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nlp.seas.harvard.edu | ||
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harvardnlp.github.io
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| | | | | [AI summary] The provided code is a comprehensive implementation of the Transformer model, including data loading, model architecture, training, and visualization. It also includes functions for decoding and visualizing attention mechanisms across different layers of the model. The code is structured to support both training and inference, with examples provided for running the model and visualizing attention patterns. | |
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teddykoker.com
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| | | | | This post is the first in a series of articles about natural language processing (NLP), a subfield of machine learning concerning the interaction between computers and human language. This article will be focused on attention, a mechanism that forms the backbone of many state-of-the art language models, including Googles BERT (Devlin et al., 2018), and OpenAIs GPT-2 (Radford et al., 2019). | |
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jalammar.github.io
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| | | | | Discussions: Hacker News (65 points, 4 comments), Reddit r/MachineLearning (29 points, 3 comments) Translations: Arabic, Chinese (Simplified) 1, Chinese (Simplified) 2, French 1, French 2, Italian, Japanese, Korean, Persian, Russian, Spanish 1, Spanish 2, Vietnamese Watch: MIT's Deep Learning State of the Art lecture referencing this post Featured in courses at Stanford, Harvard, MIT, Princeton, CMU and others Update: This post has now become a book! Check out LLM-book.com which contains (Chapter 3) an updated and expanded version of this post speaking about the latest Transformer models and how they've evolved in the seven years since the original Transformer (like Multi-Query Attention and RoPE Positional embeddings). In the previous post, we looked at Att... | |
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www.superannotate.com
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| | | Dive into LLM fine-tuning: its importance, types, methods, and best practices for optimizing language model performance. | ||
