Bidirectional Encoder Representations from Transformers (BERT) | Masked Language Modeling |Explained

RoboSathi · Beginner ·🧠 Large Language Models ·2mo ago

About this lesson

📘 Notes: https://robosathi.com/docs/natural_language_processing/bert/ 🎥 NLP Playlist: https://www.youtube.com/playlist?list=PLnpa6KP2ZQxcDlHCeNiKbRhLWKVunQaxn 🎥 Transformer: https://youtu.be/eD2pF-NvzSs 🎥 LLM: https://youtu.be/vEqaew-D28U ✅ This video describes the BERT architecture and its working in depth will all the desired maths. ✅ BERT: Google AI Language team developed, Transformer (encoder only) based language model, designed to understand the meaning of words in a text by using the context from both directions, making it ideal for Natural Language Inference kind of tasks. 🕔 Time Stamp 🕘 00:00:00 - 00:01:23 Introduction 00:01:24 - 00:02:15 BERT Architecture 00:02:16 - 00:03:30 Limitations of Word Vectors 00:03:31 - 00:06:36 Bidirectional Encoder Representations from Transformers (BERT) 00:06:37 - 00:10:20 Pre Training Phase 00:10:21 - 00:11:19 Masked Language Modeling 00:11:20 - 00:13:06 BERT Input Representation 00:13:07 - 00:16:27 Next Sentence Prediction 00:16:28 - 00:18:29 BERT Special Tokens; CLS (Classification) & SEP (Separator) 00:18:30 - 00:21:24 Key Applications of BERT 00:21:25 - 00:22:04 Next: Sentence BERT

Original Description

📘 Notes: https://robosathi.com/docs/natural_language_processing/bert/ 🎥 NLP Playlist: https://www.youtube.com/playlist?list=PLnpa6KP2ZQxcDlHCeNiKbRhLWKVunQaxn 🎥 Transformer: https://youtu.be/eD2pF-NvzSs 🎥 LLM: https://youtu.be/vEqaew-D28U ✅ This video describes the BERT architecture and its working in depth will all the desired maths. ✅ BERT: Google AI Language team developed, Transformer (encoder only) based language model, designed to understand the meaning of words in a text by using the context from both directions, making it ideal for Natural Language Inference kind of tasks. 🕔 Time Stamp 🕘 00:00:00 - 00:01:23 Introduction 00:01:24 - 00:02:15 BERT Architecture 00:02:16 - 00:03:30 Limitations of Word Vectors 00:03:31 - 00:06:36 Bidirectional Encoder Representations from Transformers (BERT) 00:06:37 - 00:10:20 Pre Training Phase 00:10:21 - 00:11:19 Masked Language Modeling 00:11:20 - 00:13:06 BERT Input Representation 00:13:07 - 00:16:27 Next Sentence Prediction 00:16:28 - 00:18:29 BERT Special Tokens; CLS (Classification) & SEP (Separator) 00:18:30 - 00:21:24 Key Applications of BERT 00:21:25 - 00:22:04 Next: Sentence BERT

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Chapters (11)

00:01:23 Introduction

1:24 00:02:15 BERT Architecture

2:16 00:03:30 Limitations of Word Vectors

3:31 00:06:36 Bidirectional Encoder Representations from Transformers (BERT)

6:37 00:10:20 Pre Training Phase

10:21 00:11:19 Masked Language Modeling

11:20 00:13:06 BERT Input Representation

13:07 00:16:27 Next Sentence Prediction

16:28 00:18:29 BERT Special Tokens; CLS (Classification) & SEP (Separator)

18:30 00:21:24 Key Applications of BERT

21:25 00:22:04 Next: Sentence BERT

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