux shows that their Graspan implementations scale to millions of lines of code and are much simpler than their original implementations.
These analyses were used to augment the existing checkers; these augmented checkers found 132 new NULL pointer bugs and 1308 unnecessary NULL tests in Linux 4.4.0-rc5, PostgreSQL 8.3.9, and Apache httpd 2.2.18.
- Accepted in ASPLOS ‘17, Xi’an, China.
- Featured in the tutorial, Systemized Program Analyses: A Big Data Perspective on Static Analysis Scalability, ASPLOS ‘17.
- Invited for presentation at SoCal PLS ‘16.
- Invited for poster presentation at PLDI SRC ‘16.
May Marketo Masterclass, London MUG May 22 2024.pdfAdele Miller
Can't make Adobe Summit in Vegas? No sweat because the EMEA Marketo Engage Champions are coming to London to share their Summit sessions, insights and more!
This is a MUG with a twist you don't want to miss.
Top Features to Include in Your Winzo Clone App for Business Growth (4).pptxrickgrimesss22
Discover the essential features to incorporate in your Winzo clone app to boost business growth, enhance user engagement, and drive revenue. Learn how to create a compelling gaming experience that stands out in the competitive market.
In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I ...Juraj Vysvader
In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I didn't get rich from it but it did have 63K downloads (powered possible tens of thousands of websites).
Providing Globus Services to Users of JASMIN for Environmental Data AnalysisGlobus
JASMIN is the UK’s high-performance data analysis platform for environmental science, operated by STFC on behalf of the UK Natural Environment Research Council (NERC). In addition to its role in hosting the CEDA Archive (NERC’s long-term repository for climate, atmospheric science & Earth observation data in the UK), JASMIN provides a collaborative platform to a community of around 2,000 scientists in the UK and beyond, providing nearly 400 environmental science projects with working space, compute resources and tools to facilitate their work. High-performance data transfer into and out of JASMIN has always been a key feature, with many scientists bringing model outputs from supercomputers elsewhere in the UK, to analyse against observational or other model data in the CEDA Archive. A growing number of JASMIN users are now realising the benefits of using the Globus service to provide reliable and efficient data movement and other tasks in this and other contexts. Further use cases involve long-distance (intercontinental) transfers to and from JASMIN, and collecting results from a mobile atmospheric radar system, pushing data to JASMIN via a lightweight Globus deployment. We provide details of how Globus fits into our current infrastructure, our experience of the recent migration to GCSv5.4, and of our interest in developing use of the wider ecosystem of Globus services for the benefit of our user community.
OpenFOAM solver for Helmholtz equation, helmholtzFoam / helmholtzBubbleFoamtakuyayamamoto1800
In this slide, we show the simulation example and the way to compile this solver.
In this solver, the Helmholtz equation can be solved by helmholtzFoam. Also, the Helmholtz equation with uniformly dispersed bubbles can be simulated by helmholtzBubbleFoam.
Exploring Innovations in Data Repository Solutions - Insights from the U.S. G...Globus
The U.S. Geological Survey (USGS) has made substantial investments in meeting evolving scientific, technical, and policy driven demands on storing, managing, and delivering data. As these demands continue to grow in complexity and scale, the USGS must continue to explore innovative solutions to improve its management, curation, sharing, delivering, and preservation approaches for large-scale research data. Supporting these needs, the USGS has partnered with the University of Chicago-Globus to research and develop advanced repository components and workflows leveraging its current investment in Globus. The primary outcome of this partnership includes the development of a prototype enterprise repository, driven by USGS Data Release requirements, through exploration and implementation of the entire suite of the Globus platform offerings, including Globus Flow, Globus Auth, Globus Transfer, and Globus Search. This presentation will provide insights into this research partnership, introduce the unique requirements and challenges being addressed and provide relevant project progress.
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI AppGoogle
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI App
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https://sumonreview.com/ai-fusion-buddy-review
AI Fusion Buddy Review: Key Features
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✅Use Gemini to Build high-converting Converting Sales Video Scripts, ad copies, Trending Articles, blogs, etc.100% unique!
