Building with Pre-Trained BART, BERT, and GPT-Style LLMs

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@AI-Makerspace Жыл бұрын

Colab Notebook: colab.research.google.com/drive/1NwwPKYlyOIxeRI1BBUMZ-uFIApyZxARy?usp=sharing Slide Link: www.canva.com/design/DAFy31ZLJsA/h36gP_adzG-_H0_pe_npaw/edit?DAFy31ZLJsA&

@HarpaAI Жыл бұрын

🎯 Key Takeaways for quick navigation: 00:03 📚 *Introduction to the Video and Speakers* - Greg introduces the video and speakers. - Chris and Greg discuss the complexity of a diagram. 01:03 🤝 *Event Overview* - Greg talks about the event's objectives. - Mention of asking questions through Slido. 01:27 🎤 *Introduction to Chris* - Greg introduces Chris as the CTO of AI Makerspace. - Highlights Chris's role as an instructor and content creator. 02:10 🧠 *Objectives of the Session* - Greg outlines the objectives of the session. - Mention of developing an understanding of Transformers and their applications. 03:28 🧩 *What is a Transformer?* - Greg begins explaining the concept of Transformers. - Introduction to tokenization, embeddings, and positional encoding. 04:10 📊 *Tokenization, Embeddings, and Encoding* - Further explanation of tokenization, embeddings, and positional encoding. - The importance of these concepts in Transformers. 08:37 🔄 *Embeddings and Semantic Meaning* - Discussion on word embeddings and their role in understanding semantic meaning. - Examples illustrating word embeddings. 09:47 🔍 *Pre-trained Models and Downstream Tasks* - Mention of the significance of pre-trained models like Word2Vec. - Reference to Downstream NLP tasks and their use of pre-trained models. 10:57 🗺️ *Positional Encoding* - Explanation of positional encoding and its role in maintaining word order. - The connection between tokenization, embeddings, and positional encoding. 13:38 📦 *Transformer Structure* - Overview of the Transformer's structure, including encoder and decoder blocks. - Mention of the encoder-decoder architecture for sequence-to-sequence tasks. 15:06 🧠 *Self-Attention Mechanism* - Introduction to self-attention mechanism and its role in Transformers. - Explanation of how self-attention captures context within input sequences. 16:51 🧮 *Multiple Heads in Self-Attention* - Discussion on the benefits of using multiple attention heads in self-attention. - Considerations for determining the number of attention heads. 18:02 🤖 *Encoder-Decoder Architecture* - Explanation of the encoder-decoder architecture in sequence-to-sequence models. - Emphasis on the bidirectional, auto-regressive, and decoder elements. 19:10 🎯 *Applications of Encoder-Decoder Models* - Chris discusses the practical applications of encoder-decoder models like Bart. - Mention of tasks such as translation and summarization. 26:23 📜 *Practical Example: Bill Summarization* - Transition to Chris for a practical example of using a Bart-like model for bill summarization. - Focus on how Bart can be applied in real-world scenarios. 27:07 📚 *Training a Bart Model for Summarization* - Dr. Greg introduces training a Bart model for summarization. - Data preprocessing: Tokenizing text inputs and labels. - Setting up a data iterator for sequence-to-sequence models. 31:13 🧾 *Evaluation Pipeline and Training* - Establishing an evaluation pipeline for model performance. - Using the Rouge score and sentence tokenizer from NLTK. - Initiating training with specified hyperparameters. 34:11 🚀 *Deploying and Using a Fine-Tuned Bart Model* - Deploying the trained Bart model to Hugging Face Hub. - Demonstrating summarization capabilities with examples. 38:07 🌐 *Understanding BERT for Sentiment Analysis* - Introduction to BERT and its use in sentiment analysis. - Preparing the 20 News Group dataset for fine-tuning. - Creating a TensorFlow model for sequence classification. 42:04 📈 *Fine-Tuning BERT and Model Evaluation* - Fine-tuning the BERT model for sentiment analysis. - Monitoring training progress and evaluating the model. - Pushing the trained model to Hugging Face Hub for further use. 45:42 🎶 *GPT-2 for Creative Text Generation* - Introduction to GPT-2 as a decoder-only model. - Fine-tuning GPT-2 for lyric generation using the Genius lyrics dataset. - Setting up the model and tokenizer for GPT-2. 50:13 🧠 *Introduction to Auto regression and decoder-only architecture* - Explanation of Auto regression and decoder-only architecture. - Mention of adding a padding token for Hugging Face trainer compatibility. - Description of the GPT-2 model used (12 blocks, 12 heads). 51:09 📊 *Tokenization and data preprocessing* - Tokenization of lyrics data. - Data preprocessing steps, including mapping the tokenizer and creating input IDs and attention masks. - The absence of labels in the data and the use of input sequences as labels. 52:06 🔄 *Training setup and parameters* - Setting up the training process. - Configuration details, including the number of epochs, learning rate, and weight decay. - Saving and loading the best-performing model. 53:30 ⏳ *Creating a cosine scheduler and text generation setup* - Creation of a cosine scheduler for training. - Configuration of hyperparameters for text generation during evaluation. - Initiating the training process using the Hugging Face Transformer trainer. 54:36 🎶 *Generating text sequences* - Process of generating text sequences using the fine-tuned model. - Discussion of avoiding repetition in generated lyrics. - Converting token sequences to human-readable text. 55:30 🔑 *Key takeaways on Transformer models and tasks* - Recap of the three discussed models and their fine-tuning examples. - Emphasis on understanding model capabilities and data manipulation for specific tasks. - Mention of the importance of vocabulary and attention mechanisms in Transformers. 56:12 🤖 *Closing remarks on Transformer models* - Conclusion of the presentation, highlighting the significance of Transformers in NLP. - Differentiation between BERT, BART, and GPT-style models. - Reminder that models with decoders are required for text generation tasks. 57:35 ❓ *Q&A and audience engagement* - Transition to a Q&A session with the audience. - Encouragement for questions and feedback from the viewers. - Discussion of the significance of Transformers in AI research and engineering. 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@AI-Makerspace Жыл бұрын

That's awesome!