Researchers state that they see edge training as an optimization problem, thereby discovering the optimal scheduling to achieve minimal energy consumption under a given memory budget. Currently, deep learning models are widely deployed on edge devices such as smartphones and embedded platforms for inference. Training, however, is still primarily conducted on large cloud servers equipped … Read more
Delivering NLP technology insights to you every day! From: MyEncyclopedia In this issue, we continue from the last issue on BERT Chinese short sentence similarity calculation Docker CPU image, continuing to use huggingface transformer and sentence-transformer libraries, generating BERT embeddings for English sentences, and then introducing the faiss library to establish an index and finally … Read more
Abstract: Large Language Models (LLMs) are natural language processing technologies used to describe vast amounts of text through vector representations and generative probabilities. Recently, with the emergence of representative products like ChatGPT, which has garnered widespread attention in the education sector due to its excellent capabilities in generation, comprehension, logical reasoning, and dialogue, research on … Read more
This article is reproduced from:NLP Intelligence Bureau Since Google introduced the pre-trained language model BERT, various applications of language models have emerged. However, most models were proposed in English contexts, and their performance often declines to varying degrees when transferred to Chinese contexts. Previously, my friends and I participated in the CCKS machine reading comprehension … Read more
Follow our WeChat public account “ML_NLP“ Set as “Starred“, heavy content delivered to you instantly! Reprinted from｜PaperWeekly ©PaperWeekly Original · Author｜ Su Jianlin Unit｜Zhuiyi Technology Research Direction｜NLP, Neural Networks ▲ The Years of “Chicken and Rabbit in the Same Cage” “Profit and Loss Problems”, “Age Problems”, “Tree Planting Problems”, “Cows Eating Grass Problems”, “Profit Problems”… … Read more
This article is sourced from Machine Heart. Researchers indicate that they viewedge training as an optimization problem, thus discovering the optimal scheduling to achieve minimal energy consumption under a given memory budget. Currently, deep learning models are widely deployed for inference on edge devices such as smartphones and embedded platforms. However, training predominantly occurs on … Read more
Follow our public account “ML_NLP“ Set as “Starred“, heavy content delivered first! Author:Qiu Zhenyu (Algorithm Engineer, Huatai Securities Co., Ltd.) Zhihu Column:My AI Journey Recently, I came across a paper titled “Revisiting Few-sample BERT Fine-tuning”. The paper has just been released on arXiv, and although it hasn’t attracted much attention yet, I found it very … Read more
Follow us on WeChat “ML_NLP” Set as “Starred”, heavy content delivered first hand! Written by / Radu Soricut and Zhenzhong Lan, Researchers, Google Research Since the advent of BERT a year ago, natural language research has adopted a new paradigm: leveraging a large amount of existing text to pre-train model parameters in a self-supervised manner … Read more
Click the “MLNLP” above and select “Star” to follow the public account Heavyweight content delivered to you first Author:Old Song’s Tea Book Club Zhihu Column:NLP and Deep Learning Research Direction:Natural Language Processing Introduction A few days ago, during an interview, an interviewer directly asked me to analyze the source code of BERT. Emm, that was … Read more
MLNLP community is a well-known machine learning and natural language processing community in China and abroad, covering NLP master’s and doctoral students, university teachers, and corporate researchers. The Vision of the Community is to promote communication and progress between the academic and industrial communities of natural language processing and machine learning, especially for beginners. Reprinted … Read more