NLP_and_Transformers_introduction to Transformer models_presentation.pptx

transformers introduction presentation

1. NLP and Transformers

2. Introduction to Transformers Exploring the Need, Architecture, and Application of Transformer Models

3. Need for Transformers • Overcome the limitations of RNNs and LSTMs in handling long-range dependencies. • Enable parallel processing for faster training and inference. • Address issues like the vanishing gradient problem. • Provide a scalable architecture for handling large datasets and complex tasks.

4. Transformer Architecture • Self-Attention Mechanism: Allows the model to weigh the importance of different parts of the input. • Multi-Head Attention: Captures various aspects of the relationships between tokens. • Feed-Forward Neural Networks: Enhances features learned during attention. • Positional Encoding: Adds information about the order of tokens. • Encoder-Decoder Structure: Uses encoders to process input and decoders to generate output.

5. Working of Transformers 1. Tokenization: Breaking down input into tokens. 2. Embedding: Converting tokens into vectors. 3. Positional Encoding: Adding position information to embeddings. 4. Self-Attention: Calculating relationships between tokens. 5. Multi-Head Attention: Applying multiple attention mechanisms. 6. Feed-Forward Networks: Processing token representations. 7. Output Generation: Producing final output using softmax.

6. Problem Solving with Transformers • Step 1: Data Preprocessing - Tokenization, Stemming, Lemmatization. • Step 2: Embedding and Vectorization. • Step 3: Model Selection and Training. • Step 4: Fine-Tuning with Specific Datasets. • Step 5: Model Evaluation - Metrics like Accuracy, F1 Score, Precision, Recall. • Step 6: Interpretation of Results and Iterative Improvement.

7. Types of Transformer Models • BERT: Bidirectional Encoder Representations from Transformers. • BART: Bidirectional and Auto-Regressive Transformers. • T5: Text-To-Text Transfer Transformer. • Pegasus: Pre-training with Gap Sentence Generation. • LLaMA: Large Language Model Meta AI. • GPT: Generative Pre-trained Transformer. • Vicuna: Open-source fine-tuned version of LLaMA. • PHI-3 Vision: Vision-based Transformer model.

8. Comparison of Transformer Models • BERT: Great for understanding context; limited in generation tasks. • GPT: Excellent for text generation; lacks bidirectional context. • BART: Combines BERT and GPT benefits; suitable for text completion and generation. • T5: Versatile, handles multiple NLP tasks; may require large datasets. • Pegasus: Specialized in summarization; highly effective but task-specific. • LLaMA: Efficient and accessible large language model; strong generalization. • Vicuna: Enhanced for conversational tasks; based on LLaMA. • PHI-3 Vision: Tailored for vision tasks; effective in image recognition.