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๐Ÿ”„ Topic 28: T5 โ€” Text-to-Text Transfer Transformer

Course: NLP Advanced | Section 4: Pre-trained Language Models Topic: T5 โ€” Everything isโ€ฆ

Topic 1
โœจ

๐Ÿ“Œ T5 Kya Hai?

๐Ÿ“š T5 = Text-to-Text Transfer Transformer Google ne 2019 me release kiya. Core Idea: HAR NLPโ€ฆ
Topic 2
๐Ÿ’ก ๐Ÿ“Š ๐Ÿ”ฌ

๐Ÿ—๏ธ T5 Architecture โ€” Full Encoder-Decoder

๐Ÿ’ก

Relative Position Embeddings (notโ€ฆ

๐Ÿ”‘

Pre-norm (LayerNorm BEFOREโ€ฆ

โšก

No bias in dense layers

๐ŸŽฏ

Simplified Layer Norm (no biasโ€ฆ

Topic 3
๐Ÿ”’

๐ŸŽฏ T5 Pre-training โ€” Span Corruption

๐ŸŽฏ ` T5 Pre-training: Span Corruption (not MLM like BERT) Original: "The quick brown foxโ€ฆ
Topic 4
โญ

๐Ÿ”ง T5 Task Formatting โ€” Text-to-Text Examples

โญ ` Har task ko text-in, text-out me convert karo: 1๏ธโƒฃ CLASSIFICATION: Input: "sst2โ€ฆ
Topic 5
โœจ

๐Ÿ’ป Complete Python Implementation

๐Ÿ’ก

Position (i, j) ka bias = f(i - j)

๐Ÿ”‘

Shared across all layers

โšก

Bucketed for long distances

๐ŸŽฏ

Small distances: exact (1 bucketโ€ฆ

Topic 6
๐Ÿ“ฅ ๐Ÿ“ฅ ๐Ÿง  ๐Ÿ”ฌ ๐Ÿ’ก ๐ŸŽฏ

๐Ÿ”‘ Key Takeaways

โœจ ` T5 โ€” Core Lessons: โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”‚ โ”‚ โ”‚โ€ฆ
Topic 7
๐Ÿ“ฅ โš™๏ธ ๐Ÿ”ฌ ๐Ÿ’ก

โ“ Quiz โ€” T5 Mastery

๐Ÿ’ก

A) Har task ke liye alag model

๐Ÿ”‘

B) Har NLP task ko text-to-textโ€ฆ

โšก

C) Sirf translation ke liye hai

๐ŸŽฏ

D) Encoder-only architecture

Topic 8
๐Ÿš€

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Quiz โ€” Question 1

๐Ÿ”„ Topic 28: T5 โ€” Text-to-Text Transfer Transformer ka sabse sahi definition kya hai?

Complete! ๐ŸŽ‰
COMPLETE

๐Ÿ”„ Topic 28: T5 โ€” Text-to-Text Transfer Transformer Complete!

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