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See My Other Reviews Article:
(1) AI Genie Review: https://sumonreview.com/ai-genie-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
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#HowDoesAIFusionBuddyWorks
First Steps with Globus Compute Multi-User EndpointsGlobus
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A Study of Variable-Role-based Feature Enrichment in Neural Models of Code
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
1. BERT: Pre-training of Deep
Bidirectional Transformers for
Language Understanding
Devlin et al., 2018 (Google AI Language)
Presenter
Phạm Quang Nhật Minh
NLP Researcher
Alt Vietnam
al+ AI Seminar No. 7
2018/12/21
3. Research context
• Language model pre-training has been used to
improve many NLP tasks
• ELMo (Peters et al., 2018)
• OpenAI GPT (Radford et al., 2018)
• ULMFit (Howard and Rudder, 2018)
• Two existing strategies for applying pre-trained
language representations to downstream tasks
• Feature-based: include pre-trained representations as
additional features (e.g., ELMo)
• Fine-tunning: introduce task-specific parameters and
fine-tune the pre-trained parameters (e.g., OpenAI GPT,
ULMFit)
12/21/18 al+ AI Seminar No.7 3
4. Limitations of current techniques
• Language models in pre-training are unidirectional,
they restrict the power of the pre-trained
representations
• OpenAI GPT used left-to-right architecture
• ELMo concatenates forward and backward language
models
• Solution BERT: Bidirectional Encoder
Representations from Transformers
12/21/18 al+ AI Seminar No.7 4
5. BERT: Bidirectional Encoder
Representations from Transformers
• Main ideas
• Propose a new pre-training objective so that a deep
bidirectional Transformer can be trained
• The “masked language model” (MLM): the objective is to
predict the original word of a masked word based only on its
context
• ”Next sentence prediction”
• Merits of BERT
• Just fine-tune BERT model for specific tasks to achieve
state-of-the-art performance
• BERT advances the state-of-the-art for eleven NLP tasks
12/21/18 al+ AI Seminar No.7 5
6. 12/21/18 al+ AI Seminar No.7 6
BERT: Bidirectional Encoder
Representations from Transformers
7. Model architecture
• BERT’s model architecture is a multi-layer
bidirectional Transformer encoder
• (Vaswani et al., 2017) “Attention is all you need”
• Two models with different sizes were investigated
• BERTBASE: L=12, H=768, A=12, Total Parameters=110M
• (L: number of layers (Transformer blocks), H is the hidden size,
A: the number of self-attention heads)
• BERTLARGE: L=24, H=1024, A=16, Total Parameters=340M
12/21/18 al+ AI Seminar No.7 7
8. 12/21/18 al+ AI Seminar No.7 8
Differences in pre-training model architectures: BERT,
OpenAI GPT, and ELMo
9. Transformer Encoders
• Transformer is an attention-based architecture for NLP
• Transformer composed of two parts: Encoding
component and Decoding component
• BERT is a multi-layer bidirectional Transformer encoder
12/21/18 al+ AI Seminar No.7 9
Vaswani et al. (2017) Attention is all you need
Encoder Block
Encoder Block
Encoder Block
Input sequence
10. Inside an Encoder Block
12/21/18 al+ AI Seminar No.7 10
Source: https://medium.com/dissecting-
bert/dissecting-bert-part-1-d3c3d495cdb3
In BERT experiments, the number
of blocks N was chosen to be 12
and 24.
Blocks do not share weights with
each other
13. Self-Attention in Detail
• Attention maps a query and a set of key-value pairs
to an output
• query, keys, and output are all vectors
12/21/18 al+ AI Seminar No.7 13
Input
Queries
Keys
Values
X1 X2
q1 q2
k1 k2
v1 v2
Use matrices WQ , WK and
WV to project input into
query, key and value vectors
Attention ', ), * = softmax
')1
23
* dk is the dimension of
key vectors
14. Multi-Head Attention
12/21/18 al+ AI Seminar No.7 14
X1
X2
...
