Real-time personalization has advanced significantly in recent years, with platforms utilizing machine learning models to predict user preferences based on rich behavioral data on each individual user. Traditional approaches usually rely on embedding-based machine learning models to capture user preferences, and then reduce the final optimization task to nearest-neighbors, which can be performed extremely fast. However, these models struggle to capture complex user behaviors, which are essential for making accurate recommendations. Transformer-based models, on the other hand, are known for their practical ability to model sequential behaviors, and hence have been intensively used in personalization recently to overcome these limitations. However, optimizing recommendations under transformer-based models is challenging due to their complicated architectures. In this paper, we address this challenge by considering a specific class of transformers, showing its ability to represent complex user preferences, and developing efficient algorithms for real-time personalization. We focus on a particular set of transformers, called simple transformers, which contain a single self-attention layer. We show that simple transformers are capable of capturing complex user preferences. We then develop an algorithm that enables fast optimization of recommendation tasks based on simple transformers. Our algorithm achieves near-optimal performance in sub-linear time. Finally, we demonstrate the effectiveness of our approach through an empirical study on datasets from Spotify and Trivago. Our experiment results show that (1) simple transformers can model/predict user preferences substantially more accurately than non-transformer models and nearly as accurately as more complex transformers, and (2) our algorithm completes simple-transformer-based recommendation tasks quickly and effectively.

The rapid advancements in deep learning have enabled the development of models capable of addressing a wide range of tasks across domains such as natural language processing, computer vision, and time series forecasting (Vaswani et al., 2017; Devlin et al., 2018). However, predicting financial market behavior, especially identifying price surges in cryptocurrency markets, remains a challenging problem due to the stochastic nature of financial data and the influence of external factors (Benth et al., 2003; Cont, 2001). In recent years, Transformer-based models have demonstrated exceptional performance in time series forecasting by capturing long-range dependencies and temporal interactions(Vaswani et al., 2017; Lim and Zohren, 2021; Zhou et al., 2021). Simultaneously, the emergence of large language models (LLMs) has paved the way for transfer learning applications in financial time series data, including cryptocurrency markets (Raffel et al., 2020; Liu et al., 2019). This study introduces BreakGPT, an architecture that combines the strengths of LLMs and Transformer-based models for predicting cryptocurrency price surges. We evaluate multiple architectures, including a modified TimeLLM (Doe and Lee, 2023) and TimeGPT (Smith and Johnson, 2023), assessing their effectiveness in detecting price surges in assets like Bitcoin and Solana(Nakamoto, 2008; Zhang and McGovern, 2019). Key contributions of this study include: Development of a modified TimeLLM architecture that adapts GPT-2 for time series prediction using domain-specific prompts and embeddings (Doe and Lee, 2023; Radford et al., 2019). Implementation and comparison of various Transformer-based models that utilize attention mechanisms and convolutional layers to process financial time series data.

Foundation models exhibit significant capabilities in decision-making and logical deductions. Nonetheless, a continuing discourse persists regarding their genuine understanding of the world as opposed to mere stochastic mimicry. This paper meticulously examines a simple transformer trained for Othello, extending prior research to enhance comprehension of the emergent world model of Othello-GPT. The investigation reveals that Othello-GPT encapsulates a linear representation of opposing pieces, a factor that causally steers its decision-making process. This paper further elucidates the interplay between the linear world representation and causal decision-making, and their dependence on layer depth and model complexity. We have made the code public.

From your Kaggle profile navigate to My Account API and then click on Create New API Token. Once you have this file, run the below code. During the execution, it will prompt you to upload a JSON file so you can upload the kaggle.json Since Google Colab comes with transformers pre-installed, let's upgrade the transformers library so we will have the latest version of it and then install Simple Transformers: BBC News Articles dataset has different folders for each category -- business, entertainment, politics, sports, and tech. But for simplicity, we will take only the business category here.

Using Transformer models has never been simpler! Yes that's what Simple Transformers author Thilina Rajapakse says and I agree with him so should you. You might have seen lengthy code with hundreds of lines to implement transformers models such as BERT, RoBERTa, etc. Once you understand how to use Simple Transformers you will know how easy and simple it is to use transformer models. TheSimple Transformers library is built on top of Hugging Face Transformers library. Hugging Face Transformers provides state-of-the-art general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet, T5, etc.) for Natural Language Understanding (NLU) and Natural Language Generation (NLG) and provides more than thousand pre-trained models and covers around 100 languages.

Text classification is one of the most common problems in natural language processing. In the past few years, there have been numerous successful attempts which gave rise to many state-of-the-art language models capable of performing classification tasks with accuracy and precision. Text classification powers many real-world applications -- from simple spam filtering to voice assistants like Alexa. These applications have the capability to classify the user's input to understand the context of spoken words. In this article, we will build on the basic idea of giving the machine the power to listen to human speech and classify what the person is talking about.

The Simple Transformers library is built as a wrapper around the excellent Transformers library by Hugging Face. I am eternally grateful for the hard work done by the folks at Hugging Face to enable the public to easily access and use Transformer models. I don't know what I'd have done without you guys! I believe it's fair to say that the success of Transformer models have been nothing short of phenomenal in advancing the field of Natural Language Processing. Not only have they shown staggering leaps in performance on many NLP tasks they were designed to solve, pre-trained Transformers are also almost uncannily good at Transfer Learning.