Head #0 Head #1 Head #7
Concat
Linear Projection
Use a weight
matrix Wo
15. Position Encoding
• Position Encoding is used to make use of the order of the
sequence
• Since the model contains no recurrence and no convolution
• In Vawasni et al., 2017, authors used sine and cosine functions of
different frequencies
!"($%&,()) = sin
/01
10000
()
4!"#$%
!"($%&,()56) = cos
/01
10000
()
4!"#$%
• pos is the position and 9 is the dimension
12/21/18 al+ AI Seminar No.7 15
16. Input Representation
12/21/18 al+ AI Seminar No.7 16
• Token Embeddings: Use pretrained WordPiece embeddings
• Position Embeddings: Use learned Position Embeddings
• Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, and Yann N.
Dauphin. Convolutionalsequence to sequence learning. arXiv preprint
arXiv:1705.03122v2, 2017.
• Added sentence embedding to every tokens of each sentence
• Use [CLS] for the classification tasks
• Separate sentences by using a special token [SEP]
17. Task#1: Masked LM
• 15% of the words are masked at random
• and the task is to predict the masked words based on its
left and right context
• Not all tokens were masked in the same way
(example sentence “My dog is hairy”)
• 80% were replaced by the <MASK> token: “My dog is
<MASK>”
• 10% were replaced by a random token: “My dog is
apple”
• 10% were left intact: “My dog is hairy”
12/21/18 al+ AI Seminar No.7 17
18. Task#2: Next Sentence Prediction
• Motivation
• Many downstream tasks are based on understanding the
relationship between two text sentences
• Question Answering (QA) and Natural Language Inference (NLI)
• Language modeling does not directly capture that
relationship
• The task is pre-training binarized next sentence
prediction task
12/21/18 al+ AI Seminar No.7 18
Input = [CLS] the man went to [MASK] store [SEP] he bought a gallon
[MASK] milk [SEP]
Label = isNext
Input = [CLS] the man [MASK] to the store [SEP]penguin [MASK] are
flight ##less birds [SEP]
Label = NotNext
19. Pre-training procedure
• Training data: BooksCorpus (800M words) + English
Wikipedia (2,500M words)
• To generate each training input sequences: sample
two spans of text (A and B) from the corpus
• The combined length is ≤ 500 tokens
• 50% B is the actual next sentence that follows A and 50%
of the time it is a random sentence from the corpus
• The training loss is the sum of the mean masked
LM likelihood and the mean next sentence
prediction likelihood
12/21/18 al+ AI Seminar No.7 19
20. Fine-tuning procedure
• For sequence-level classification task
• Obtain the representation of the input sequence by using the
final hidden state (hidden state at the position of the special
token [CLS]) ! ∈ #$
• Just add a classification layer and use softmax to calculate
label probabilities. Parameters W ∈ #&×$
( = softmax(!23
)
12/21/18 al+ AI Seminar No.7 20
21. Fine-tuning procedure
• For sequence-level classification task
• All of the parameters of BERT and W are fine-tuned
jointly
• Most model hyperparameters are the same as in
pre-training
• except the batch size, learning rate, and number of
training epochs
12/21/18 al+ AI Seminar No.7 21
22. Fine-tuning procedure
• Token tagging task (e.g., Named Entity Recognition)
• Feed the final hidden representation !" ∈ $% for each
token & into a classification layer for the tagset (NER
label set)
• To make the task compatible with WordPiece
tokenization
• Predict the tag for the first sub-token of a word
• No prediction is made for X
12/21/18 al+ AI Seminar No.7 22
24. Fine-tuning procedure
• Span-level task: SQuAD v1.1
• Input Question:
Where do water droplets collide with ice
crystals to form precipitation?
• Input Paragraph:
.... Precipitation forms as smaller
dropletscoalesce via collision with other rain
dropsor ice crystals within a cloud. ...
• Output Answer:
within a cloud
12/21/18 al+ AI Seminar No.7 24
25. Fine-tuning procedure
• Span-level task: SQuAD v1.1
• Represent the input question and paragraph as a single
packed sequence
• The question uses the A embedding and the paragraph uses
the B embedding
• New parameters to be learned in fine-tuning are start
vector ! ∈ ℝ$ and end vector % ∈ ℝ$
• Calculate the probability of word & being the start of the
answer span
'( =
*+,-!
∑/ *+,-"
• The training objective is the log-likelihood the correct
and end positions
12/21/18 al+ AI Seminar No.7 25
26. Comparison of BERT and OpenAI GPT
OpenAI GPT BERT
Trained on BooksCorpus (800M) Trained on BooksCorpus (800M) +
Wikipedia (2,500M)
Use sentence separater ([SEP])
and classifier token ([CLS]) only at
fine-tuning time
BERT learns [SEP], [CLS] and
sentence A/B embeddings during
pre-training
Trained for 1M steps with a batch-
size of 32,000 words
Trained for 1M steps with a batch-
size of 128,000 words
Use the same learning rate of 5e-5
for all fine-tuning experiments
BERT choose a task-specific
learning rate which performs the
best on the development set
12/21/18 al+ AI Seminar No.7 26
28. Experiments
• GLUE (General Language Understanding Evaluation)
benchmark
• Distribute canonical Train, Dev and Test splits
• Labels for Test set are not provided
• Datasets in GLUE:
• MNLI: Multi-Genre Natural Language Inference
• QQP: Quora Question Pairs
• QNLI: Question Natural Language Inference
• SST-2: Stanford Sentiment Treebank
• CoLA: The corpus of Linguistic Acceptability
• STS-B: The Semantic Textual Similarity Benchmark
• MRPC: Microsoft Research Paraphrase Corpus
• RTE: Recognizing Textual Entailment
• WNLI: Winograd NLI
12/21/18 al+ AI Seminar No.7 28
32. SWAG
• The Situations with Adversarial Generations (SWAG)
• The only task-specific parameters is a vector ! ∈ ℝ$
• The probability distribution is the softmax over the four
choices
%& =
()*+,
∑./0
1
()*+,
12/21/18 al+ AI Seminar No.7 32
34. Ablation Studies
• To understand
• Effect of Pre-training Tasks
• Effect of model sizes
• Effect of number of training steps
• Feature-based approach with BERT
12/21/18 al+ AI Seminar No.7 34
35. Ablation Studies
• Main findings:
• “Next sentence prediction” (NSP) pre-training is important
for sentence-pair classification task
• Bidirectionality (using MLM pre-training task) contributes
significantly to the performance improvement
12/21/18 al+ AI Seminar No.7 35
36. Ablation Studies
• Main findings
• Bigger model sizes are better even for small-scale tasks
12/21/18 al+ AI Seminar No.7 36
37. Conclusions
• Unsupervised pre-training (pre-training language
model) is increasingly adopted in many NLP tasks
• Major contribution of the paper is to propose a
deep bidirectional architecture from Transformer
• Advance state-of-the-art for many important NLP tasks
12/21/18 al+ AI Seminar No.7 37
38. Links
• TensorFlow code and pre-trained models for BERT:
https://github.com/google-research/bert
• PyTorch Pretrained Bert:
https://github.com/huggingface/pytorch-
pretrained-BERT
• BERT-pytorch: https://github.com/codertimo/BERT-
pytorch
• BERT-keras: https://github.com/Separius/BERT-
keras
12/21/18 al+ AI Seminar No.7 38
39. Remark: Applying BERT for non-
English languages
• Pre-trained BERT models are provided for more than
100 languages (including Vietnamese)
• https://github.com/google-
research/bert/blob/master/multilingual.md
• Be careful with tokenization!!
• For Japanese (and Chinese): “spaces were added
around every character in the CJI Unicode rage before
applying WordPiece” => Not a good way to do
• Use SentencePiece:
https://github.com/google/sentencepiece
• We may need to pre-train BERT model
12/21/18 al+ AI Seminar No.7 39
40. References
1. Devlin, J., Chang, M. W., Lee, K., & Toutanova, K.
(2018). BERT: Pre-training of Deep Bidirectional
Transformers for Language Understanding. arXiv
preprint arXiv:1810.04805.
2. Vaswani et al. (2017). Attention Is All You Need. arXiv
preprint arXiv:1706.03762.
https://arxiv.org/abs/1706.03762
3. The Annotated Transformer:
http://nlp.seas.harvard.edu/2018/04/03/attention.ht
ml, by harvardnlp.
4. Dissecting BERT: https://medium.com/dissecting-bert
12/21/18 al+ AI Seminar No.7 40